Miracle-dz/UniFAR-FTUs huggingface dataset:
A unified ontology namespace for enterprise integration - a digital twin case study

In order to be able to scale business enterprises and digitally transform them, we need to be able to readily integrate business data. Traditional systems have business data travelling through different layers of the automation stack vertically. This study presents an architecture that employs a unified ontology namespace for integration of business data. Such an architecture is thought to enhance transparency and visibility easing integrations. A use-case exploiting such an architecture with webservices is presented to convey the ease at which different applications, such as a digital twin, can be “plugged in” to realize further business potential of enterprises.
Automation of Balanced Nested Design; NeDPy

This work was done considering the principle of automating nested designs. It was aimed at developing a user friendly statistical package (NeDPy) that can be used to analyse experiment that has nested factor(s). NeDPy was coined from “NESTED DESIGN PYTHON” and was developed using python programming language. Python modules like NumPy, Scipy, Matplotlib, Tkinter etc. was used to develop a user-friendly app with GUI (graphical user interface) that can analyse a two-stage and three-stage nested design to produce all relevant information. These include, indicating significant factor(s), creating ANOVA table, giving estimate for model and variance component, descriptive analysis, generating the residuals of the data set, drawing diagnostic plots like normal probability plot, box plot etc. NeDPy was used to solve problems from some cited sources where the problem has been solved both manually and using some trusted software. Some important results were compared and found to be identical. This validates NeDPy and makes it a recommendable statistical package for analysing nested design.
MEME CHAT: AN INNOVATIVE SOCIAL MEDIA PLATFORM FOR CONTENT MONETIZATION & CORPORATE OUTREACH USING OCR & NLP

Social media has boomed in recent years, along with platforms for user generated content. Major social network platforms like Google and Facebook use a singular advertisement mediation service, where ads are provided by corporates to target users/customers. Facebook analyses and uses a users meta data and content consuming behavior to present the best suitable commercial. We, in this work, show an innovative approach where advertisement mediation is done using users own generated content, in the form of memes on a corporate. Memes are chosen as a medium due to their supreme ability to penetrate the internet and get viral quickly. Because the content is generated by the user, who is also the target of the advertisement, the content is well understood, received and appreciated in comparison of content used in other approaches like Facebook audience network, Google Adsense or Twitter MoPub. Using this method, users are renumerated monetarily for the content they post, which also becomes the advertisements that will be later used by corporates. We track user generated content, for subscribed corporates using Tesseract algorithm for optical character recognition along with Vader NLP. for NLP. and provide results that strongly suggest why the method suggested in this article is useful and advantageous compared to other methods.
Characterization And Remediation For Soft Error Reliability On Gpu

of the Dissertation Characterization and Remediation for Soft Error Reliability on GPU by Fritz Gerald Previlon Doctor of Philosophy in Computer Engineering Northeastern University, August 2019 Dr. Kaeli, Advisor Graphic Processing Units (GPUs) have become the accelerator of choice for improving the performance of many of the most demanding applications. While performance of these devices continues to improve generation after generation, reliability of these devices has not been studied rigorously. Several sources of errors can undermine the reliability of these devices, including radiation-induced transient faults, environmental perturbations, and process, temperature or voltage variations. In particular, transient faults in GPU execution have become a significant threat to high performance computing (HPC) and safety-critical applications. HPC systems experience transient faults every few tens of hours and the trend is expected to become worse. A key point in the study of transient faults and their effects on user programs is that some faults do not cause undesirable results in the affected programs. This is especially true for GPU applications. Past efforts to study and understand GPU vulnerability to transient faults have demonstrated reliability can vary greatly across different applications. A significant amount of resilience resides intrinsically in some GPU applications. Transient faults in these applications are not likely to affect the results they produce. Other applications are highly sensitive to transient faults and are likely to crash or produce incorrect results when they are affected by transient faults. While it is generally a good idea to protect the GPU hardware from transient faults, the penalties incurred in terms of performance, power and area are not always justifiable, depending on the applications utilizing the hardware resources. Understanding the relationship between the underlying program characteristics and their implications on vulnerability is crucial. The inherent resilience in applications should be carefully considered when making decisions about designing protection mechanisms to guard against nefarious transient faults. In this thesis, we focus on program characteristics that contribute to their vulnerability. We offer several methodologies that aim at alleviating the prohibitively expensive process of quantifying
Diagnosis of stator faults in permanent magnet synchronous machine using finite element method

This paper attempts to analyse the characteristics of the inter-turn short circuit fault for the permanent magnet synchronous motor (PMSM). Based on finite element analysis (FEA), the influence of the stator faults on the behaviour of the PM machine is studied. A simple dynamic model for a PM machine with inter-turn winding fault is presented. The precision of the proposed finite element (FE) model is verified by a comparison of the simulation results tests. Using the simulated model, a technical method, based on fast fourier transform (FFT) analysis of stator current and electromagnetic torque, is exported to detect the inter turn fault. The technique used and the obtained results show clearly the possibility of extracting signatures to detect and locate faults.
Evaluating a Faceted Search Approach for Efficient News Event Filtering

Thismaster thesis presents the process of developing and evaluation of three search interface prototypes of high fidelity. Applying Design Science research for problem-solving, meeting with experts within the news domain to ensure the best possible development of the prototypes as possible. The aim is to investigate the user’s behavior from the three types of search interface and compare the effectiveness of the user’s point of view. The goal of the thesis is to find out to what extent the faceted search approach developed is efficient. To do so, I compare to other baseline approaches typically found on the Web to search for news content. User evaluation was conducted through one experiment to delve into the behavior of the users and the performance of these three interfaces. A baseline search interface includes a search only option. The second search interface shows the summary and which category each article is entitled to. The last search interface (faceted filtering) uses the same establishment as the latter one with addition facets functions. The three prototypes were evaluated with quantitative and qualitative methods for data gathering. Semi-structured interview, questionnaires, observation and System Usability Scale(SUS). The feedback was valuable, as a result, indicate a significant difference between the search interfaces from user performance, where the faceted filtering search interface is more efficient compared to the other two baseline approaches search interfaces. The faceted filtering search interface exceeds the baseline and the text summary search interface in performance and outperforms the other two baselines in terms of user satisfaction.
Stereotactic body radiation therapy (SBRT) for patients with stage I non-small cell lung cancer is applicable to more tumors than sublobar resection

Background Virtually all patients with medically inoperable stage I non-small cell lung cancer (NSCLC) can receive stereotactic body radiation therapy. However, the percentage of such patients in whom sublobar resection is technically feasible is unknown. This discrepancy can confound clinical trial eligibility and designs comparing stereotactic body radiation therapy vs. sublobar resection. Methods A total of 137 patients treated with stereotactic body radiation therapy for lung lesions (3/2013–11/2017) underwent retrospective review. Diagnostic CT chest and PET/CT images, stereotactic body radiation therapy dates, and demographic data were collected on 100 of 137 patients. Two experienced board-certified thoracic surgeons independently reviewed anonymized patients’ pre-stereotactic body radiation therapy diagnostic imaging and completed a custom survey about the technical feasibility of sublobar resection for each patient. Interrater agreement was measured using Cohen’s kappa coefficient by bootstrap methodology. Summary statistics were performed for baseline demographics and tumor characteristics. Results Of the 100 patients, 57% were female, with median age of 75 years (range, 52–95 years) and Karnofsky Performance Status of 80 (range, 40–100). Most patients (61%) had Stage IA1, T1a tumors. For interrater agreement analysis, one patient was removed from each cohort due to inability to locate tumor on images, leaving 98 patients analyzed. Comparing Surgeon #1 vs. Surgeon #2, 64 (65.3%) vs. 69 (70.3%) of tumors were thought eligible for sublobar resection, respectively (κ=0.414). Conclusions Stereotactic body radiation therapy for stage I NSCLC is applicable to more tumors than sublobar resection, with ~30–35% of stereotactic body radiation therapy patients unable to undergo sublobar resection assessed by pretreatment diagnostic imaging based on technical grounds. This study illustrates that clinical trials comparing stereotactic body radiation therapy vs. sublobar resection are limited to only a subpopulation of patients with stage I NSCLC.
A comprehensive method to estimate power system stability constraint costs

Power systems are now being operated closer to their stability lim­ its. This occasionally requires that generation schedules should de­ viate from merit order operation to ensure an acceptable dynamic re­ sponse in die event of unforeseen plant outages. A cost is incurred by this rescheduling, termed s t a b i l i t y c o n s t r a i n t c o s t . In planning time scales it is useful to weigh this cost against costs of network devel­ opments. This paper describes software designed to estimate stability constraint costs, combining Monte Carlo simulations of network con­ ditions with a dynamic security assessor. A transient stability index is used to target those generation units which must be constrained.
Signaling Sepsis Scenario Development & Validation

Patient-based scenario-driven usability tests are routinely created for health information technology and clinical decision support evaluations. Due to low clinician awareness of sepsis, a study was undertaken to understand clinician performance and preference of different display types for sepsis clinical decision support through multi-centered usability testing. Patient-based clinical scenarios were created to mimic the environment in which providers would interact with clinical decision support. The data provided in the scenarios were drawn from real patient cases from two sepsis databases, including: demographics, visit/operational details, medical history (comorbidities, assessments, vital signs, laboratory values, clinician documentation), and patient disposition/outcomes. The purpose of this work is to inform electronic health record alert optimization and clinical practice workflow to support the effective and timely delivery of high quality sepsis care. This paper discusses the methodology, selection, and validation of patient-based cases used as the clinical scenarios in usability testing.
Assessment of Nutrition Knowledge of Childcare Providers Regarding the Implementation of the 2017 CACFP Meal Pattern Update

ABSTRACT Background: With the release of 2017 Child and Adult Care Food Program (CACFP) meal pattern, states need to determine knowledge gaps in order to develop targeted training materials and resources to aid childcare providers in achieving new regulations. Purpose: To assess the nutrition knowledge of childcare providers in regards to the implementation of the 2017 CACFP meal pattern. Methods: Convenience sampling, where CACFP participants (n = 398) completed a self-reported survey at the annual mandatory trainings across the state of Nebraska, was employed in this study. Descriptive statistics including frequencies, means, standard deviations, independent sample t tests, and chi-square tests were used to determine differences in nutrition knowledge by geographical location, for-profit and nonprofit, and program settings. Results: Data obtained from this study indicate that CACFP participants scored low on questions regarding yogurt (30%), juice (35%), breakfast cereal (37%), and whole grain (43%) questions. Data also show that there was no significant difference in levels of knowledge among characteristics and demographics of programs, types of facilities, and geographic locations. Translation to Health Education Practice: The present study results underscore the importance for continued professional development for CACFP participating childcare providers to implement the new CACFP meal pattern for child nutrition standards.
Methods and Mechanisms of DNA methylation in Development and Disease

Cytosine methylation (5mC) and hydroxymethylation (5hmC) play important roles in transcriptional regulation in post-mitotic neurons. Neuronal activity-induced gene activation in the adult brain is mediated through the Tet oxidation of 5mC to 5hmC, which is replaced with unmodified cytosine by the base excision repair pathway to complete the demethylation cycle. Little is known about the dynamics of how 5mC and 5hmC are established in adult neurons partially owing to the difficulty of studying neuron maturation ex vivo. Here, we leverage a recently developed 5mC genome-wide mapping technique and a motor neuron puromycin-selectable transgenic embryonic stem cell line to characterize the DNA methylome of motor neurons (MNs) as they differentiate and mature. We show that motor neuron differentiation and maturation is characterized by a loss of 5mC at genes critical for neuron function and by a global gain in 5hmC. These changes in cytosine modification result in attendant gene activation and show enrichment for known sequence motifs of motor neuron-specific transcription factors. Furthermore, single, double, and triple knockout experiments show that individual Tet proteins are not required for MN maturation and that Tet3 is sufficient for proper MN differentiation and maturation, and that MN can differentiate and form without the presence of any TET enzymes. Our study suggests that transcription factor-mediated active demethylation of 5mC through the 5hmC intermediate occurs in a temporal manner and that the TET proteins play redundant and compensatory roles in MN differentiation
Applications of Machine Learning in Cyber Security

Machine learning is one of the latest trends models and methods that can be implemented and the users can get good results. The growth of this area was developing day to day by almost all areas of applications and research. The other important area to be considered was the cyber security. Most of the common people also being trapped by these technologies and the people are losing their valuable data in some cases and in some other cases the people or losing their valuable money also. In some other cases, the people are losing their lives due to the sharing of their personal data to the public domain. Hence, people need to think of these problems in a different manner and also to solve these technical problems by using these advanced technologies. In the current article, some of the cyber security issues and threats that were being occurring in these days were highlighted and how the utilization of these machine learning techniques will be used to identify such threats and can be avoided or can be protected from these sorts of attacks.
AGE ESTIMATION USING SPECIFIC DOMAIN TRANSFER LEARNING

Nowadays, the engagement of deep neural networks in computer vision increases the ability to achieve higher accuracy in many learning tasks, such as face recognition and detection. However, the automatic estimation of human age is still considered as the most challenging facial task that demands extra efforts to obtain an accepted accuracy for real application. In this paper, we attempt to obtain a satisfied model that overcomes the overfitting problem, by fine-tuning CNN model which was pre-trained on face recognition task to estimate the real age. To make the model more robust, we evaluated the model for real age estimation on two types of datasets: on the constrained FG_NET dataset, we achieved 3.446 of MAE, while on the unconstrained UTKFace dataset, we achieved 4.867 of MAE. The experimental results of our approach outperform other state-of-the-art age estimation models on the benchmark datasets. We also fine-tuned the model for age group classification task on Adience dataset and our model achieved an accuracy of 61.4%.
MicroRNA-331-3p inhibits proliferation and metastasis of ovarian cancer by targeting RCC2

Introduction Epithelial ovarian carcinoma (EOC) is one of the most lethal gynecologic malignancies, with a poor 5-year survival rate. Numerous studies have shown that microRNAs participate in the malignant behavior of ovarian cancer cells by directly targeting multiple oncogenes or tumor suppressor genes. Material and methods Reverse transcription-PCR was used to determine the level of miR-331-3p in EOC. Cells proliferation was measured with the Cell Counting Kit-8. Cell mobility were measured by wound-healing assay. Cell migration and invasion were measured by transwell assay. Luciferase assays were used to demonstrate that RCC2 was a directed target of miR-331-3p in EOC. Western blots were used to measure the protein expression. Results We found that the expression of microRNA-331-3p (miR-331-3p) in ovarian cancer cell lines is reduced (p < 0.01), and an increase of expression of miR-331-3p in ovarian cancer cells significantly inhibits cell proliferation (p < 0.001). Transwell and wound-healing assays showed that miR-331-3p inhibits the cell motility of ovarian cancer cells (p < 0.001). Regulator of chromosome condensation 2 (RCC2) was predicted to be a novel target for miR-331-3p. Our luciferase activity assay confirmed that RCC2 is directly targeted by miR-331-3p. RCC2 was negatively regulated by miR-331-3p (p < 0.001), and overexpression of RCC2 could restore the malignant behaviors of ovarian cancer cells, which was suppressed by miR-331-3p. Conclusions These data indicate that miR-331-3p can inhibit proliferation, migration, and invasion of ovarian cancer cells via directly targeting RCC2. Our study provides potential therapeutic targets for the treatment of ovarian cancer.
Sequence-to-sequence Autoencoder based Korean Text Error Correction using Syllable-level Multi-hot Vector Representation

현재 다양한 모바일 메신저를 기반으로 사람간의 언어 소통이 이루어지고 있고, 최근에는 채팅봇 형태의 콜센 터 안내, 쇼핑 도우미, 고객상담 등 대화형 서비스가 가 속화 되고 있다. 음성인식 성능이 기대 수준에 이른다면 문자 기반의 서비스들은 음성 인터페이스로 그 기능을 확장 연계할 수 있을 것이다. 실생활에 사용 가능한 음성인식 시스템을 구성하기 위 해서는 음성언어용 언어모델과 이를 위한 구어체 표현이 반영된 충분한 학습 코퍼스가 필요하다. 온라인 게시판, 댓글, 채팅창은 사용자간의 대화가 자유롭게 이루어지는 공간이며, 그렇기 때문에 사용자가 작성한 텍스트(User Generated Text; 이하 UGT)에는 구어체적인 특성이 잘 나 타나 있다. 그러나 학습 데이터로 바로 사용하기에는 다 양한 텍스트 입력 오류 및 음성 표기로 변환하기 어려운 노이즈들이 많이 포함되어 있다. 표 1과 같이 다양한 한 글 입력 오류 유형과 다양한 측면에서의 오류 발생과 함 께 중복 노이즈가 발생할 수 있기 때문에 언어모델용 학 습 데이터로 활용하기 위한 추가 작업이 필요하다. 본 논문에서는 UGT에서 사용자에 의해 발생할 수 있는 오류를 ‘한글 입력 오류’라 정의한다. 이러한 오류는 미리 구축된 변환 사전을 활용하여 오류 문자열 패턴을 검색 기반으로 대체하는 방식으로 보정할 수도 있다. 하 지만 범용적으로 사용 가능한 사전 구축 작업이 현실적 으로 어려울 뿐만 아니라 도메인 별로 오류 분석을 통해 추가 사전을 미리 만들어야 하는 번거로움이 있다. 또한 UGT가 만들어내는 한글 오류는 그 패턴과 규칙도 계속 변화하기 때문에 오류를 보정하기 위한 모든 규칙을 찾 기 위해서는 많은 노력이 필요하다.
Augmented Visual SLAM for the Localisation of a Transportation Asset Management Survey Vehicle

In this paper we explore how visual simultaneous localisation and mapping (vSLAM) systems might estimate the pose (position and orientation) of a vehicle surveying highway assets, as part of a wider transport asset management (TAM) system. Such a system would reduce reliance on (or enhance estimation from) a GPS-enabled inertial measurement unit (IMU). Two problems with employing vSLAM systems on highway survey imagery are identified. Firstly, straight segments of the highway cause low parallax issues, and secondly the presence of other vehicles breaks the assumption that the camera moves within a static environment. To overcome these problems, an existing monocular vSLAM system is modified with two state-of-the-art deep neural network (DNN) tools. We show that the modified system provides an improved estimation of pose. In addition to TAM, our work has clear applications in autonomous vehicles, when there is only limited visual field sensing.
The validity of conceptual knowledge test on mechanics topic

This study aims to analyze the validity of conceptual knowledge test on mechanics topic. The research method used four stages that was Preliminary Research, Prototyping Stage, Summative Evaluation, Systematic Reflection and Documentation. This article report is focused on preliminary research and prototyping stage that is to arrange conceptual knowledge test and analyze validity of test by Content Validity Ratio (CVR) method. Based on the data analysis, it was concluded that the test was valid and feasible to use on students.
Pose-Guided Tracking-by-Detection: Robust Multi-Person Pose Tracking

Multi-person pose tracking task aims to estimate and track person keypoints in videos. Most of the previous methods follow the general track-by-detection strategy that ignores the consistent pose information during the whole framework. Thus, they often suffer from missing detections or inaccurate human association in challenging scenes with motion blur or person occlusion. To handle those problems, we propose a pose-guided tracking-by-detection framework that fuses pose information into both video human detection and human association procedures. In the video human detection stage, we adopt the pose-guided person location prediction exploiting the temporal information to make up missing detections. Technically, pose heatmaps are utilized to cope with the person-specific intra-class distractors. Furthermore, in the human association stage, we propose an appearance discriminative model based on the hierarchical pose-guided graph convolutional networks (PoseGCN). The PoseGCN-based model exploits human structural relations to boost person representation. Extensive experiments show the superiority of our method on the challenging pose tracking benchmark. Our proposed method ranks first on the PoseTrack leaderboard.11http://posetrack.net/leaderboard.php till the submission date (22-Aug-2019) of this paper. Our code has been publicly available at https://github.com/human-centric982/PGPT.