Miracle-dz/UniFAR-FTUs huggingface dataset:
WireAR - legacy applications in augmented reality

Current augmented reality (AR) applications require that the application software be written to support a specific AR interface set up. WireAR was developed to enable output from any OpenGL application to be viewed in an AR fashion. This enables the output from any legacy graphical or scientific visualization applications to be viewed in a collaborative AR setting. This demonstration shows how the output of standard desktop visualization programs can be embedded into an augmented reality experience.
Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets

Background Multiple myeloma is a cancer which has a high occurrence rate and causes great injury to people worldwide. In recent years, many studies reported the effects of miRNA on the appearance of multiple myeloma. However, due to the differences of samples and sequencing platforms, a large number of inconsistent results have been generated among these studies, which limited the cure of multiple myeloma at the miRNA level. Methods We performed meta-analyses to identify the key miRNA biomarkers which could be applied on the treatment of multiple myeloma. The key miRNAs were determined by overlap comparisons of seven datasets in multiple myeloma. Then, the target genes for key miRNAs were predicted by the software TargetScan. Additionally, functional enrichments and binding TFs were investigated by DAVID database and Tfacts database, respectively. Results Firstly, comparing the normal tissues, 13 miRNAs were differently expressed miRNAs (DEMs) for at least three datasets. They were considered as key miRNAs, with 12 up-regulated (hsa-miR-106b, hsa-miR-125b, hsa-miR-130b, hsa-miR-138, hsa-miR-15b, hsa-miR-181a, hsa-miR-183, hsa-miR-191, hsa-miR-19a, hsa-miR-20a, hsa-miR-221 and hsa-miR-25) and one down-regulated (hsa-miR-223). Secondly, functional enrichment analyses indicated that target genes of the upregulated miRNAs were mainly transcript factors and enriched in transcription regulation. Besides, these genes were enriched in multiple pathways: the cancer signal pathway, insulin signal metabolic pathway, cell binding molecules, melanin generation, long-term regression and P53 signaling pathway. However, no significant enrichment was found for target genes of the down-regulated genes. Due to the distinct regulation function, four miRNAs (hsa-miR-19a has-miR-221 has-miR25 and has-miR223) were ascertained as the potential prognostic and diagnostic markers in MM. Thirdly, transcript factors analysis unveiled that there were 148 TFs and 60 TFs which bind target genes of the up-regulated miRNAs and target genes of the down-regulated miRNAs, respectively. They respectively generated 652 and 139 reactions of TFs and target genes. Additionally, 50 (31.6%) TFs were shared, while higher specificity was found in TFs of target genes for the upregulated miRNAs. Discussions Together, our findings provided the key miRNAs which affected occurrence of multiple myeloma and regulation function of these miRNAs. It is valuable for the prognosis and diagnosis of multiple myeloma.
A Survey of Real‐Time Hard Shadow Mapping Methods

Because of its versatility, speed and robustness, shadow mapping has always been a popular algorithm for fast hard shadow generation since its introduction in 1978, first for offline film productions and later increasingly so in real‐time graphics. So it is not surprising that recent years have seen an explosion in the number of shadow map related publications. Because of the abundance of articles on the topic, it has become very hard for practitioners and researchers to select a suitable shadow algorithm, and therefore many applications miss out on the latest high‐quality shadow generation approaches. The goal of this survey is to rectify this situation by providing a detailed overview of this field. We show a detailed analysis of shadow mapping errors and derive a comprehensive classification of the existing methods. We discuss the most influential algorithms, consider their benefits and shortcomings and thereby provide the readers with the means to choose the shadow algorithm best suited to their needs.
Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach

This paper presents a unified methodology for detecting, isolating and accommodating faults in a class of nonlinear dynamic systems. A fault diagnosis component is used for fault detection and isolation. On the basis of the fault information obtained by the fault-diagnosis procedure, a fault-tolerant control component is designed to compensate for the effects of faults. In the presence of a fault, a nominal controller guarantees the boundedness of all the system signals until the fault is detected. Then the controller is reconfigured after fault detection and also after fault isolation, to improve the control performance by using the fault information generated by the diagnosis module. Under certain assumptions, the stability of the closed-loop system is rigorously investigated. It is shown that the system signals remain bounded and the output tracking error converges to a neighborhood of zero.
Union schemas as a basis for a C++ extractor

An extractor is a program which processes source code and outputs facts about the code in a software exchange format (SEF). An SEF can be further specified by a schema, analogous to a schema for a database. This paper explains how two such schemas can be combined into a union schema as the basis for creating an extractor. We describe CPPX (C Plus Plus eXtractor), which transforms a schema designed as the internals of a compiler (GCC) to a schema designed for software exchange (Datrix). CPPX performs this transformation as a pipelined sequence of sub-transformations. At each stage in the pipeline, the intermediate data conforms to the union of the two schemas.
Computing 4-Fingered Force-Closure Grasps from surface Points Using Genetic Algorithm

This paper proposes an evolutionary computation method to compute force-closure grasps from surface points. The object is presented as set of points. The proposed method searches for grasping configurations without prior knowledge of object's geometry. The experiment is carried out to validate the proposed method. The result when compared with a random search method shows that the proposed method finds more and better grasping configurations
Long-term care insurance and integrated care for the aged in Japan

Abstract By the introduction of a public, mandatory program of Long-Term Care Insurance (LTCI) on April 1, 2000, Japan has moved towards a system of social care for the frail and elderly. The program covers care that is both home-based and institutional. Fifty percent of the insurance is financed from the general tax and the other fifty percent from the premiums of the insured. The eligibility process begins with the individual or his/her family applying to the insurer (usually municipal government). A two-step assessment process to determine the limit of benefit follows this. The first step is an on-site assessment using a standardised questionnaire comprising 85 items. These items are analysed by an official computer program in order to determine either the applicant's eligibility or not. If the applicant is eligible it determines which of 6 levels of dependency is applicable. The Japanese LTCI scheme has thus formalised the care management process. A care manager is entrusted with the entire responsibility of planning all care and services for individual clients. The introduction of LTCI is introducing two fundamental structural changes in the Japanese health system; the development of an Integrated Delivery System (IDS) and greater informatisation of the health system.
Do gender differences in career aspirations contribute to sticky floors

Purpose The purpose of this paper is to test hypotheses regarding the importance of employee preferences in explaining sticky floors, the pattern that women are, compared to men, less likely to start to climb the job ladder. Design/methodology/approach The authors use original data obtained using a survey and a vignette study in which participants had to score the likeliness with which they would accept job offers with different promotion characteristics. Findings The main findings are that young female professionals have a less pronounced preference for more demanding and less routinary jobs and that this effect is mediated by the greater risk aversion and anticipated gender discrimination among women. No gender differences were found in the relative likeliness to apply for jobs that involve a promotion in terms of job authority. Research limitations/implications The vignette method assumes that artificial settings with low stakes do not bias results. Another limitation follows from the focus on inter-organizational promotions among young professionals, which raises the question to what extent the results can be generalized to broader settings. Originality/value This paper contributes to the literature on gender differences in careers by measuring the impact of employee preferences on gender differences in career decisions.
A Third Order Delta-Sigma Modulator with Shared Opamp Technique for Wireless Applications

this paper described the design of A third orders delta-sigma modulator (DSM) exploited shared opamp technique in order to reduce number of opamp required, consequently the total power consumption for the modulator decreased as well as required area decreased too. The architecture relaxed comparator speed which appropriate for wireless applications. First and second stages are sharing one opamp in integration and sampling phase. The proposed circuit has been designed on TSMC 0.18um CMOS technology. 2MHz Bandwidth, 50dB Peak Signal-to-Quantization-Noise Ratio (SQNR), which is suitable for WCDMA, have been achieved. It consumes 2.4mW with power supply 1.2V and area is 0.3mm.
A Corpus-Based Study of Phoneme Distribution in Thai Adirek Munthuli

This paper presents steps in accessing Thai phoneme distribution from large-scale written Thai corpora. The data were from 12 text genres from InterBEST [1], considered the biggest Thai corpora. Each word was transliterated using the grapheme-to-phoneme software [2]. Then, frequency of words, frequency of 81 Thai phonemes in each genre, and the 95% CIs of average occurrences of each phoneme were calculated. Phonemes from any genre that did not fall within the 95% CI were counted. As a result, 3 genres whose distributions are highly incompatible with others were removed, resulting in the remaining of approximately 80% of the data. Finally, we obtained phoneme distribution of initials, finals, vowels, and tones. Importantly, 4 bigram frequencies (final-to-tone, vowel-to-final, initial-totone, and initial-to-vowel) and a trigram frequency (vowel-final-tone) were also given.
Bioprospecting of Marine Macrophytes Using MS-Based Lipidomics as a New Approach

The marine environment supports a remarkable diversity of organisms which are a potential source of natural products with biological activities. These organisms include a wide variety of marine plants (from micro- to macrophytes), which have been used in the food and pharmaceutical industry. However, the biochemistry and biological activities of many of these macrophytes (namely macroalgae and halophytes, including seagrasses) are still far from being fully explored. Most popular bioactive components include polysaccharides, peptides, phenolics and fatty acids (FAs). Polar lipids (glycolipids, phospholipids and betaine lipids) are emerging as novel value-added bioactive phytochemicals, rich in n-3 FA, with high nutritional value and health beneficial effects for the prevention of chronic diseases. Polar lipids account various combinations of polar groups, fatty acyl chains and backbone structures. The polar lipidome of macrophytes is remarkably diverse, and its screening represents a significant analytical challenge. Modern research platforms, particularly mass spectrometry (MS)-based lipidomic approaches, have been recently used to address this challenge and are here reviewed. The application of lipidomics to address lipid composition of marine macrophytes will contribute to the stimulation of further research on this group and foster the exploration of novel applications.
Supply Chain Model for Personalized Car Web Services Discovery

The XML standard adopted for the interoperability among web services had played prominent role in enhancing systems/application integration and also facilitates linking up client’s demands through loosely couple platform. However, the use of this technology has a major disadvantage of lacking in semantic representations of the contents in the web and makes the service discovery scheme of the UDDI inefficient. Adding semantic features to web services has a long role to play in facilitating automatic publishing, discovery, composition, and usage of these numerous services available on the web which are dynamically published and discovered. In this paper, we applied the use of semantic registry-based system meant for personalizing car web services’ discovery. We maintained an OWL-based ontology for the car service together with a catalogue that keeps track of the published services for easy discovery. The semantic registry-based approach enhances the service discovery and guarantees Quality of Service by ensuring satisfaction of user’s requests which web service personalization is meant to achieve.
Learners’ perspectives on Life Orientation sexuality education in South Africa

ABSTRACT This article examines the views of male and female learners regarding how Life Orientation (LO) sexuality education is taught at their schools. Learners in the study were selected from five former ‘Black’ schools in the Eastern and Western Cape Provinces of South Africa. Focus groups were used to identify what learners could recall about their LO sexuality education classes. The strong trend in the data speaks to how LO sexuality education implies a gendered, heteronormative and moralistic approach to youth sexuality which silences and negates same sex relationships and girls’ accounts of sexuality. Although LO sexuality curricula are, as crafted on paper, often sophisticated learning programmes, participants point to a disjuncture between the official LO sexuality education curriculum and how LO sexuality education is taught in the studied schools. The paper concludes with some specific recommendations for teachers to promote a non-judgemental approach to sexuality education that challenges heteronormativity and other gendered injustices as part of the teaching of LO sexuality education.
Sentencing Drug Court Failures: Judicial Considerations With Increased Offender Information

This study explores the influential predictors of sentence severity within a sample of drug court failures. This sample is unique, in that, judges possess greater amounts of offender information at the time of sentencing, relative to conventional adjudications. Due to defendants’ participation in a drug court program, judges possessed offender program performance information in addition to traditionally assessed criminogenic and offender indicators. Data were collected from 320 individuals who participated, yet failed, in Midwestern adult felony drug court program. Results suggest that, in these instances, judicial sentencing decisions were influenced by case and program performance characteristics. Moreover, judges who oversaw defendants in the program sentenced participant failures more harshly than judges who had no affiliation with the drug court or offender prior to sentencing.
Network Coding Aided Cooperative Cognitive Radio for Uplink Transmission

An uplink transmission for Adaptive Dynamic Network Coding(ADNC) assisted Cooperative Cognitive Radio(CCR) system is proposed for facilitating the recovery of the source information received from the Primary Users (PUs) at the BS. The Cognitive Users (CUs) acting as Relay Nodes invoke the ADNC technique, where the CCR- based control information is exchanged between the CUs and the BS. % The network encoder may be activated in its adaptive mode for the sake of supporting the CUs, depending on the Boolean value of the feedback flags generated by the receiver based on the success/failure of the Adaptive Turbo Trellis Coded Modulation (ATTCM) channel decoder and of the network decoder. As a result, our novel ATTCM-ADNC-CCR system constructed based on a holistic approach is capable of providing an increased throughput, despite reducing the transmission-period of the PU. This reduced transmission-period can also be directly translated into an increased time-duration for the secondary communications of the CUs.
WAVELENGTH ROUTERS IN WDM LOCAL AREA NETWORKSMichael

This paper presents a novel scheme for scheduling packet traac on a wavelength-division-multiplexed (WDM) local optical network which employs a wavelength router. An N physical wavelengths. We propose TDM schedules for interconnecting MN nodes with a wavelength router. We show that for non-zero transceiver tuning time, throughput on the order of N can be achieved.
The Structural Model of Educational Self-regulation Based on Learning Strategies and Attributional Styles by the Mediator of Achievement Motivation Among Secondary High School Students in Sari in 2017-2018

Background: Achievement motivation is one of the critical variables that predict educational achievement and success in the future. Therefore, the identification of effective factors upon achievement motivation may be one of the basic subjects in education. The present study aimed to evaluate the structural model of educational self-regulation based on learning strategies by the mediator of achievement motivation among students. Methods: The present study was correlation research with structural equation modeling (SEM). The statistical universe of this study consisted of all boy and girl students at secondary high schools in 2017-2018. The multistep random cluster sampling selected 375 (215 girls and 160 boys). Data gathered by the Bouffard educational self-regulation questionnaire, learning strategies questionnaire, attributional style questionnaire, and Herman’s achievement motivation questionnaire. Data analysis gathered by path analysis and structural equations with Lisrel 8.80 software. Results: The research results indicated the direct effects of learning strategies and attributional styles upon achievement motivation (P<0.05). Also, model fitting indicated indirect effects of learning strategies and attributional styles by achievement motivation upon students learning self-regulation (P<0.01). Conclusion: Research results indicate that self-regulation learning strategies are educational. The teaching of these strategies is effective in increasing internal attribution style for positive events and decreasing this style for adverse events among students.
Automatic Intelligibility Assessment of Dysarthric Speech Using Phonologically-Structured Sparse Linear Model

This paper presents a new method for automatically assessing the speech intelligibility of patients with dysarthria, which is a motor speech disorder impeding the physical production of speech. The proposed method consists of two main steps: feature representation and prediction. In the feature representation step, the speech utterance is converted into a phone sequence using an automatic speech recognition technique and is then aligned with a canonical phone sequence from a pronunciation dictionary using a weighted finite state transducer to capture the pronunciation mappings such as match, substitution, and deletion. The histograms of the pronunciation mappings on a pre-defined word set are used for features. Next, in the prediction step, a structured sparse linear model incorporated with phonological knowledge that simultaneously addresses phonologically structured sparse feature selection and intelligibility prediction is proposed. Evaluation of the proposed method on a database of 109 speakers consisting of 94 dysarthric and 15 control speakers yielded a root mean square error of 8.14 compared to subjectively rated scores in the range of 0 to 100. This is a promising performance in which the system can be successfully applied to help speech therapists in diagnosing the degree of speech disorder.
Learning from Noisy Data using Hyperplane Sampling and Sample Averages

We present a new classification algorithm capable of learning from data corrupted by a class dependent uniform classification noise. The produced classifier is a linear classifier, and the algorithm works seamlessly when using kernels. The algorithm relies on the sampling of random hyperplanes that help the building of new training examples of which the correct classes are known; a linear classifier (e.g. an svm) is learned from these examples and output by the algorithm. The produced examples are sample averages computed from the data at hand with respect to areas of the space defined by the random hyperplanes and the target hyperplane. A statistical analysis of the properties of these sample averages is provided as well as results from numerical simulations conducted on synthetic datasets. These simulations show that the linear and kernelized versions of our algorithm are effective for learning from both noise-free and noisy data.
Acquisition and identification of OFDM signals using cyclostationary signatures

An effective solution for increasing spectrum efficiency in current networks assumes OFDM-based cognitive radio (CR) systems, which allow for reconfigurable activation and loading on specific group of subcarriers in locally available spectrum. However, each CR receiver should synchronize itself to appropriate carrier frequency and identify currently activated subcarriers and corresponding waveform parameters, i.e., applied modulation/coding scheme. Proposed demonstrator, showcases dynamic reconfiguration and identification of OFDM-based CR system utilizing the detection of cyclostationary signatures, intentionally embedded features that introduce certain periodicity exhibited by second order statistics. Presented framework additionally allows to easily extend given scenario to multiuser environment which assumes coexistence and identification of several CR transmitter/receiver pairs operating with different waveform parameters within the common dedicated bandwidth.
Classification of neovascularization using convolutional neural network model

Neovascularization is a new vessel in the retina beside the artery-venous. Neovascularization can appear on the optic disk and the entire surface of the retina. The retina categorized in Proliferative Diabetic Retinopathy (PDR) if it has neovascularization. PDR is a severe Diabetic Retinopathy (DR). An image classification system between normal and neovascularization is here presented. The classification using Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine, k-Nearest Neighbor, Naive Bayes classifier, Discriminant Analysis, and Decision Tree. By far, there are no data patches of neovascularization for the process of classification. Data consist of normal, New Vessel on the Disc (NVD) and New Vessel Elsewhere (NVE). Images are taken from 2 databases, MESSIDOR and Retina Image Bank. The patches are made from a manual crop on the image that has been marked by experts as neovascularization. The dataset consists of 100 data patches. The test results using three scenarios obtained a classification accuracy of 90%-100% with linear loss cross validation 0%-26.67%. The test performs using a single Graphical Processing Unit (GPU).