Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes

With the explosive growth of internet technology, there are very large amount of information on the web for the internet users. Users not only use that information but also provide opinions for decision making process. Sentiment analysis or opinion mining is one of text classification techniques that identify and extract opinion described in a piece of text. Our aims in this paper are to develop automatic sentiment analysis system for Myanmar news and to annotate sentiment news. Therefore, this system creates sentiment annotated corpus for Myanmar news. Feature extraction and selection are very important for sentiment analysis to get higher performance. N-grams, Countvectorizer, and TF-IDF are used for feature selection and feature extraction. In this system, Myanmar news sentiment analysis system is implemented by using K Nearest Neighbor (KNN) and Naïve Bayes machine learning algorithms.
An assessment of workload in a simulated submarine control room

The Command Team Experimental Testbed (ComTET) is a programme of work designed to study the operation of current and future submarine command teams. As part of this work a submarine control room simulator was designed and built for testing purposes. Baseline testing was conducted to establish current functionalities, with recommendations forming the basis for Manipulation One where some operators were co-located. During testing, 32 participants (four teams of eight individuals) were given general maritime, and role specific training. Each team completed a high and low demand version of three scenario types: return to periscope depth, inshore operations, and dived tracking. On completing each scenario, the workload of participants was assessed using an electronic version of the Bedford scale. Preliminary results suggest the workload of operators was affected by scenario demand and type. Results also suggest that the co-location of operators had a positive effect on the demand placed on them. The results are discussed, along with future analysis plans.
CALIBRATION OF ENSEMBLE SPREAD USING FORECAST SPECTRA

Estimating internal (atmospheric) error growth by observing the real atmosphere would be ideal, but the absence of good analogues prevents a robust estimate (Van Den Dool 1994) and we are forced to use models. An understanding of the second-moment statistics (spatial variance and its spectral decomposition) of a NWP model is one step toward full utilization of the model as a substitute for the real atmosphere, facilitating prediction and predictability experiments. Examples include observation-system simulation experiments for designing observing and data-assimilation systems, experiments to estimate the limits of predictability of phenomena or scales, and experiments to characterize internal and external (model) error growth. Ensemble forecasting in some capacity lies at the heart of much of this research, but quantitative results from ensemble forecasts often rely on the assumption that the forecast model reproduces the first and second moments of the climate of the real atmosphere. In this paper we explore the quantitative relationship between second-moment NWP model forecast statistics and ensemble spread, and the effect on resulting errorgrowth estimates. The inability of a model to produce realistic error growth in either phase or amplitude is referred to as a deficiency, and it is really one manifestation of model error. It is also different from a phase or amplitude error in the traditional sense, though it may be related. Following Leith (1974), we start by asserting that NWP forecasts with scale-dependent spatial variance (thus spectra) similar to the real atmosphere are necessary to comprise an ensemble intended for predictability studies. Forecast energy spectra describe forecast amplitude, and we will show that in the absence of other factors, an amplitude-deficient model will produce spreaddeficient ensembles. Further, knowledge of the spectral properties of a forecast model can explain the ensemble
Fiscal Policy and Stock Market Movement in the Philippines

The Philippine government has suffered greatly from fiscal imbalances for the past few decades. The largest fiscal deficit was recorded at PhP112 billion in 2017. Fiscal reforms led to a considerable decline of the national government debt from 52.4% of GDP in 2010 to 44.8% in 2015. The study aims to investigate the Philippine fiscal policy and its link to asset prices, as measured by the changes in the Philippine Stock Exchange Index (PSEI). Quarterly observations from 2001 to 2017 of monetary, fiscal, and economic variables were used on a vector error correction model (VECM) to observe their long-run relationship with the stock market index. The results indicate that policy rates, government revenue, inflation rates, and GDP influence stock prices positively, but foreign interest rates and government expenditures have a negative effect on the stock exchange in the long run. All of these are in accordance with a priori expectations except for the inflation rates. This study also confirms the existence of a long-run relationship between all of the variables and the PSEI. The empirical evidence nonetheless suggests that 28% of the deviations of the PSEI from its long-run equilibrium due to short-run shocks are corrected after a quarter. Hence, it will take about three to four quarters for the PSEI to go back to its equilibrium level.
Learning Energy-Based Models by Diffusion Recovery Likelihood

While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained by maximizing the recovery likelihood: the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. The recovery likelihood objective is more tractable than the marginal likelihood objective, since it only requires MCMC sampling from a relatively concentrated conditional distribution. Moreover, we show that this estimation method is theoretically consistent: it learns the correct conditional and marginal distributions at each noise level, given sufficient data. After training, synthesized images can be generated efficiently by a sampling process that initializes from a spherical Gaussian distribution and progressively samples the conditional distributions at decreasingly lower noise levels. Our method generates high fidelity samples on various image datasets. On unconditional CIFAR-10 our method achieves FID 9.60 and inception score 8.58, superior to the majority of GANs. Moreover, we demonstrate that unlike previous work on EBMs, our long-run MCMC samples from the conditional distributions do not diverge and still represent realistic images, allowing us to accurately estimate the normalized density of data even for high-dimensional datasets.
Isolation and Molecular Identification of Xylaria adscendens and Acremonium zeae from Exotic Vegetables in Aberdeen

In an experiment involving the isolation and molecular identification of fungi associated with exotic vegetables in an Aberdeen shop, Xylaria adscendens and Acremonium zeae were isolated and identified from scotch bonnet chilli (Capsicum chinense) and baby corn (Zea mays) imported from Uganda and Kenya respectvely. Isolates were identified using colony and morphological characters on Potato Dextrose Agar and PCR analysis. The DNA of the fungi were extracted using a QiagenDNeasy Plant Mini Kit and PCR products were purified with a QIAquick PCR Purification Kit after electrophoresis. PCR amplifications were run using the primer pair ITS1/ITS4. DNA sequences were compared to published sequences in GenBank using BLASTn. Xylaria adscendens and Acremonium zeae have both been reported as edophytes of many important crops plants with potential to serve as biocontrol agents against some pathogenic fungi. GSJ: Volume 7, Issue 7, July 2019 ISSN 2320-9186 608 GSJ© 2019 www.globalscientificjournal. com
Feature-based Summarizing of Hotel Customer Reviews

Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work to the main subtask of opinion summarization. The task of product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon how intense the opinion is for both positive and negative features. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.
Using In Vitro Co-Culture Models

We co-cultured Caco-2 and HT29-MTX to ensure a tunable intestinal model and study the 17 transport of two classes of nanoparticles. We exposed Caco-2/HT29-MTX of different seeding ratios, 18 cultured on Transwell® systems, to non-cytotoxic concentration levels (20 μg/mL) of Si/SiO2 19 quantum dots and iron oxide (α-Fe2O3) nanoparticles. Transepithelial electric resistance was 20 measured before and after exposure, and permeability was assessed via the paracellular marker 21 Lucifer Yellow. At regular intervals during the 3-hour transport study, samples were collected from 22 the basolateral compartments for detection and quantitative testing. Cell morphology 23 characterization was done by phalloidin-FITC/DAPI labelling, and Alcian Blue/eosin staining was 24 performed on insert cross-sections in order to compare the intestinal models and evaluate the 25 production of mucins. Morphological alterations of the Caco-2/HT29-MTX (7:3 ratio) co-cultures 26 were observed at the end of the transport study compared to the controls. The nanoparticle 27 suspensions tested did not diffuse across the intestinal model and were not detected in the receiving 28 compartments, due to their tendency to precipitate at the monolayer surface level and form visible 29 aggregates. These preliminary results indicate the need for further nanoparticle functionalization in 30 order to appropriately assess intestinal absorption in vitro. 31
Case Study of Energy Management Model in the Threshing System for the Production of White Rice

This work is a contribution in mathematical modeling through Matlab in the process of threshing for the production of white rice, a mode of energy management to achieve energy savings must be implemented with the purpose of reducing costs and CO2 emissions. The sample of production is taken again power consumption and modeled by a regression line called the base line. With the higher points of production (threshing) at lower consumption second line regression line reference is created and on the basis of the reference line a goal line is established as an objective for improvement with 95% confidence. Once the objective of saving power is established, E.F. M.A. is one of the most important of the methods used to determine whether or not the process is effective. The results of the research clearly show the importance of incorporating a good system of systematic control for the management of energy management in the mill industry, seeking an annual savings of approximately 60 million C.O. P.
Safety First! Expert Perspectives on Cultural Differences in the Use of Track Warning Systems

Network Rail, who maintains Britain's rail infrastructure, identified that UK track workers may have an increased incident rate associated with use of warning systems, compared to other countries. This study aimed to identify cultural factors that could influence the design, procurement and use of rail safety equipment. Semistructured interviews with six experts were conducted. Three of these were subject to a thematic analysis, using information from the other interviews and several industry documents for the purpose of verification. Six main themes were identified: "Organisation", "Mentality", "Training", "Kit", "Choice of warning system", and "Product acceptance process". Recommendations for improvements are proposed.
Usability Testing and Evaluation of Multimedia E-Learning Management System in Higher Education: Criteria for Evaluation

This paper discusses the criteria for usability testing and evaluation of Multimedia e-Learning Management Systems (MEMS). This was achieved through an in-depth analysis and synthesis of literature and presentation of results of a practical application using the University of Zimbabwe MEMS, Towards Student-Centred Integration of Multimedia ELearning, TSIME. Firstly, a critical review, analysis and synthesis of usability testing and evaluation of MEMS was done. That was followed by an in-depth synthesis of the learning theories as the structural basis of MEMS. Major criteria were drawn from MEMS usability, design aspects, institutional dimensions, and learning theories. The derived criteria were merged with the generic usability heuristics producing sixteen TSIME Usability Heuristics, TSIMEUH. Heuristic Evaluation (HE) method was used to test TSIME. The evaluation was carried out for two weeks using three expert evaluators. Twenty-eight usability problems were identified from the study, ten of which were classified as requiring high priority intervention while the rest needed moderate to minimal priority intervention and were solved. The major criteria that emanated from the study were under motivation, ethics and navigation attributes. The key findings indicated that criteria drawn can empower learning and solidify a good learning environment through the use of MEMS. The criteria also embrace a consolidated learning pattern that can be used in global pandemics such as the COVID-19 era by both instructors and learners. The research concluded that learning is embedded in the historical, social and material context and should be improved through interaction and feedback that incorporate the learning theories.
CUDA-based SeqSLAM for Real-Time Place Recognition

Vehicle localization is a fundamental issue in autonomous navigation that has been extensively studied by the Robotics community. An important paradigm for vehicle localization is based on visual place recognition which relies on learning a database, then consecutively trying to ﬁnd matchings between this database and the actual visual input. An increasing interest has been directed to visual place recognition in varying conditions like day and night cycles and seasonal changes. A major approach dealing with such challenges is Sequence SLAM (Se-qSLAM) based on matching a sequence of images to the database instead of a single image. This algorithm allows global pose recovery at the expense of a higher computational time. To solve this problem with a certain amount of speedup, we propose in this work, a CUDA-based solution for real-time place recognition with SeqSLAM. We design a mapping of SeqSLAM to CUDA architecture and we describe, in detail, our hardware-speciﬁc implementation considerations as well as the parallelization methods. Performance analysis against existing CPU implementation is also given, showing a speedup to six times faster than the CPU for common sized databases. More speedup could be obtained when dealing with bigger databases.
Xanthosoma sagittifolium is resistant to Meloidogyne spp. and controls Meloidogyne enterolobii by soil biofumigation

Abstract Meloidogyne is a relevant plant-parasitic nematode that causes enormous damage. It is very challenging to control, and there are not many chemicals available on the market for that. As an alternative method of nematode control, biofumigation is increasingly gaining space. This research aimed to study the reaction of Xanthosoma sagittifolium to Meloidogyne enterolobii, M. incognita, and M. javanica and soil biofumigation with X. sagittifolium leaves for M. enterolobii control. The reaction test was performed in the populations 0 (control), 333, 999, 3,000, 9,000, 27,000 eggs and eventual juveniles. X. sagittifolium did not host the Meloidogyne species studied, even in a high population. X. sagittifolium leaves incorporated in soil at concentrations 0 (control), 0.45, 0.9, 1.8, 3.6 g were also studied to control M. enterolobii, and they were able to reduce galls and eggs. The number of galls and egg masses was reduced to a concentration of 1.8 g. In the maximum concentration, the number of galls was less than 15 galls, and the eggs were also reduced to less than 200 eggs. As these macerates emitted nematicidal volatile organic compounds (VOCs) against M. enterolobii, it reduced the infectivity and reproduction of nematodes.
I/O Traffic Management on PC-Cluster based Cloud Storage System

Cloud storage system architecture and design plays a vital role in the cloud computing infrastructure. Cloud storage system provides users to efficient storage space with elasticity feature. One of the challenges of cloud storage server is difficult to balance the providing huge elastic capacity of storage and investment of expensive cost for it. In order to solve this issue, we propose the low cost PC cluster based storage system architecture which can be activated to store a large amount of data and provide cost-effective for cloud storage user and present FP-growth algorithm for metadata prefetching method and Least Recently Used (LRU) replacement policy for caching to improve performance by reducing response time. According to the experimental testing, the storage can be utilized more than 90% of storage space and can significantly reduce the average response time.
Element Method and Digital Image Correlation

Porous materials especially closed-cell metallic foams play important roles among novel materials because of their good characteristics e.g. high strength to weight ratio and crashworthiness. On the other hand, mechanical behavior determination and detailed characterization are essential in efficient manipulation and material tailoring. In the present research especial hybrid experimental-numerical approach is used for aluminum foam behavior determination as to the main goal, i.e. continuous deformation field measurement using digital image correlation (DIC) and finite element analysis (FEA) on porous specimen’s surface. To overcome the 3D modelling problem of closed-cell foams structure, we present the method based on CT-scan and digital optic microscope imaging combination. In the experimental part of the study, aluminum foams and proper specimens are manufactured, and then highresolution digital imaging and illumination setup are employed. Finally, the deformation field is obtained using DIC. On the other hand, measurement verification and DIC parameters optimization processes are conducted using ABAQUS 2019 with comprehensive mesh independency study and response surface methodology (RSM) respectively as major research achievement. Finally, correlation equations based on high regression models are obtained. Using detailed geometrical micro-model and optimal DIC parameters yields to good numerical-experimental accordance. The novel approach of combined CT and digital microscope imaging instead of industrial micro-CT lowered imaging costs while yielded to accurate numerical results. doi: 10.5829/ije.2020. 33.10a. 25
Sample drop detection for asynchronous devices distributed in space

In many applications of multi-microphone multi-device processing, the synchronization among different input channels can be affected by the lack of a common clock and isolated drops of samples. In this work, we address the issue of sample drop detection in the context of a conversational speech scenario, recorded by a set of microphones distributed in space. The goal is to design a neural-based model that given a short window in the time domain, detects whether one or more devices have been subjected to a sample drop event. The candidate time windows are selected from a set of large time intervals, possibly including a sample drop, and by using a preprocessing step. The latter is based on the application of normalized cross-correlation between signals acquired by different devices. The architecture of the neural network relies on a CNN-LSTM encoder, followed by multi-head attention. The experiments are conducted using both artificial and real data. Our proposed approach obtained F1 score of 88% on an evaluation set extracted from the CHiME-5 corpus. A comparable performance was found in a larger set of experiments conducted on a set of multi-channel artificial scenes.
An Interrelation-based Approach to Aspect Extraction from Customer Reviews

Aspect extraction plays a key role in aspect-based sentiment analysis. Without knowing them, the target of sentiments expressed by customers cannot be known. If the target of the sentiments is not known, the sentiments expressed in a review are of limited use. Aspects and sentiment words are related to one another. Aspect extraction affects the performance of sentiment word extraction and also sentiment analysis. This study concentrates on aspects extraction from customer product reviews. This paper proposes an interrelation-based approach which considers the strength of interrelations between aspects and sentiment words to solve the problem of aspect extraction. The proposed approach takes into account the frequency of aspects, the weight of aspects and the weight of sentiment words associated with the aspects. Due to considering the interrelations between the aspects and sentiment words, the proposed method is expected to extract the relevant product aspects effectively and solve the major bottleneck of domain dependency.
An exploratory focus group study of factors influencing helicopter pilots' Non-Technical Skills

Non-technical skills are the interpersonal and cognitive skills that promote effective performance alongside a pra2ctitioner’s technical abilities in their working environment. Whilst helicopter pilots are trained and assessed in non-technical skills, there is a lack of encompassing research assessing the factors which influence these skills as a whole. The current study aimed to report and compare factors which influenced the utilisation of non-technical skills across two pilot groups; search and rescue and offshore transport. Fifteen semi-structured focus group sessions were undertaken (n=8 offshore transport, n=7 search and rescue) where pilots were asked a series of open-ended questions centred around factors that influenced their non-technical skill utilisation. Focus group analysis was conducted by way of inductive thematic analysis to identify and compare factors. Workload, stress, individual influences, crew interaction/composition, environmental influences, changes and the status of other non-technical skills were identified as overarching factors which influenced the utilisation of pilots’ non-technical skills. Each factor consisted of a range of specific elements. While there was no variation between pilots on a factor level (e.g. workload), there was significant variation on the elemental level (e.g. too high a workload for offshore transport vs. not having all relevant mission information for search and rescue). The results indicate that while there are similarities in the overarching factors that affect non-technical skill utilisation, variations exist on the elemental level. Non-technical skills training, therefore, should be adapted for specific mission types and focused around role specific elements.