Miracle-dz/UniFAR-FTUs huggingface dataset:
In : Fault Detection : Classification , Techniques and Role inIndustrial Systems

Complex industrial systems, such as gas turbine engines, ha ve m ny heterogeneous subsystems (e.g., thermal, mechanical, hydraulic and elec trical) with complex thermalhydraulic, electro-mechanical, and electronic interacti ons. Schedule-based policies are largely followed in the industry for maintenance of complex systems. Since the degradation profiles of individual systems are usually different due to manufacturing and usage variations, a scheduled maintenance policy in most ca ses becomes either overly conservative or a source of serious safety concern. This cal ls for development of reliable and cost-effective health management for complex indu strial systems. However, reliable first-principle modeling of such systems may often b very expensive (e.g., in terms of computational memory, execution time, and cost) and hence may become inappropriate for condition monitoring, fault detection, diagnostics and prognostics. This chapter describes a data-driven framework to obtain ab str ct models of complex systems from multiple sensor observations to enable co nditi n monitoring, diagnostics and supervisory control. The algorithms are form ulated in the setting of symbolic dynamic filtering (SDF) that has been recently repo ted in literature. The underlying concept of SDF is built upon the principles of symbo lic dynamics, statistical pattern recognition, probabilistic graphical models and i nformation theory. SDF was initially developed for statistically quasi-stationary d ata and then it was successfully extended to analysis of transient data. The tool-chain invo lves data space abstraction for analyzing mixed data (i.e., continuous, discrete and ca tegorical) without significant loss of information. Spatiotemporal pattern discovery & fu sion algorithms have been built upon Markovian & Bayesian network concepts and health status classifications This work has been supported in part by the U.S. Army Research l boratory and the U.S. Army Research Office under Grant No. W911NF-07-1-0376 and by NASA under Coo perative Agreement No. NNX07AK49A. Currently with United Technologies Research Center, E-mai l address: sarkars@utrc.utc. com E-mail address: soumalya.still. dreaming@gmail.com E-mail address: axr2@psu.edu 2 Soumik Sarkar, Soumalya Sarkar and Asok Ray by using machine learning techniques. The chapter also pres ents applications of the generic data-driven tool in heterogeneous areas, such as fa ult detection in aircraft gas turbine engines, fatigue damage prediction in structural m aterials, and health monitoring in nuclear power plants & ship-board auxiliary systems.
A Real-time Rtl Engineering-change Method Supporting On-line Debugging For Logic-emulation Applications

In recent years, logic emulation has been widely usedas a key design verification methodology in many complex CPU, telecom, and multimedia design projects. When using logic emulation for design verification, designers often need toperform engineering changes as a result of design debugging of a design specificationmodification. One of the essential issues to engineeringchanges is the turn-around time. Ideally, after designers modify their designs, they resume their debugging and verification tasks immediately. However, converting a design from its Register-Transfer-Level (RTL) description to a target emulator is a time-consuming procedure which may take hours. Such long engineering-change turn-around times are unacceptable by the designers. In this paper, we present a real-time RTLengineering-change method supporting on-line debuggingfor logic-emulation applications. We propose a novel design method which is able to link design data generated at different design stages in a unified way. Using thismethod, the users can immediately locate the portion ofthe circuit design affected by the design modification fromits RTL specification. This feature provides users with afast time-to-debug environment by significantly improving the efficiency of the engineering-change process. We have developed aprototype system Quick ECO supporting interactive on-line RTL engineering changes. Experimental results on a number of industrial designs are reported to demonstrate the effectiveness of the proposed method.
A possible role of the Infant/Toddler Sensory Profile in screening for autism: a proof-of-concept study in the specific sample of prematurely born children with birth weights <1,500 g

Objective The objective of this study was to explore the potential of the Infant/Toddler Sensory Profile (ITSP) as a screening tool for autism spectrum disorders (ASD) in prematurely born children. Methods Parents of 157 children with birth weights <1,500 g (aged 2 years, corrected for prematurity; 88 boys, 69 girls) completed a screening battery that included the ITSP, Modified Checklist for Autism in Toddlers (M-CHAT), and the Communication and Symbolic Behavior Scales Developmental Profile Infant-Toddler Checklist (CSBS-DP-ITC). Children with known disabilities were excluded. All the children who were screened positive on any of the screening tools subsequently underwent clinical examination including the Autism Diagnostic Observation Schedule. Results We used classification trees to answer the question whether ITSP (or some of its subscales) could be combined with the M-CHAT and/or the CSBS-DP-ITC or its subscales into an effective ASD screening tool. Using the CSBS-DP-ITC, overall score, and the Sensation Seeking subscale of the ITSP, we obtained a screening tool that was able to identify all of the ASD children in our sample (confirmed by cross-validation). The proposed screening tool is scored as follows: 1) if the overall CSBS-DP-ITC value is <45.5, then the screening is positive; 2) if the overall CSBS-DP-ITC value is ≥45.5 and the z-score of the Sensation Seeking subscale of ITSP is ≥1.54, then the screening is positive; 3) otherwise, the screening is negative. Conclusion The use of CSBS-DP-ITC in combination with the Sensation Seeking subscale of the ITSP improved the accuracy of autism screening in preterm children.
Size-resolved emission rates of airborne bacteria and fungi in an occupied classroom

The role of human occupancy as a source of indoor biological aerosols is poorly understood. Size-resolved concentrations of total and biological particles in indoor air were quantified in a classroom under occupied and vacant conditions. Per-occupant emission rates were estimated through a mass-balance modeling approach, and the microbial diversity of indoor and outdoor air during occupancy was determined via rDNA gene sequence analysis. Significant increases of total particle mass and bacterial genome concentrations were observed during the occupied period compared to the vacant case. These increases varied in magnitude with the particle size and ranged from 3 to 68 times for total mass, 12–2700 times for bacterial genomes, and 1.5–5.2 times for fungal genomes. Emission rates per person-hour because of occupancy were 31 mg, 37 × 106 genome copies, and 7.3 × 106 genome copies for total particle mass, bacteria, and fungi, respectively. Of the bacterial emissions, ∼18% are from taxa that are closely associated with the human skin microbiome. This analysis provides size-resolved, per person-hour emission rates for these biological particles and illustrates the extent to which being in an occupied room results in exposure to bacteria that are associated with previous or current human occupants. Practical Implications Presented here are the first size-resolved, per person emission rate estimates of bacterial and fungal genomes for a common occupied indoor space. The marked differences observed between total particle and bacterial size distributions suggest that size-dependent aerosol models that use total particles as a surrogate for microbial particles incorrectly assess the fate of and human exposure to airborne bacteria. The strong signal of human microbiota in airborne particulate matter in an occupied setting demonstrates that the aerosol route can be a source of exposure to microorganisms emitted from the skin, hair, nostrils, and mouths of other occupants.
Broadening the Science of Broadening Participation in STEM Through Critical Mixed Methodologies and Intersectionality Frameworks

If we wish to see our STEM (science, technology, engineering, and mathematics) broadening participation efforts affect change, we must also critically reflect upon and broaden our scientific approaches to studying STEM participation, adopting methodologies and frameworks that most appropriately fit the problems and questions at hand. In this article, we discuss how critical mixed-methodological approaches and intersectionality frameworks offer the possibility of a science of broadening participation that deeply understands, contextualizes, and addresses complex barriers to STEM inclusion. First, we describe the suggested approaches and frameworks, illustrating how they allow us to improve how we collect, measure, interpret, and analyze data. Next, we provide some specific examples of how such approaches and frameworks have enriched our scientific work. Last, we offer some final recommendations for researchers seeking to broaden the science of broadening participation in STEM.
Automatic Categorization of Software Modules

Summary The world of software has demonstrated the remarkable appeal of communal software development. Large number of software projects can leverage, reuse, and coordinate their work through internet and web-based technology. For example, Source-Forge currently hosts about sixty thousand software systems, similar strategies have suggested for corporate software development. With thousands of projects, manually locating related projects can be difficult. Hence to use automatic software categorization to find clusters of related software projects using only the source code from projects, automatic categorization of software experiments with a set of programs. Automatic categorization of software systems is a novel and intriguing challenge on software archive. Evolution has focused on determining intracomponent relations of given software system also increase to differentiate between categories. Function oriented produces better result than the object oriented. Automatic categorization of software has provided better results than LSA retrieval techniques in terms of Precision and Recall with multinomial Naive Bayes scheme has outperformed all other approaches and shows better results than the existing approach (SVD), being used by some open source code repositories e.g. Source forge Hence, the tool can also be utilized for the automatic categorization of software components and this kind of automation may improve.
New Dropout Prediction for Intelligent System

The main purpose of this research is to develop a dynamic dropout prediction model for universities, institutes and colleges. In this work, we first identify dependent and independent variables and dropping year to classify the successful from unsuccessful students. Then we have classify the data using Support Vector Machines(SVM). SVM helped the data set to be properly design and manipulated . The main purpose of applying this identification is to design a Knowledge Base which is sometimes known as joint probability distribution . The concepts of propositional logic helped to build the knowledge Base. Bayes theorem will perform the prediction by collecting the information from knowledge Base. Here we have considered most important factors to classify the successful students over unsuccessful students are gender, financial condition and dropping year. We also consider the socio-demographic variables such as age, gender, ethnicity, education, work status, and disability and study environment that may in-flounce persistence or dropout of students at university level
Multithread Reconfiguration Algorithm for Mesh-Connected Processor Arrays

Mesh-connected processor array is a popular architecture used in parallel processing. Extensive studies have been conducted on reconfiguration algorithms for the processor arrays with faults, but few work is on parallel algorithm to accelerate the reconfiguration. This paper presents a fast algorithm to reconfigure two dimensional mesh-connected processor arrays with faults. A traditional algorithm is successfully accelerated in the manner of multithread, without loss of harvest. The proposed algorithm reconfigures the processor array with the mechanics of route distance in order to avoid the routing errors. Simulation results show that the proposed algorithm can accelerate the reconfiguration nearly by 15 times on a 64 × 64 array in comparison to the traditional algorithm cited in this paper.
Stable states of perturbed Markov chains

Given an infinitesimal perturbation of a discrete-time finite Markov chain, we seek the states that are stable despite the perturbation, \textit{i.e.} the states whose weights in the stationary distributions can be bounded away from $0$ as the noise fades away. Chemists, economists, and computer scientists have been studying irreducible perturbations built with exponential maps. Under these assumptions, Young proved the existence of and computed the stable states in cubic time. We fully drop these assumptions, generalize Young's technique, and show that stability is decidable as long as $f\in O(g)$ is. Furthermore, if the perturbation maps (and their multiplications) satisfy $f\in O(g)$ or $g\in O(f)$, we prove the existence of and compute the stable states and the metastable dynamics at all time scales where some states vanish. Conversely, if the big-$O$ assumption does not hold, we build a perturbation with these maps and no stable state. Our algorithm also runs in cubic time despite the general assumptions and the additional work. Proving the correctness of the algorithm relies on new or rephrased results in Markov chain theory, and on algebraic abstractions thereof.
A Neural-Symbolic Cognitive Agent with a Mind's Eye

The DARPA Mind’s Eye program seeks to develop in machines a capability that currently exists only in animals: visual intelligence. This paper describes a Neural-Symbolic Cognitive Agent that integrates neural learning, symbolic knowledge representation and temporal reasoning in a visual intelligent system that can reason about actions of entities observed in video. Results have shown that the system is able to learn and represent the underlying semantics of the actions from observation and use this for several visual intelligent tasks, like recognition, description, anomaly detection and gap-filling.
Cultural and Ethical Aspects of Social Desirability in Psychological Research

This study is part of an exploratory work on the child-parent relationship from the perspective of attachment theory and parenting styles. In the context of an increased interest in psychological research on foster parenting, the implications of social desirability for psychological assessment are discussed. This chapter aims to answer the following questions: what is the difference in social desirability between biological and foster parents? What is the effect size of the different correlations between social desirability and other psychological characteristics in the two groups of parents? The research sample was formed of 216 dyads, of which 108 parent-biological child and 108 foster parent-foster child. The ethical aspects of clinical research and the cultural influences associated with social desirability are discussed.
Face recognition based on Haar LBP histogram

In this paper, a novel face representation approach, Haar Local Binary Pattern histogram (HLBPH), is proposed to represent the face images. First, the face image is decomposed into four-channel subimages in frequency domain by Haar wavelet transform, and then the LBP operator is applied on each subimage to extract the face features. After that, a novel Haar LBP representation method (HLBPH) based on two-layer weighted fusion scheme is presented to balance the face block regions for LBP, to fuse the multi-channel face features. In recognition stage, the Chi square statistic(χ2) is employed as the dissimilarity measure for face histograms represented by HLBPH. Finally, the proposed algorithm is tested on ORL and Yale face database, and achieve satisfying results to pose, expression and illumination variations.
Towards Interoperable Reporting Standards for Omics Data: Hopes and Hurdles

BACKGROUND As the size and complexity of scientific datasets and the corresponding information stores grow, standards for collecting, describing, formatting, submitting and exchanging information are playing an increasingly active role. Several initiatives occupy strategic positions in the international scenario, both within and across domains. However, the job of harmonising reporting standards is still very much a work in progress; both software interoperability and the data integration remain challenging as things stand. RESULTS The status quo with respect to standardization initiatives is summarized here, with particular emphasis on the motivation for, and the challenges of, ongoing synergistic activities amongst the academic community focused on the creation of truly interoperable standards. CONCLUSIONS Groups generating standards should engage with ongoing cross-domain activities to simplify the integration of heterogeneous data sets to the greatest possible extent.
Real-time quantitative polymerase chain reaction assessment of immune reactivity in melanoma patients after tumor peptide vaccination.

BACKGROUND Monitoring the immune response to epitope-specific vaccination in cancer patients is important for vaccine development. The traditional method, in which the in vitro sensitization of peripheral blood mononuclear cells (PBMCs) with epitope is compared before and after vaccination, is time-consuming and allows only a qualitative assessment of the response. We used a rapid, quantitative, real-time polymerase chain reaction (PCR) assay to directly measure the immune reactivity of patients' PBMCs to the vaccine epitope. METHODS PBMCs were obtained from melanoma patients before and after two rounds of vaccination with either g209-2M, a peptide derived from melanoma protein gp100 (n = 24), or ESg209-2M, a modified version of this peptide (n = 20). PBMCs were tested for immune reactivity by assaying interferon gamma (IFN gamma) protein release after in vitro sensitization with the epitope or for IFN gamma messenger RNA expression by real-time PCR. A twofold or more increase in IFN gamma protein release or a 1.5-fold or more increase in IFN gamma transcript accumulation in PBMCs after vaccination was considered to be evidence of a specific response. Correlation between the two methods was tested by use of the Spearman correlation coefficient after the results were ranked as positive or negative. All statistical tests were two-sided. RESULTS The results obtained with the two methods were strongly correlated (Spearman's rho = 0.72; P =.0006). The g209-2M and Esg209-2M peptides resulted in similar percentages of vaccine-specific reactivity in PBMCs after in vitro sensitization (63% and 65% of patients, respectively; Fisher's exact test P =.6 for comparison of the two groups). The PCR method could detect vaccine-specific reactivity in a subset of patients (38% and 35% of patients, respectively; Fisher's exact test P =.7 for comparison of the two groups). CONCLUSION Vaccination induces circulating antitumor lymphocytes, albeit in low frequencies, capable of directly reacting with tumor antigen. PBMCs of vaccinated individuals can respond to a vaccine-specific stimulus in a direct assay that does not require prolonged in vitro manipulations.
Active Surveying

Opinion leaders play an important role in influencing people’s opinions, actions and behaviors. Although a number of methods have been proposed in literature for identifying influentials using secondary sources of information, the true set of opinion leaders revealed through primary sources, such as surveys, is still favored in many domains. In this work, we present a new active learning method, which combines secondary data with partial knowledge from primary sources, to guide the primary information gathering process. We apply our proposed active surveying method to a dataset from the pharmaceutical domain, and show how we are able to efficiently obtain the true set of opinion leaders while minimizing the required acquisition of primary data.
Pragmatic framework of 3D visual navigation for mobile user

Mobile user navigation system explore and apply the emerging of 3D visualization system for visualizing and navigating conveniently in an environment. The important consideration in visualization navigation is the real time operation and the application device's computational complexity capabilities. Currently more and new research are encourage on 3D application for mobile device that will give user truly and accurately virtual reality 3D environment in real time for user to find its path to navigate from one location to other location in an environment. Unfortunately, there is no commonly agreed way to determine the accurate 3D visualization and navigation system accompanying with the increasing activity of people among cities and countries around the world, the requirement for navigating people's movement grows with unexpected speed. This paper provides a framework for the improved 3D visualization and navigation system. The framework will provide some contributions for consolidation of visualization navigation applications and techniques on mobile devices for user within an environment.
Estonian Emotional Speech Corpus: Culture and Age in Selecting Corpus Testers

The Estonian Emotional Speech Corpus serves as the acoustic basis for emotional text-to-speech synthesis. Because the Estonian synthesizer is a TTS-synthesizer, we started off by focusing on read texts and the emotions contained in them. The corpus is built on a theoretical model and we are currently at the stage of verifying the components of the model. In the present article we give an overview of the corpus and the principles used in selecting its testers. Some studies show that people who have lived longer in a certain culture can more easily recognize vocal expressions of emotion that are characteristic of the culture without seeing the speaker's facial expressions. We therefore decided not to use people under 30 years of age as testers of emotions in our theoretical model. We used two tests to verify the selection principles for the testers. In the first test, 27 young adults aged under 30 were asked to listen to and identify the emotion (joy, anger, sadness, neutral) of 35 sentences. We then compared the results with those of adults aged over 30. In the second test we asked 32 Latvians listen to the same sentences, and then compared the results with those of Estonians. Our analysis showed that younger and older testers, Estonians and Latvians perceive emotions quite differently. From these test results we can say that the selection principle of corpus testers, using people who are more familiar with Estonian culture, is acceptable. The study was supported by the National Program for Estonian Language Technology and the project SF0050023s09 “Modeling intermodular phenomena in Estonian”.