Miracle-dz/UniFAR-FTUs huggingface dataset:
The Super Default Rules in Ontologies

In representing ontologies there are two kinds of default statements: one is the default assertions which say that some concept has some property defaultly, another is the default inheritance of default assertions. The latter actually is a default rule of default rules (called super default rules), that is, a default rule in which the formulas occur in the default are default rules. In this paper we shall give a formal description of such super default rules, a super default theories and the definitions of the extensions of the super default theories.
Rendering Order Optimization for SVGStat Improvement

Vector representation of digital images offers a number of advantages over the more common raster representation, such as scalability and resolution independence. Many efforts have been made to deploy scalable raster standards for photographic imagery addressed to portable applications. However, they lack the flessibility and simplicity of vector representation. Vector graphics is a new and little explored alternative to the more common representation. In this paper we present our raster to vector technique, called SVGStat, improved with a new boundaries simplification algorithm.
Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.
Parallel Ips on Planar Triangulations

In this paper, we analyze ips over triangulations. Given a convex quadrilateral divided into two triangles in the triangulation, a ip consists in removing the triangle and replacing them by the two whose common side is the opposite diagonal. A parallel ip is a series of ips on disjoint quadrilaterals performed at the same time. We analyze the number of parallel ips that are necessary to transform any triangulation into another, and show that it can be performed in O(n) steps; this bound is tight. We also study a class of heuristics that give simple ways to compute a parallel ip, and show various results on them.
RolX: Role Extraction and Mining in Large Networks

Given a graph, how can we automatically discover roles for nodes? Roles could be, eg. , ’bridges’, or ’peripherynodes’, etc. Roles are compact summaries of a node’s behavior that generalize across networks. They enable numerous novel and useful network mining tasks, such as sense-making, searching for similar nodes, and node classification. We propose RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting roles from general network data. We demonstrate the effectiveness of RolX on several network mining tasks, from exploratory data analysis to network transfer learning. Moreover, we compare network role discovery with network community discovery. We highlight fundamental differences between the two (e.g., roles generalize across disconnected networks, communities do not). Keywords-Graph mining, role discovery
Issues in Informing Science and Information Technology Socio-technical Theory and Knowledge Construction: towards New Pedagogical Paradigms?

After a short introduction on socio-technical theory, the experiences the author made in the planning and carrying out of web sites and information systems for research and teaching are reported. The results from those experiences are analyzed with a special attention to knowledge development at three different levels: individuals, communities, and organizations (society), and the author's hypotheses for the explanation of the observed phenomena are discussed. The most relevant results on the effects of ICT on knowledge can be summarized as follows: a) they can improve knowledge construction and induce meaningful learning in students, while contributing in building communities of learners (CoLs) and communities of practice (CoPs), b) they can induce new models for knowledge construction and evolution in special communities and learning organizations, and c) they can contribute in hypothesizing new teaching paradigms (or new pedagogical paradigms more generally). At last a revision of the well known scheme synthesizing the socio-technical approach to organization description (as regards knowledge construction and evolution) is reported and it is applied to the special case of a master course for teachers. The explanation of the effects that the new scheme has on the hypothesized three levels and on the structure of MIS (Management Information System) follows.
Opioids Contribute to Fracture Risk: A Meta-Analysis of 8 Cohort Studies

Objective To evaluate the association between chronic opioid use for non-cancer pain and fracture risk by conducting a meta-analysis of cohort studies. Methods Cohort studies were identified by searching PubMed and EMBASE from their inception to July 2014. A fracture was considered an endpoint. The information was extracted by two authors independently. When the heterogeneity was significant, a random-effects model was used to calculate the overall pooled risk estimates. Results Eight cohort studies were included in the final meta-analysis. On the basis of the Newcastle-Ottawa Scale (NOS), six studies were considered to be of high quality. The overall combined relative risk for the use of opioids and fractures was 1.88 (95% confidence interval [CI] 1.51-2.34). A subgroup analysis revealed the sources of heterogeneity. The sensitivity analysis indicated stable results, and no publication bias was observed. Conclusions This meta-analysis of cohort studies demonstrates that opioids significantly increase the risk of fractures.
Improvement of a face perception deficit via subsensory galvanic vestibular stimulation

The remediative effect of galvanic vestibular stimulation (GVS) was investigated in a patient who, following right hemisphere damage, is profoundly unable to recognize faces. We administered a two-alternative forced choice match-to-sample task in which the patient had to choose which of two faces matched a sample face presented directly above, while bipolar, transcutaneous current was applied to the left and right vestibular nerves at a level below the patient's sensory threshold. Performance improved beyond the chance-level observed prestimulation, and relied on reversing the electrode polarity across two separate blocks of trials, such that each mastoid received positive current for one block and then negative charge for the next. Although our study involved only a single case, the data provide preliminary evidence that a deficit in perceptual face matching can be reduced by GVS. This raises the intriguing possibility that other unilateral visual disorders may also respond in such a manner. (JINS, 2005, 11, 925–929.)
A New Technique for Multi Resolution Characterization of Epileptic Spikes in EEG

A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). A multi-resolution approach and a non-linear energy operator are exploited. The signal on each EEG channel is decomposed into three sub bands using a non-decimated wavelet transform (WT). The WT is a powerful tool for multi-resolution analysis of non-stationary signal as well as for signal compression, recognition and restoration. Each sub band is analyzed by using a non-linear energy operator, in order to detect spikes. A decision rule detects the presence of spikes in the EEG, relying upon the energy of the three sub-bands. The effectiveness of the proposed technique was confirmed by analyzing both test signals and EEG layouts. Keywords—EEG, Spike, SNEO, Wavelet Transform
GSM-based positioning for public transportation commuters

Crowd-sourcing of information about various aspects of a road is an important mechanism in Intelligent Transportation Systems (ITS). In this work, we consider crowd-sourcing from public transportation commuters, information about the road, traffic, and the specific public transportation unit. Any crowd-sourced information has to be tagged with the information provider's location. Since GPS-based location determination is energy-expensive, we focus on GSM signal based location determination. A specific dimension we explore, not considered in prior work, is the use of GSM signal information from multiple commuters' phones. This consideration is triggered by the observation that a large set of commuters have (almost) the same physical location for large durations of time: when they share the same bus. We present an analysis of data collected from two different Indian cities: Mumbai and Chandigarh. We find that such combination of information can lower the median location error by a factor of 2-10.
Effective Test Generation for Combinatorial Decision Coverage

Complex software systems present significant challenges to existing software testing techniques. Simply applying exhaustive testing will lead to the execution of a prohibitively large number of test cases. Furthermore, many testing techniques today provide neither promising coverage achievement nor reliable fault detection strength. In this paper, we propose a technique, which represents an innovative synthesis of combinatorial testing and symbolic execution, to generate test cases based on a novel coverage criterion, namely combinatorial decision coverage (CDC). Strength t (or t-way) CDC requires each t-tuple of decision outcomes to be executed by at least one test case. Given a program, our CDC-based technique first uses a revised version of a symbolic executor, S2E, to collect all program decisions with symbolic variables as well as their corresponding constraints and then applies a combinatorial test generation tool, ACTS, to generate t-way combinations for the outcomes of these decisions. A test case can be generated with respect to each combination that represents a single path-condition of the program. Case studies were conducted on three versions of photo editing applications. Our results indicate that a test set generated using the proposed technique has higher statement, decision, and all use coverage as well as better fault detection strength than a test set of the same size generated by random testing and genetic algorithm-based test generation.
GPU-Accelerated FDTD simulation of human tissue using C++ AMP

We present results of 3D time-domain simulations of human tissue performed on high-performance graphics processing unit (GPU) hardware using a new simulation code, C++ Accelerated Massive Parallelism (AMP). A GPU implementation of the finite-difference time-domain (FDTD) method is shown to give an at least 30x reduction in execution time compared to an equivalent central processor unit (CPU) code.
MC-LMAC: A Multi-Channel MAC Protocol for Wireless Sensor Networks

In traditional wireless sensor network (WSN) applications, energy efficiency is considered to be the most important concern whereas utilizing the use of bandwidth and maximizing the throughput are of secondary importance. However, recent applications, such as structural health monitoring, require high amounts of data to be collected at a faster rate. We present a multi-channel MAC protocol, MC-LMAC, designed with the objective of maximizing the throughput of WSN by coordinating transmissions over multiple channels. MC-LMAC takes the advantage of interference and contention free parallel transmissions on different channels. It is based on scheduled access which eases the coordination of nodes dynamically switching their interfaces between channels and makes the protocol to operate effectively free of collisions during peak traffic. Time is organized into timeslots and each node shall be assigned control over a timeslot to transmit on a particular channel. We analyze the performance of MC-LMAC with extensive simulations. MC-LMAC exhibits significant bandwidth utilization and high throughput while ensuring an energy-efficient operation. Moreover, MC-LMAC outperforms the contention-based multi-channel MMSN protocol, cluster based channel assignment and single-channel CSMA in terms of data delivery ratio and throughput for high data rate, moderate-size networks of 100 nodes.
Framework of intrinsic and extrinsic motivators of knowledge sharing: A case of training institutes of an oil and gas company in Malaysia

Knowledge sharing is vital for the success of Knowledge Management (KM) initiatives. But it is inconceivable to flourish knowledge sharing without individual's motivation to share his knowledge. Individuals are motivated through intrinsic as well as extrinsic motivators. Due to this reason researchers have underlined the importance of extrinsic rewards and Organization Citizenship Behavior (OCB) to influence individual's decision to share his knowledge. Extrinsic rewards dwell in extrinsic motivation whereas OCB represents intrinsic motivation. The relationships of these variables with knowledge sharing have been tested in past research works, however there are a number of limitations which need to be addressed. Using the case of training institutes of an oil and Gas Company in Malaysia, the aim of the study is to propose a framework of intrinsic and extrinsic motivators of knowledge sharing. The proposed framework will overcome the limitations in the literature by revisiting the impact of extrinsic rewards and OCB on knowledge sharing. The proposed framework has incorporated intrinsic (OCB) and extrinsic motivators (extrinsic rewards) in the Theory of Reasoned Action (TRA). Five major and in total 15 hypotheses have been tested. The data was gathered from the knowledge workers, working as trainers and facilitators, at three training institutes of an oil and gas company. Questionnaire method was used to gather data. Later the data was tested using regression analysis through SPSS. The results have shown that OCB is a very strong predictor of knowledge sharing behavior, whereas extrinsic reward's affect on individual's intention to share his knowledge is moderate. The study will help us to understand the role of individual's intrinsic (i.e. OCBs) and extrinsic motivators (i.e. extrinsic rewards) to affect his decision to share his knowledge. The study will also help the theorists and practitioners to develop strategies to foster knowledge sharing in their organizations.
Improved Adaptive Fingerprint Binarization

In this paper improvements to a previous work are presented. Removing the redundant artifacts in the fingerprint mask is introduced enhancing the final result. The proposed method is entirely adaptive process adjusting to each fingerprint without any further supervision of the user. Hence, the algorithm is insensitive to the characteristics of the fingerprint sensor and the various physical appearances of the fingerprints. Further, a detailed description of fingerprint mask generation not fully described in the previous work is presented. The improved experimental results are presented.
Neural network prediction in a system for optimizing simulations

Neural networks have been widely used for both prediction and classification. Back-propagation is commonly used for training neural networks, although the limitations associated with this technique are well documented. Global search techniques such as simulated annealing, genetic algorithms and tabu search have also been used for this purpose. The developers of these training methods, however, have focused on accuracy rather than training speed in order to assess the merit of new proposals. While speed is not important in settings where training can be done off-line, the situation changes when the neural network must be trained and used on-line. This is the situation when a neural network is used in the context of optimizing a simulation. In this paper, we describe a training procedure capable of achieving a sufficient accuracy level within a limited training time. The procedure is first compared with results from the literature. We then use data from the simulation of a jobshop to compare the performance of the proposed method with several training variants from a commercial package.
Augmented Hippocampal Ripple Oscillations in Mice with Reduced Fast Excitation onto Parvalbumin-Positive Cells

Generation of fast network oscillations in the hippocampus relies on interneurons, but the underlying specific synaptic mechanisms are not established. The excitatory recruitment of fast-spiking interneurons during hippocampal sharp waves has been suggested to be critical for the generation of 140–200 Hz (“ripple”) oscillations in the CA1 area. To directly test this, we used genetically modified mice (PV–ΔGluR-A) with reduced AMPA receptor-mediated excitation onto parvalbumin (PV)-positive interneurons and studied hippocampal oscillations in freely moving animals. In PV–ΔGluR-A mice, ripple-amplitude and associated rhythmic modulation of pyramidal cells and fast-spiking interneurons were increased. These changes were not accompanied by concurrent alterations of firing rates. Neither theta nor gamma oscillations displayed marked alterations in the mutant. These results provide evidence that fast excitation from pyramidal cells to PV-positive interneurons differentially influences ripple and gamma oscillations in vivo.
Identification and Characterization of the Arabidopsis Orthologs of Nuclear Transport Factor 2, the Nuclear Import Factor of Ran1

Ran is a multifunctional small GTPase that is involved in nucleocytoplasmic transport, mitotic spindle assembly, and nuclear envelope formation. Nuclear import of Ran relies on a small RanGDP-binding protein, Nuclear Transport Factor 2 (NTF2). Three proteins are expressed in Arabidopsis (Arabidopsis thaliana) that show significant sequence similarity to human and yeast (Saccharomyces cerevisiae) NTF2. Here, we demonstrate that two of them, AtNTF2a and AtNTF2b, can functionally replace the essential NTF2 gene in yeast. Consistent with this finding, both AtNTF2a and AtNTF2b interact with yeast and Arabidopsis Ran. The third NTF2-related protein, AtNTL, does not functionally replace NTF2 in yeast. Similar to yeast NTF2-green fluorescent protein (GFP), AtNTF2a-GFP and AtNTF2b-GFP accumulate at the nuclear rim. The AtNTF2a E38K and E91K mutants, which fail to bind Ran, are not functional in yeast, indicating conservation of the requirement for these key amino acids in plants and yeast. AtNTF2a overexpression, but not AtNTF2aE38K overexpression, blocks nuclear import of a plant transcription factor in Nicotiana benthamiana leaves, indicating that excess AtNTF2a disrupts nuclear import in a Ran-binding-dependent manner. On the basis of these results, we propose that AtNTF2a and AtNTF2b function in Ran import in Arabidopsis and that nuclear import of Ran is functionally conserved in plants.
On the importance of harmonic phase modification for improved speech signal reconstruction

Conventional single-channel speech enhancement is mainly focused on modifying the noisy short-time Fourier transform amplitude spectrum while for signal reconstruction the noisy phase is used. Recent advances demonstrate the positive improvements in speech enhancement when the noisy phase is replaced with an estimated clean phase for signal reconstruction. In this paper, we study the impact of the linear phase and unwrapped phase components provided by harmonic phase decomposition on the speech quality at signal reconstruction. We present objective and subjective results comparing the proposed harmonic phase modification with other phase estimation methods. Our results show that enhancement of decomposed phase parts suffices for improved reconstruction in speech enhancement.
A new kind of high durable traffic weighbridge based on FBG sensors

Durability is the key problem of traditional traffic weighbridge based on electrical gauges. In this paper, a new kind of high durable traffic weighbridge based on FBG (Fiber Bragg Grating) sensors has been studied and developed. The principle of the smart FBG-weighbridge is based on that the traffic weight can be gotten from the deformation of the reinforced concrete beam with embedded FRP (Fiber Reinforced Polymer) - packaged FBG strain sensors. The FBG-based weighbridge is designed to be a reinforced concrete board supported by composite beams, and the truck load is shared by the composite beams. A 30-ton full scale FBG-based weighbridge has been set up, and the results from the tests and calibration analysis show that this kind of weighbridge features high durability, simplicity, convenience, low cost, etc. This new kind of FBG-based weighbridge shows good prospect in future to replace the traditional traffic weighbridge for long-term monitoring of traffic load.
Automatic Face Animation with Linear Model

This report proposed an automatic face animation method. First, 28 features facial features are automatically extracted from the videorecorded face. Then, using a linear model, we can decompose the variation of the 28 facial features into the shape variation and the expression variation. Finally, the expression variation is used to control the animation of the target face. All the tracking and animation procedure are fully automatic.