Miracle-dz/UniFAR-FTUs huggingface dataset:
Spell checker for Myanmar language

Natural Language Processing (NLP) is one of the most important research areas carried out in the world of Human language. For every language, spell checker is an essential component of many of the common Desktop applications, Machine Translation system and Office Automation system. In Myanmar, Myanmar Language is used as an official language. Myanmar Pronunciation and orthography has differences because spelling is often not an accurate reflection of pronunciation. In this paper, we developed Myanmar Spell Checker which can handle Typographic Errors (Non-word Errors), Phonetic Errors and Sequence Errors of Myanmar words. If misspelled word contains in the input sentence, this system can provide suggestion for misspelled Myanmar words. We apply Myanmar text Corpus to check Myanmar words. And then we used String Cosine Similarity to generate suggestions list for mistyped Myanmar words. The system can improve the quality of suggestion for misspelled Myanmar words and users' efficiency when the users cannot figure out the correct spelling by themselves.
Multivariate Fuzzy Analysis for Mobile Ad hoc Network Threat Detection

Detection of malicious, compromised and selfish node attacks is urgently needed to protect Mobile Ad hoc Networks (MANET) and their applications from failures. Once a MANET component is affected by a malicious, compromised or selfish node its operational state shifts from normal state to vulnerable state. Online monitoring mechanisms can collect important MANET network data that can be effectively used to detect abnormal behaviors caused by attacks. In this article we develop an online multivariate analysis algorithm to analyze the behavior of network resources and protocols to proactively detect MANET attacks. This algorithm is based on multivariate fuzzy analysis technique. We have validated the algorithm and demonstrated how it can effectively detect wellknown attacks such as Denial of Service in MANET.
Clinical Neuropathology Practice Guide 3-2013: levels of evidence and clinical utility of prognostic and predictive candidate brain tumor biomarkers

A large number of potential tissue biomarkers has been proposed for brain tumors. However, hardly any have been adopted for routine clinical use, so far. For most candidate biomarkers substantial controversy exists with regard to their usefulness in clinical practice. The multidisciplinary neurooncology taskforce of the Vienna Comprehensive Cancer Center Central Nervous System Unit (CCC-CNS) addressed this issue and elaborated a four-tiered levels-of-evidence system for assessing analytical performance (reliability of test result) and clinical performance (prognostic or predictive) based on consensually defined criteria. The taskforce also consensually agreed that only biomarker candidates should be considered as ready for clinical use, which meet defined quality standards for both, analytical and clinical performance. Applying this levels-of-evidence system to MGMT, IDH1, 1p19q, Ki67, MYCC, MYCN and β-catenin, only immunohistochemical IDH1 mutation testing in patients with diffuse gliomas is supported by sufficient evidence in order to be unequivocally qualified for clinical use. For the other candidate biomarkers lack of published evidence of sufficiently high analytical test performance and, in some cases, also of clinical performance limits evidence-based confirmation of their clinical utility. For most of the markers, no common standard of laboratory testing exists. We conclude that, at present, there is a strong need for studies that specifically address the analytical performance of candidate brain tumor biomarkers. In addition, standardization of laboratory testing is needed. We aim to regularly challenge and update the present classification in order to systematically clarify the current translational status of candidate brain tumor biomarkers and to identify specific research needs for accelerating the translational pace.
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network

This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-the-art results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results. Our age, gender and emotion recognition models are available to developers through the Sighthound Cloud API at this https URL
A compositional merge of probabilistic processes in the alternating model

We investigate concurrency of probabilistic systems in the alternating model. So far two different merge operators that capture two different views how concurrent probabilistic processes may interleave have been proposed. We show that neither of these operators is compositional with respect to weak probabilistic bisimulation for the alternating model. Currently, we work on defining a variant of a merge operator that will preserve weak bisimulation. We present the basic points of our research in this presentation. Analysis of probabilistic systems has been in the focus of researchers for more than two decades. Early research mainly addressed problems concerning modelling of prob-abilistic systems. Lately it has been a real challenge to investigate more complex issues concerning equivalence and preorder relations between these systems, (such as (bi-)simulation relations), particularly in the presence of abstraction and internal (unobservable) activities. For the specification and verification of probabilistic systems that also incorporate non-deterministic choice, mainly two models are in use. In the alternating model [8, 9] the probabilistic behaviour is modelled by a graph that consists of two types of states and transitions, viz. probabilistic and non-deterministic. The other model is based on probabilistic automata (it is also known as the non-alternating model) [11, 12]. Here there is only one type of states (and transitions). Every transition is labelled with an atomic action, and, instead of ending at a single state, it ends in a distribution over a set of states. The differences and similarities, as well as an algebraic characterization of the two models have been extensively studied in several papers/by several authors [9, 2, 6, 7, 1, 13]. As one can observe, the alternating model permits a probabilistic choice to appear as the outermost operator, e.g. [9, 2], for instance in the process x π y where π is a probability ranging in (0, 1). In the graph representation this situation corresponds to appearances of probabilistic states with outgoing probabilistic transitions. The reason we should allow processes like x π y in our language is two fold: 1. we need it to specify that a choice can be made between two possibilities x and y, 2. probabilistic choice
The Phoenix Deep Survey: Spectroscopic Catalog

The Phoenix Deep Survey is a multiwavelength survey based on deep 1.4 GHz radio imaging, reaching well into the sub-100 μJy level. One of the aims of this survey is to characterize the submillijansky radio population, exploring its nature and evolution. In this paper we present the catalog and results of the spectroscopic observations aimed at characterizing the optically "bright" (R ≲ 21.5 mag) counterparts of faint radio sources. Of 371 sources with redshift determination, 21% have absorption lines only, 11% show active galactic nucleus signatures, 32% are star-forming galaxies, 34% show narrow emission lines that do not allow detailed spectral classification (owing to poor signal-to-noise ratio and/or lack of diagnostic emission lines), and the remaining 2% are identified with stars. For the star-forming galaxies with a Balmer decrement measurement we find a median extinction of AHα = 1.9 mag, higher than that of optically selected samples. This is a result of the radio selection, which is not biased against dusty systems. Using the available spectroscopic information, we estimate the radio luminosity function of star-forming galaxies in two independent redshift bins at z ≈ 0.1 and 0.3, respectively. We find direct evidence for strong luminosity evolution of these systems consistent with L1.4 GHz ∝ (1 + z)2.7.
Expression of two different tachykinin receptors in Xenopus oocytes by exogenous mRNAs

Three tachykinins, substance P (SP), neurokinin A (NKA), and neurokinin B (NKB), have been isolated from the mammalian nervous system. In accordance with the presence of multiple tachykinins, the existence of multiple tachykinin receptors has been suggested. These receptors differ in binding specificities for different tachykinins. However, it is not known whether these receptors are indeed made of different molecules or whether the same receptor molecule undergoes posttranslational modification at different destination tissues, thereby altering its binding specificity. We examined whether mRNAs isolated from different tissues may synthesize different types of tachykinin receptors in the same expression system. For this purpose, Xenopus oocytes were injected with poly (A)+ RNAs extracted from rat brain or bovine stomach, and their responses to different tachykinins were examined under voltage-clamp. On the basis of potency ranking of 6 tachykinin agonists, the receptor induced by rat brain mRNA was found to correspond to a tachykinin receptor subtype currently classified as the NK-1 (SP-P) receptor, whereas that synthesized by bovine stomach mRNA corresponded to the NK-2 (NK-A) receptor. Thus, each of the 2 receptors can be induced in the same expression system, depending upon the source of exogenous mRNA injected. Therefore, the difference in the nature of the 2 receptors does not seem to be due to the possible posttranslational modification alone. However, the ionic mechanisms underlying activation of the 2 receptors translated in oocytes were similar. It is likely that activation of the 2 receptors uses the same internal mediator in oocytes.
Diversity Coloring for Distributed Data Storage in Networks 1

Distributively storing widely shared files with redundancy is an important technique for high performance and fault-tolerance in information networks. This paper proposes a new file storage scheme for encoded files, aiming at satisfying highly varied QoS requirements and guaranteeing graceful performance-degradation while some data become inaccessible. In the scheme, every node can get a file by accessing data in its proximity of a bounded radius; and the variety of data in the proximity increases steadily when the proximity’s radius increases. We formulate the file storage scheme as a new graph coloring problem which we call diversity coloring. The diversity coloring problem is shown to be NP-hard for general graphs. An algorithm using a K-interleaving technique for obtaining diversity coloring on tree networks is presented. The algorithm is of low complexity and can guarantee to output solutions that minimize the length of the codeword representing the file and also minimize the delay for any node to retrieve any amount of distinct data. Various other aspects of the algorithm, as well as properties of diversity coloring on trees and more general graphs, are also studied. Index Terms — Distributed networks, diversity coloring, file assignment, K-interleaving, tree.
Unspoken knowledge: kindergarteners are sensitive to patterns in Chinese pinyin before formally learning it

ABSTRACT Do young children extract patterns from print to which they are naturally exposed but only in a limited way? This study investigated Chinese kindergarteners’ sensitivity to regularities in pinyin, an alphabetic coding system for non-alphabetic Chinese characters to which kindergarteners have limited exposure and little motivation for learning. Sixty 4-and 5-year-olds from Beijing took a pinyin decision task and a pinyin learning task and were assessed on Chinese word reading, Chinese word writing, and non-verbal IQ. Sensitivity to letter patterns in pinyin spellings and sensitivity to letter-sound correspondences in pinyin syllables appeared in 5-year-olds. After statistically controlling for age and IQ, sensitivity to letter-sound correspondences in pinyin syllables explained 4% additional variance in Chinese word reading and 7% additional variance in Chinese word writing. This study contributes novel evidence to statistical learning in early literacy, demonstrating that children are adept at pattern recognition in print even under limited conditions.
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark

Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.
Link layer measurements in sensor networks

Key issues in wireless sensor networks such as data aggregation, localisation, MAC protocols and routing, all have to do with communication at some level. At a low level, these are influenced by the link layer performance between two nodes. The lack of accurate sensor network specific radio models, and the limited experimental data on actual link behaviour warrant additional investigation in this area. We present the results from extensive experiments, exploring several factors that are relevant for the link layer performance. These include (i) the effect of interference from simultaneous transmissions, which has not been looked into before, (ii) the degree of symmetry in the links between nodes, and (iii) the use of calibrated RSSI measurements. Finally, we present some guidelines on how to use the results for effective protocol design.
Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

The current weather forecasting paradigm is deterministic, based on numerical models. Multiple estimates of the current state of the atmosphere are used to generate an ensemble of deterministic predictions. Ensemble forecasts, while providing information on forecast uncertainty, are often uncalibrated. Bayesian model averaging (BMA) is a statistical ensemble postprocessing method that creates calibrated predictive probability density functions (PDFs). Probabilistic wind forecasting offers two challenges: a skewed distribution, and observations that are coarsely discretized. We extend BMA to wind speed, taking account of these challenges. This method provides calibrated and sharp probabilistic forecasts. Comparisons are made between several formulations.
PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids

This paper proposes a new voltage control methodology using the particle swarm optimization (PSO) technique for smart grid loadability enhancement. The goal of this paper is to achieve reliable and efficient voltage profile/stability regulation in power grids. This methodology is based on the decouple power flow equations and the worst-case design technique. Specifically, the secondary voltage control (SVC) problem is formulated as an L-infinity norm minimization problem which considers overall load voltage deviations in electrical power systems as an objective model, and the PSO technique is employed to determine a robust control action which aims to improve voltage profile and to enlarge transmission grid loadability by optimal coordinated control of VAR sources. The methodology was successfully tested on several IEEE benchmark systems.
Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors

Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori, we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.
On the Universal Principles of Tense Embedding: The Lesson from Before

Languages that are classified as non-Sequence-of-Tense come in more than one variety (e.g., Arregui & Kusumoto 1998): some of these languages allow a past tense in beforeclauses while others do not. We propose that some languages have quantificational (existential) tenses, while others have pronominal (referential/bound) tenses. The past tense in before-clauses is ill-formed in a language that has quantificational tenses, because the semantics of before is incompatible with existential quantification over times. A language with pronominal tenses does not have this problem. The pronominal/quantificational tense distinction interacts with the Sequence-of-Tense parameter, providing a general theory of possible and impossible languages.
A Note on Convexity of the Expected Delay Cost in Single-Server Queues

In this note, we show that the expected delay cost for aG/G/1 queue is not necessarily convex in the arrival rate as sometimes claimed in the literature. We can prove, however, that the total expected delay cost rate is convex in the arrival rate. This cost rate is often of interest when designing queueing systems.
Feature selection using multi-objective genetic algorithms for handwritten digit recognition

Discusses the use of genetic algorithms for feature selection for handwriting recognition. Its novelty lies in the use of multi-objective genetic algorithms where sensitivity analysis and neural networks are employed to allow the use of a representative database to evaluate fitness and the use of a validation database to identify the subsets of selected features that provide a good generalization. Comprehensive experiments on the NIST database confirm the effectiveness of the proposed strategy.
True Amplitude Migration-Inversion in Phase Space

Summary We have developed a new method for pre-stack depth migration in laterally varying media possibly including anisotropy in a coupled ray-parameter domain. The conventional Kirchhoff migration is carried out in configuration space in which ray-theoretical Green’s functions are used to derive the so-called migration weights in a true amplitude migration. Our formula tion makes use of Chapman-Maslov Green’s function which results in a migration operator in the phase-space. The most general development includes both source and receiver Green’s functions in the mixed phase-space of ray-parameter and depth. For application to seismic data migration, this dictates the use of data transformed to source and receiver ray-parameter and delay time domain using double slant stacks. Although Maslov asymptotic theory overcomes some of the shortcomings of asymptotic ray theory, the principal advantage of our formulation for migration results from significant reduction of data volume and increase in computation speed. Wavefields that are propagated through the model are parameterized by surface source/receiver rayparameters. We make use of Eikonal equation based travel time algorithm to evaluate the integrand of the migration operator. The resulting algorithm is computationally very efficient and thus can be used for velocity model building.
Tests for Production of Bacterial Inhibitors In Viro

The phenomenon of bacterial interference has at tracted renewed interest because of its evident significance in the epidemiology of human disease due to Staphylococcus aureus. Interference has been studied clinically as a factor in staphylococcal colonization of hospital and outpatient populations and has also been employed successfully in the control of outbreaks of staphylococcal infection (1-3). Yet the phenomenon is poorly understood and laboratory examination of its occurrence has been limited to the chick embryo model, where interference by coagulase-negative staphylococci with lethal Staph. aureus infection has been demonstrated (4, 5). The present studies of interference between strains of Staph. aureus in experimental bums in rabbits were undertaken to develop an animal model which might be useful in exploring the mechanism(s) underlying this phenomenon and to obtain observations of some of the qualitative, quantitative, and temporal factors in bacterial interference.