Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure discovery in conditional probability distributions via an entropic prior and parameter extinction

We introduce an entropic prior for multinomial parameter estimation problems and solve for its maximum a posteriori (MAP) estimator. The prior is a bias for maximally structured and minimally ambiguous models. In conditional probability models with hidden state, iterative MAP estimation drives weakly supported parameters toward extinction, effectively turning them off. Thus structure discovery is folded into parameter estimation. We then establish criteria for simplifying a probabilistic model’s graphical structure by trimming parameters and states, with a guarantee that any such deletion will increase the posterior probability of the model. Trimming accelerates learning by sparsifying the model. All operations monotonically and maximally increase the posterior probability, yielding structure-learning algorithms only slightly slower than parameter estimation via expectation-maximization (EM), and orders of magnitude faster than search-based structure induction. When applied to hidden Markov model (HMM) training, the resulting models show superior generalization to held-out test data. In many cases the resulting models are so sparse and concise that they are interpretable, with hidden states that strongly correlate with meaningful categories.
Survey on Various Methods and Techniques for Searching Dimension in Incomplete Database

Now a days, dimension incomplete problem is fundamental research problem in multidimensional database. Information regarding the missing dimension posses great computational challenges. In multidimensional database similarity query problem occur with numerous application in database area such as, data mining, information retrieval etc. Due to various practical issues like remote data accessing represent the dimension incomplete problem. This paper presents a framework for searching the dimension in incomplete database. The existing work proposed that, on certain dimension data values are unknown due to similarity query and query incomplete data. In this work we proposed to investigate the problem of information regarding incomplete dimension. In this paper we studied various methods and techniques such as, missing data recovery method, clustering algorithm as well as indexing method. This paper presents an approach for Indexing scheme like BRTree, MOSAIC Tree , R* Tree which works according to databases. Generating AFD based techniques define the approximation based approach. MDT’s that is missing dimension technique proposed four techniques . Each method specifies their identical works according to field. So that, in proposed work we are using various methods and techniques. Probabilistic method is used to find no of possible sub queries using parameter. The probabilistic technique and scheme can be applied to whole as well as subsequence query.
Numerical Analysis of Thin-wire Dipole Antenna Measurement System for Pulse Electric Field

Multi-grids impedance model for dealing with lumped circuit elements is put forward in this paper. Influencing magnitude of import impedance of optical fiber transmission system on electric field component in all frequency point when measuring pulse electric field and influence of optical transmitter's shielding shell on transmission coefficient of measurement system for pulse electric field are discussed.
Assessment Strategies, Self-Regulated Learning Skills, and Perceptions of Assessment in Online Learning.

This study investigated the effects of assessment strategies and self-regulated learning skills on students’ learning and perceptions of assessment for learning. The results revealed no statistically significant difference in the immediate skill-based and cognitive learning outcomes, but peer assessment teams scored significantly higher than the other teams in the subsequent collaborative writing process. Significant interaction effects were found in the participants’ perceptions of assessment for learning. The findings support the emphasis on assessment processes and long-term learning benefits, and provide further evidence for the benefits of using assessment strategies and the value of learner assessment experience in online environments.
TR 03-042 Training Support Vector Machine using Adaptive Clustering

Training supportvectormachinesinvolvesa hugeoptimizationproblemandmany speciallydesignedalgorithms have beenproposed.In this paper , we proposedan algorithmcalledClusterSVMthat acceleratesthe training process by exploiting thedistributionalpropertiesof thetrainingdata,that is, thenaturalclusteringof thetrainingdataandthe overall layout of theseclustersrelative to the decisionboundaryof supportvectormachines.The proposedalgorithm first partitionsthe training datainto several pair-wise disjoint clusters.Then,the representati vesof theseclustersare usedto train an initial supportvectormachine,basedon which we canapproximatelyidentify thesupportvectorsand non-supportvectors.After replacingtheclustercontainingonly non-supportvectorswith its representati ve, thenumber of trainingdatacanbesignificantlyreduced,therebyspeedingup the trainingprocess.TheproposedClusterSVMhas beentestedagainstthe populartraining algorithmSMO on both the artificial dataandthe real data,anda significant speedupwasobserved. The complexity of ClusterSVMscaleswith the squareof the numberof supportvectorsand, after a further improvement,it is expectedthat it will scalewith squareof the numberof non-boundarysupport vectors.
Driver Assistance System Using Eye Gaze Estimation

A driver assistance system (DAS) constantly monitors the behavior of the vehicle, behavior of the driver and road scene events and provides the relevant information to the driver in case of any threat events. This paper presents the automated detection and recognition of road signs, combined with the monitoring of the drivers response. The complete system reads traffic signs, compares the drivers gaze, and provides immediate feedback if it appears the sign has been missed by the driver. The driver video captured is processed to detect the eye pair and determine the eye gaze using DWT and Neural Networks. The road scene video captured is processed to detect the presence of speed sign board and speed limit on it. Based on these detections driver is alerted. The system is tested with different scenarios and the performance is estimated to 92.308%.
Comparing Cournot and Bertrand Equilibria Rivisited

Comparing Cournot and Bertrand Equilibria Revisited by Jim Y. Jin This paper compares Cournot and Bertrand equilibria with mixed products, linear demand and cost functions. It is found that a firm's price (output) need not be higher (lower) in Cournot equilibrium. However, given any number of firms and a mixture of complement and substitute products, every firm's price margin/output ratio is always higher in Cournot equilibrium, and the weighted squared outputs (price margins) are higher (lower) in Bertrand equilibrium. When price (quantity) competition is a supermodular game, consumer surplus (social welfare) is higher in price competition. Nevertheless, price competition results in more market concentration measured by Herfindahl index.
Random Spatial Networks: Small Worlds without Clustering, Traveling Waves, and Hop-and-Spread Disease Dynamics

Random network models play a prominent role in modeling, analyzing and understanding complex phenomena on real-life networks. However, a key property of networks is often neglected: many real-world networks exhibit spatial structure, the tendency of a node to select neighbors with a probability depending on physical distance. Here, we introduce a class of random spatial networks (RSNs) which generalizes many existing random network models but adds spatial structure. In these networks, nodes are placed randomly in space and joined in edges with a probability depending on their distance and their individual expected degrees, in a manner that crucially remains analytically tractable. We use this network class to propose a new generalization of small-world networks, where the average shortest path lengths in the graph are small, as in classical Watts-Strogatz small-world networks, but with close spatial proximity of nodes that are neighbors in the network playing the role of large clustering. Small-world effects are demonstrated on these spatial small-world networks without clustering. We are able to derive partial integro-differential equations governing susceptible-infectious-recovered disease spreading through an RSN, and we demonstrate the existence of traveling wave solutions. If the distance kernel governing edge placement decays slower than exponential, the population-scale dynamics are dominated by long-range hops followed by local spread of traveling waves. This provides a theoretical modeling framework for recent observations of how epidemics like Ebola evolve in modern connected societies, with long-range connections seeding new focal points from which the epidemic locally spreads in a wavelike manner.
Let's Take Bargaining Models Seriously: The Decline in Union Power in Germany, 1992-2009

Building on the right-to-manage model of collective bargaining, this paper tries to infer union power from the observed results in wage setting. It derives a time-varying indicator of union strength and confronts it with annual data for Germany. The results show that union power was relatively stable in the 1990s but fell substantially (by almost one-third) from 1999 to 2007. Two-thirds of this fall in union power follow from the reduction in the labour share relative to the capital share whereas changes in the gap between the net wage and the income when unemployed account for the remaining third.
A topological splitting theorem for weighted Alexandrov spaces

Under an infinitesimal version of the Bishop-Gromov relative volume comparison condition for a measure on an Alexandrov space, we prove a topological splitting theorem of Cheeger-Gromoll type. As a corollary, we prove an isometric splitting theorem for Riemannian manifolds with singularities of nonnegative (Bakry-Emery) Ricci curvature.
An Unified Approach for Multimedia Document Representation and Document Similarity

In the recent years, the evolution in multimedia technology has accelerated the growth of multimedia data. Even though the multimedia data are heterogeneous, the rich information they carry has made a high demand for sophisticated multimedia knowledge discovery systems. To mine the knowledge from multimedia document, each type of multimedia data has to undergo unique processing and knowledge discovery processes because of its uniqueness. However, this procedure of processing and analyzing each type of multimedia data separately may make the system more complicated in case of large databases. Alternatively, it will be more advantageous if heterogeneous objects are represented in a common domain, such that the similar processing and knowledge discovery methods can be used. Motivated by this concept, a method known as domain converter is proposed to represent the heterogeneous multimedia objects in a spatial domain. Also based on information theory, a similarity measure is proposed to find the similarity between the documents. To evaluate the proposed framework, the experiments have been conducted for the retrieval of multimedia documents. The proposed domain converter represents the multimedia document in a homogeneous domain, and with the proposed similarity measure, better document retrieval rate has been achieved.
A social cognitive framework for examining the work of Catholic religious education teachers in Australian high schools

ABSTRACT Religious education in Australian Catholic High Schools is the raison d’etre for the Catholic education system and religious education teachers are critical to this mission. This paper offers a framework for investigating the self-efficacy of religious education teachers. We first describe the nature and context of religious education in Australian Catholic schools. We argue that the teaching of religious education presents a set of unique challenges to teachers, compared to teaching other disciplines in the curriculum. Next we review the extant literature on Social Cognitive Theory, and self-efficacy as it may apply in this context. Then we look at likely constructs that may impact upon teacher self-efficacy for teaching religious education: collective efficacy, teachers’ implicit theories about student ability and student faith, and the intrinsic spirituality of religious education teachers. We present a theoretical framework for examining the self-efficacy of religious education teachers, and we argue for research in this area that has not been studied hitherto, making this a unique contribution to the field of the psychology of religion, and to practitioners.
Matrix approach to simulation and bisimulation analysis of finite automata

In this paper, a matrix-based method to investigate the simulation and bisimulation relations of (two) finite automata is proposed. To solve the problem, composition operations of automata are studied first, and then the algorithms and criteria are provided to check their simulation and bisimulation relations. An illustrative example is also provided.
Metamorphic Runtime Checking of Applications without Test Oracles

Challenges arise in testing applications that do not have test oracles, i.e., for which it is impossible or impractical to know what the correct output should be for general input. Metamorphic testing, introduced by Chen et al., has been shown to be a simple yet effective technique in testing these types of applications: test inputs are transformed in such a way that it is possible to predict the expected change to the output, and if the output resulting from this transformation is not as expected, then a fault must exist. Here, we improve upon previous work by presenting a new technique called Metamorphic Runtime Checking, which automatically conducts metamorphic testing of both the entire application and individual functions during a program’s execution. This new approach improves the scope, scale, and sensitivity of metamorphic testing by allowing for the identification of more properties and execution of more tests, and increasing the likelihood of detecting faults not be found by application-level properties alone. We also discuss a technique for automatically discovering functions’ metamorphic properties, and present the results of new studies that demonstrate that Metamorphic Runtime Checking advances the state of the art in testing applications without oracles.
Non-homogeneous updates for the iterative coordinate descent algorithm

Statistical reconstruction methods show great promise for improving resolution, and reducing noise and artifacts in helical X-ray CT. In fact, statistical reconstruction seems to be particularly valuable in maintaining reconstructed image quality when the dosage is low and the noise is therefore high. However, high computational cost and long reconstruction times remain as a barrier to the use of statistical reconstruction in practical applications. Among the various iterative methods that have been studied for statistical reconstruction, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a novel method for further speeding the convergence of the ICD algorithm, and therefore reducing the overall reconstruction time for statistical reconstruction. The method, which we call nonhomogeneous iterative coordinate descent (NH-ICD) uses spatially non-homogeneous updates to speed convergence by focusing computation where it is most needed. Experimental results with real data indicate that the method speeds reconstruction by roughly a factor of two for typical 3D multi-slice geometries.
A Lightweight Sensor Network Management System Design

In this paper, we propose a lightweight and transparent management framework for TinyOS sensor networks, called L-SNMS, which minimizes the overhead of management functions, including memory usage overhead, network traffic overhead, and integration overhead. We accomplish this by making L-SNMS virtually transparent to other applications hence requiring minimal integration. The proposed L-SNMS framework has been successfully tested on various sensor node platforms, including TelosB, MICAz and IMote2.
Energy-Efficient Computation Offloading for Secure UAV-Edge-Computing Systems

Characterized by their ease of deployment and bird's-eye view, unmanned aerial vehicles (UAVs) may be widely deployed both in surveillance and traffic management. However, the moderate computational capability and the short battery life restrict the local data processing at the UAV side. Fortunately, this impediment may be mitigated by employing the mobile-edge computing (MEC) paradigm for offloading demanding computational tasks from the UAV through a wireless transmission link. However, the offloaded information may become compromised by eavesdroppers. To address this issue, we conceive an energy-efficient computation offloading technique for UAV-MEC systems, with an emphasis on physical-layer security. We formulate a number of energy-efficiency problems for secure UAV-MEC systems, which are then transformed to convex problems. Finally, their optimal solutions are found for both active and passive eavesdroppers. Furthermore, the conditions of zero, partial, and full offloading are analyzed from a physical perspective. The numerical results highlight the specific conditions of activating the three abovementioned offloading options and quantify the performance of our proposed offloading strategy in various scenarios.
Constructing Dictionaries for Named Entity Recognition on Specific Domains from the Web

This paper describes an automatic dictionary construction method for Named Entity Recognition (NER) on specific domains such as restaurant guides. NER is the first step toward Information Extraction (IE), and we believe that such a dictionary construction method for NER is crucial for developing IE systems for a wide range of domains in the World Wide Web (WWW). One serious problem in NER on specific domains is that the performance of NER heavily depends on the amount of the training corpus, which requires much human labor to develop. We attempt to improve the performance of NER by using dictionaries automatically constructed from HTML documents instead of by preparing a large annotated corpus. Our dictionary construction method exploits the cooccurrence strength of two expressions in HTML itemizations calculated from average mutual information. Experimental results show that the constructed dictionaries improved the performance of the NER on a restaurant guide domain. Our method increased the F1-measure by 2.3 without any additional manual labor.
The Key Technologies of Heterogeneous Data Integration System Based on Ontology

The explosion of the internet in recent years has caused the need of the association between different industries and branches to achieve communication and information sharing. However various heterogeneities exist between different data sources, especially semantic heterogeneities because the same data was modeled by different users in different ways. This paper mainly imported the concept of ontology to realize the integration of heterogeneous data. Utilization of ontology did well in solving the problem of semantic heterogeneity of data integration. This paper introduced the concept of ontology and the process of ontology integration, and emphasis studied on the main body data integration in mapping and the Query technology.
Prioritized multi-class adaptive framework for multimedia wireless networks

The next generation of wireless cellular networks (WCNs) is expected to support real-time multimedia applications with different classes of traffic and diverse bandwidth requirements. Bandwidth is a scarce resource in wireless networking that needs to be carefully allocated amidst competing connections with different quality of service (QoS) requirements. In this paper, we propose an adaptive framework for supporting multiple classes of multimedia services with different QoS requirements in WCNs. The framework combines the following components: (i) a threshold-based bandwidth allocation policy that gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls by assigning a threshold to each class, (ii) an efficient threshold-type call admission control (CAC) algorithm, and (iii) a bandwidth adaptation algorithm (BAA) that dynamically adjusts the bandwidth of an ongoing multimedia call to minimize the number of calls receiving lower bandwidth than the requested. Numerical results show that the performance of our adaptive multimedia framework outperforms that of existing non-adaptive schemes in terms of the handoff call dropping probability and effective utilization.
N1 enhancement in synesthesia during visual and audio–visual perception in semantic cross-modal conflict situations: an ERP study

Synesthesia entails a special kind of sensory perception, where stimulation in one sensory modality leads to an internally generated perceptual experience of another, not stimulated sensory modality. This phenomenon can be viewed as an abnormal multisensory integration process as here the synesthetic percept is aberrantly fused with the stimulated modality. Indeed, recent synesthesia research has focused on multimodal processing even outside of the specific synesthesia-inducing context and has revealed changed multimodal integration, thus suggesting perceptual alterations at a global level. Here, we focused on audio–visual processing in synesthesia using a semantic classification task in combination with visually or auditory–visually presented animated and in animated objects in an audio–visual congruent and incongruent manner. Fourteen subjects with auditory-visual and/or grapheme-color synesthesia and 14 control subjects participated in the experiment. During presentation of the stimuli, event-related potentials were recorded from 32 electrodes. The analysis of reaction times and error rates revealed no group differences with best performance for audio-visually congruent stimulation indicating the well-known multimodal facilitation effect. We found enhanced amplitude of the N1 component over occipital electrode sites for synesthetes compared to controls. The differences occurred irrespective of the experimental condition and therefore suggest a global influence on early sensory processing in synesthetes.
An archaeal CRISPR type III-B system exhibiting distinctive RNA targeting features and mediating dual RNA and DNA interference

CRISPR-Cas systems provide a small RNA-based mechanism to defend against invasive genetic elements in archaea and bacteria. To investigate the in vivo mechanism of RNA interference by two type III-B systems (Cmr-α and Cmr-β) in Sulfolobus islandicus, a genetic assay was developed using plasmids carrying an artificial mini-CRISPR (AC) locus with a single spacer. After pAC plasmids were introduced into different strains, Northern analyses confirmed that mature crRNAs were produced from the plasmid-borne CRISPR loci, which then guided gene silencing to target gene expression. Spacer mutagenesis identified a trinucleotide sequence in the 3′-region of crRNA that was crucial for RNA interference. Studying mutants lacking Cmr-α or Cmr-β system showed that each Cmr complex exhibited RNA interference. Strikingly, these analyses further revealed that the two Cmr systems displayed distinctive interference features. Whereas Cmr-β complexes targeted transcripts and could be recycled in RNA cleavage, Cmr-α complexes probably targeted nascent RNA transcripts and remained associated with the substrate. Moreover, Cmr-β exhibited much stronger RNA cleavage activity than Cmr-α. Since we previously showed that S. islandicus Cmr-α mediated transcription-dependent DNA interference, the Cmr-α constitutes the first CRISPR system exhibiting dual targeting of RNA and DNA.