Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method

The rapid development of information technologies enables successful results in computer-aided studies. This has led researchers to investigate the usability of technologies such as computer and software supported systems, machine learning, and artificial intelligence in many studies. One of these areas is health. For example, in order not to risk the condition of the mother and baby, in some cases, it is very important to correctly determine the times when the cesarean operation, which is mandatory, is mandatory. In this context, in order to make a faster and more accurate decision, it is very important to determine which attributes and how important the level is in making obligatory cesarean. In this study, to determine whether or not caesarean is necessary in the literature, the importance level of the five criteria taken into consideration has been determined and an attribute determination has been carried out and then a classification has been made. The data set used belongs to 80 pregnant women with 6 attributes. Although the same data set was previously classified with different methods, no study was found on determining the significance levels of the attributes and using artificial neural networks as a method. For this reason, in this study, the feature was determined using an adaptive nerve-fuzzy classifier and classified using artificial neural networks. When the results are examined, it is concluded that the importance levels of the attributes are different. Although the values such as accuracy, Sensitivity, and Specificity calculated to evaluate the classification results were found to be quite high for the training set, it was observed that the desired success was not achieved in the test data. While this result is promising, it also reveals the need to increase the learning performed with larger data sets.
Artificial pinning centres in YBCO thin films induced by substrate decoration with gas-phase-prepared Y2O3 nanoparticles

Y2O3 nanoparticles prepared by an inert gas phase condensation process were used to introduce artificial pinning centres in YBa2Cu3O7−δ thin films. The areal density of the particles was varied between 120 and 4200 particles µm−2 without changing the mean particle diameter of approximately 9 nm. Y2O3 particles were deposited on TiO2 terminated SrTiO3 (100) single-crystal substrates with areal densities up to 1654 particles µm−2 and subsequently covered with YBa2Cu3O7−δ by off-axis pulsed laser deposition (PLD). The areal density of the room temperature deposited particles is not changed by the substrate heating during PLD although their height on the substrate decreases. An influence on Jc is demonstrated for particle densities above 1588 particles µm−2, indicating that the substrate decoration with Y2O3 nanoparticles from the gas phase affects the formation of artificial pinning centres in the YBa2Cu3O7−δ films and can be applied to further study of the effect of particle size and areal density on defect formation in YBa2Cu3O7−δ.
Automating and utilizing equal-distribution data classification

Data classification, i.e., organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conventionally, data are classified by dividing the data range into intervals and assigning the same symbol or colour to all data falling within an interval. For instance, the intervals may be of the same length or may include the same number of data items. We propose a method for defining intervals so that some quantity represented by values of another attribute is equally distributed among the classes. An example is dividing a set of geographic regions into classes according to the values of the attribute “Birth rate” so that the classes have approximately equal total values of the attribute “Population” or “Arable land area”. This kind of classification supports exploratory analysis of relationships between the attribute used for the classification and the distribution of the phenomenon whose quantity is represented by the additional attribute. The approach may be especially useful when the distribution of the phenomenon is very unequal, with many data items having zero or low quantities and quite a few items having larger quantities. With such a distribution, standard statistical analysis of the relationships may be problematic. We demonstrate the potential of the approach by analysing data referring to a set of spatially distributed people (patients) in relationship to characteristics of the areas in which the people live.
Activation of naturally occurring LCAT mutants by a novel activator compound

Centro E. Grossi Paoletti, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milano, Italy: CP, AO, MT, AS, SS and LC Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milano, Italy: TL, IE Specialty Medicine Research Laboratories I, Daiichi Sankyo Co., Ltd., Tokyo, Japan: KK and NK Medical Affairs Planning Department, Daiichi Sankyo Co., Ltd., Tokyo, Japan: KY
The Parameterization of Sub-Grid Scale Processes in Climate Models

Three methods of increasing sub-grid scale heterogeneity in the representation of the land surface in AGCMs are described: a method of spatial aggregation of parameter data to AGCM resolutions which retains the original variability in the data in subsequent climate simulations; a parameterization of sub-grid scale precipitation, interception and runoff; and an attempt at a model of sub-grid scale lakes for climate models. The relative sensitivity of the land surface to these modifications is discussed. It is shown that the aggregation of data is likely to improve simulations while incorporating the precipitation or lake parameterizations will improve the level of realism in the land surface models, but may not improve the climate simulations.
Leveraging Quadratic Spherical Mutual Information Hashing for Fast Image Retrieval

Several deep supervised hashing techniques have been proposed to allow for querying large image databases. However, it is often overlooked that the process of information retrieval can be modeled using information-theoretic metrics, leading to optimizing various proxies for the problem at hand instead. Contrary to this, we propose a deep supervised hashing algorithm that optimizes the learned codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of large-scale hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI), that is inspired by QMI, but leads to significant better retrieval precision. Indeed, the effectiveness of the proposed method is demonstrated under several different scenarios, using different datasets and network architectures, outperforming existing deep supervised image hashing techniques.
Does Gender Moderate the Behaviour of Sri Lankan State University Lecturers in Using Virtual Learning Environment (VLE)

: This research paper sets out to investigate the factors influence the adoption of VLE by the academicians of Sri Lankan national universities. It is intended to accomplish this by using the Technology Acceptance Model together with a few empirical inputs as extension. Questionnaire was used collect data from 357 academics working in 15 national universities that are under the purview of the University Grants Commission. A systematic, proportionate, stratified sampling technique was applied to represent all universities and various categories of staff. Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) version 23 were used to analyse the data. Through Structural Equation Modelling, the model fit was tested and the associations between variables were examined through path analysis. This study‟s results show that perceived usefulness, subjective norm and computer self-efficacy have positive associations with behavioural intention whilst perceived ease of use and university support did not have associations. Behavioural intention is positively associated with actual usage. These findings suggest that usefulness of system, computer self-efficacy of academics, and the normative factor influence the actual usage of the VLE. Gender moderates the relationship between behavioural intention and perceived ease of use, subjective norm and computer self-efficacy.
Toward Decentralized and Collaborative Deep Learning Inference for Intelligent IoT Devices

Deep learning technologies are empowering IoT devices with an increasing number of intelligent services. However, the contradiction between resource-constrained IoT devices and intensive computing makes it common to transfer data to the cloud center for executing all DNN inference, or dynamically allocate DNN computations between IoT devices and the cloud center. Existing approaches perform a strong dependence on the cloud center, and require the support of a reliable and stable network. Thus, it may directly cause unreliable or even unavailable service in extreme or unstable environments. We propose DeColla, a decentralized and collaborative deep learning inference system for IoT devices, which completely migrates DNN computations from the cloud center to the IoT device side, relying on the collaborative mechanism to accelerate the DNN inference that is difficult for an individual IoT device to accomplish. DeColla uses a parallel acceleration strategy via a DRL-based adaptive allocation for collaborative inference, which aims to improve inference efficiency and robustness. To illustrate the advantages and robustness of DeColla, we built a testbed and employ DeColla to evaluate MobileNet DNN network trained on the ImageNet dataset, and also recognize the object for a mobile web AR application and conduct extensive experiments to analyze the latency, resource usage, and robustness against existing methods. Numerical results show that DeColla outperforms other methods in terms of latency and resource usage, which can especially reduce at least 2.5 times latency than the hierarchical inference method when the collaboration is interrupted abnormally.
Practical Guidelines on the Required Sample Size in a Two Group Multivariate Mean Comparison

This paper concerns the sample size required for two group multivariate mean comparison. Its main purpose is to provide guidelines on the sample size that is required (a) for sufficient power of the T test, and (b) for the sample means to be reasonably close to the population values. To facilitate the choice of the expected difference the well-known indices for effect size by Cohen (1977) and Stevens (1980) are expressed in terms of the common language effect size measure proposed by McGraw and Wong (1992). Cohen's effect size meas¬ ures was used to simplify the relation between the precision of the estimate and sample size. Examples are given. Faculteitder Psychologic, Vakgroep Psychonomie, De VrijeUniversiteit, DeBoelelaanllll/1115, 1081 HV, Amsterdam. E-mail: TM.Bechger@psy.vu. nl
Data Reduction Algorithm for Optical Wide Field Patrol(OWL)

OWL(Optical Wide-field Patrol) has a detector system which has the chopper which consist of 4 blades in front of the CCD camera to acquire efficiently the position and time information of moving objects such as artificial satellites. Using this system, it is possible to get more position data by splitting the streaks of the moving object into many pieces with fast rotating blades during tracking. At the same time, the time data of the rotating chopper can be acquired by the time tagger connected to the photo diode. In order to derive the orbits of the targets, we need a sequential data reduction procedure including the calculation of WCS(World Coordinate System) solution to transform the positions into celestial coordinate systems, and the combination of the time data from the time tagger and the position data. We present such a data reduction procedure and the preliminary results after applying this procedure to the observation images
Solid-state fluorescence enhancement of organic dyes by photonic crystals

Photonic crystal (PC) films were fabricated by self-assembly of colloidal crystals using a vertical deposition method at invariant temperature and humidity. Organic dyes, whose emission wavelengths overlap the photonic stopbands of selected PCs, were deposited on the surface of PC films by thermal evaporation under vacuum. The fluorescence of organic dyes deposited on PCs is obviously enhanced in comparison with that on aluminium films and glass surfaces. There were 41-fold and 20-fold fluorescence enhancements when Rhodamine B base was deposited on the surface of a yellow PC film, compared with that on glass surface and aluminium film respectively, where PCs were utilized as a Bragg reflection mirror. The results show that the PCs are more effective and selective reflection mirrors than aluminium, and have potential applications in optoelectronic and lighting devices.
Molecular characterization of Dendrobium ‘Earsakul’ mutants from in vitro selection for black rot resistance

In vitro chemical mutagenesis of Dendrobium ‘Earsakul’ protocorm-like bodies (PLBs) followed by in vitro selection with Phytophthora palmivora culture filtrate (CF) generated several putative mutants potentially resistant to black rot. The objectives of this study were to evaluate the black rot resistance levels of these putative mutants, to estimate the genetic variability among them and non-mutagenized controls using inter-simple sequence repeat (ISSR) analysis, and to identify candidate markers with significant associations to black rot resistance. When 3 non-mutagenized control lines and 8 putative mutants, derived from ethyl methane sulfonate (EMS) or sodium azide (NaN3) induced mutation, followed by in vitro selection were evaluated for black rot resistance using detached leaf assay with a virulent isolate NK-53-9, it was found that all controls were susceptible to black rot, but the resistance levels of putative mutants varied. Four of eight putative mutants were moderately resistant or resistant to the disease, suggesting their usefulness in the breeding program. ISSR analysis of these controls and putative mutants with 7 ISSR primers yielded 7 to 29 reproducible bands per primer, ranging in size from 170 to 2100 bp. A total of 114 amplified ISSR fragments were obtained, 53 of which were polymorphic (46.5%). All controls have the same DNA patterns, while all 8 putative mutants showed altered genetic profiles compared to controls and were identified as mutants. The mutant SUT13E18-A appears to have distinct genetic profile compared to others as well as high level of resistance to black rot. Moreover, five ISSR markers significantly associated with black rot resistance were identified. These results suggest that ISSR analysis is efficient for mutant identification and characterization, and in vitro chemical mutagenesis followed by in vitro selection with P. palmivora CF provides a useful tool for future improvement of black rot resistant Dendrobium.
Fluorocarbon high‐density plasmas. II. Silicon dioxide and silicon etching using CF4 and CHF3

We report a study of the application of CF4 and CHF3 electron cyclotron resonance (ECR) discharges to selective etching of SiO2 over Si. Due to significant fluorocarbon film deposition for plasma operation without rf sample bias in the pressure range below 10 mTorr, rf biasing is required for etching of SiO2 and Si. The rf threshold voltage for etching is 55 V for CHF3 and 35 V for CF4 at a pressure of 1 mTorr. At 100 V rf bias, silicon dioxide etch rates were greater than ≂600 nm/min in CF4 and 450 nm/min for 1000 W plasmas at 1 mTorr pressure. A plot of the oxide etch rate vs rf bias exhibits a fluorocarbon film suppression regime at low rf voltages and an oxide sputtering regime at higher rf voltages. In the fluorocarbon suppression regime, the etch rate is primarily determined by fluorocarbon deposition which results in a thin fluorocarbon film being present on the SiO2 surface during steady‐state etching. In the oxide sputtering regime, the oxide etch rate increases linearly with the ion current to t.
The Effect of Service Quality and Customer Satisfaction on Internet Banking in Nationalised Banks

Now a day internet is the main channel of electronic banking . E-banking also called as internet banking, allows the customers to use all the banking services from a computer that has internet access. The customer can perform financial transactions on a secure website operated by the bank. Online banking offers features such as bank statements, loan applications, funds transfer, e-bill payments, and account aggregation allows customers to monitor all their accounts in one place. This paper is investigate the relationship between service quality and customer satisfaction on internet banking in nationalized banks with reference to nationalized Banks, Tirunelveli District, Tamilnad , India. This study conducted in rural areas and 245 samples were used to analyse the data. Descriptive, multiple regression and correlation statistical tool applied to know most predict variables and relationship between variables in service quality determinants. It is found that responsiveness and reliability are the most predictable variables on customer satisfaction and tangibility and assurance highly influence customer satisfaction among other service quality variables.
Isolation of lupeol from Grewia lasiocarpa stem bark: Antibacterial, antioxidant, and cytotoxicity activities

Akwu N, Naidoo Y, Singh M, Thimmegowda SC, Nundkumar N, Lin J. 2020. Isolation of lupeol from Grewia lasiocarpa stem bark: Antibacterial, antioxidant, and cytotoxicity activities. Biodiversitas 21: 5684-5691. Lupeol a pentacyclic triterpenoid, was extracted from the stem bark of Grewia lasiocarpa. Stem bark was macerated with chloroform (CHCl3) at 25 ± 2°C, and then purified using a silica gel column. The mobile phase was hexane (100%) and hexane: ethyl acetate (80:20) as the mobile phase. The structure of lupeol was identified by HNMR, and C NMR spectral data, compared with reported data. The antibacterial, antioxidant and cytotoxicity activities of lupeol were evaluated using agar-well diffusion against six clinic bacteria isolates, namely Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), Klebsiella pneumoniae (ATCC 314588), and Salmonella typhimurium (ATCC 14026) Gram-positive bacteria: Staphylococcus aureus (ATCC 25923), methicillin-resistant Staphylococcus aureus (MRSA) (ATCC BAA-1683). The antioxidants assays were performed using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP). Cytotoxicity activity was carried out by MTT (3-(4,5-dimethylthiazol)-2,5-diphenyl tetrazolium bromide) assay was carried out against HEK293 (human embryonic kidney), HeLa (cervical carcinoma), and MCF-7 (breast adenocarcinoma) cell lines. The concentration range of lupeol was 240-15 μg/mL. Lupeol at the concentration of 240 and 120 μg/mL have no antibacterial activity against Methicillin-resistant Staphylococcus aureus (MRSA) (ATCC BAA-1683). The antioxidant activity was dose-dependent, while cytotoxic activities were observed in HEK293, HeLa, and MCF-7 cells. This study was the first report on the extraction, isolation, and biological activities of lupeol, from the stem bark of G. lasiocarpa.
Learning task-agnostic representation via toddler-inspired learning

One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e.g., supervised learning), is that they perform well on labeled datasets but cannot deduce knowledge on their own. To tackle this problem, we derive inspiration from a highly intentional learning system via action: the toddler. Inspired by the toddler's learning procedure, we design an interactive agent that can learn and store task-agnostic visual representation while exploring and interacting with objects in the virtual environment. Experimental results show that such obtained representation was expandable to various vision tasks such as image classification, object localization, and distance estimation tasks. In specific, the proposed model achieved 100%, 75.1% accuracy and 1.62% relative error, respectively, which is noticeably better than autoencoder-based model (99.7%, 66.1%, 1.95%), and also comparable with those of supervised models (100%, 87.3%, 0.71%).
An Integrated Localisation, Motion Planning and Obstacle Avoidance Algorithm in Belief Space

As robots are being increasingly used in close proximity to humans and objects, it is imperative that robots operate safely and efficiently under real-world conditions. Yet, the environment is seldom known perfectly. Noisy sensors and actuation errors compound to the errors introduced while estimating features of the environment. We present a novel approach (1) to incorporate these uncertainties for robot state estimation and (2) to compute the probability of collision pertaining to the estimated robot configurations. The expression for collision probability is obtained as an infinite series and we prove its convergence. An upper bound for the truncation error is also derived and the number of terms required is demonstrated by analyzing the convergence for different robot and obstacle configurations. We evaluate our approach using two simulation domains which use a roadmap-based strategy to synthesize trajectories that satisfy collision probability bounds.
Transformer-Based Models for Question Answering on COVID19

In response to the Kaggle's COVID-19 Open Research Dataset (CORD-19) challenge, we have proposed three transformer-based question-answering systems using BERT, ALBERT, and T5 models. Since the CORD-19 dataset is unlabeled, we have evaluated the question-answering models' performance on two labeled questions answers datasets \textemdash CovidQA and CovidGQA. The BERT-based QA system achieved the highest F1 score (26.32), while the ALBERT-based QA system achieved the highest Exact Match (13.04). However, numerous challenges are associated with developing high-performance question-answering systems for the ongoing COVID-19 pandemic and future pandemics. At the end of this paper, we discuss these challenges and suggest potential solutions to address them.
Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks

We present a bulk data collection service, Harvest, for energy constrained wireless sensor nodes. To increase spatial reuse and thereby decrease latency, Harvest performs concurrent, pipelined exfiltration from multiple nodes to a base station. To this end, it uses a distance-k coloring of the nodes, notably with a constant number of colors, which yields a TDMA schedule whereby nodes can communicate concurrently with low packet losses due to collision. This coloring is based on a randomized CSMA approach which does not exploit location knowledge. Given a bounded degree of the network, each node waits only O$(1)$ time to obtain a unique color among its distance-k neighbors, in contrast to the traditional deterministic distributed distance-k vertex coloring wherein each node waits O$(\Delta^{2})$ time to obtain a color. Harvest offers the option of limiting memory use to only a small constant number of bytes or of improving latency with increased memory use; it can be used with or without additional mechanisms for reliability of message forwarding. We experimentally evaluate the performance of Harvest using 51 motes in the Kansei testbed. We also provide theoretical as well as TOSSIM-based comparison of Harvest with Straw, an extant data collection service implemented for TinyOS platforms that use one-node at a time exfiltration. For networks with more than 3-hops, Harvest reduces the latency by at least 33% as compared to that of Straw.