Miracle-dz/UniFAR-FTUs huggingface dataset:
Considering Interaction Types for Geometric Primitive Matching

Approaches to utilize everyday objects from a user’s surrounding to provide passive haptic feedback suffer if the mismatch between two matched objects is too large, or the number of objects in the virtual scene exceeds the number of available physical counterparts. By automatically subdividing the virtual as well as the physical objects into geometric primitives and computing a match between them, these drawbacks can be softened. A single physical proxy can easily provide haptic feedback for different virtual objects by that, however, restricting the area on which users can interact to the underlying primitive. Depending on the size of these primitives, the user interactions are consequently restricted, too, e.g. to touching or pinching. Hence, this position paper proposes to already consider interactions during the matching process, e.g. to optimize the primitive matching towards a specific interaction type, or alternatively, to recommend an interaction type for a computed matching.
BLUE ECONOMY BEING A PART OF GREEN ECONOMY IN BLUE WORLD AS AN ALTERNATIVE OR DIVERSIFIED SUSTAINABLE LIVELIHOOD STRATEGY FOR THE DEVELOPMENT OF COASTAL RURAL REGIONS IN WEST BENGAL

The major part of coastal areas of West Bengal is rural in nature. Eastern part of the coast in the South 24 Parganas is mostly uninhabited due to the presence of dense mangrove forest. There is only one township (Haldia) located on the coast of West Bengal. The major activities of the coastal communities are rural in nature. These activities include agriculture, fishing, collection of minor forest produce in Sundarban, dairy and poultry development activities, small scale retail trading and service sector activities. In and around Haldia, many members of the coastal population are employed in the organized industries and in various trading activities or service oriented professions. In and around Digha, the local people are engaged in various professions related to tourism inclusive of the transport sector employment. Self-employed rural artisans of various handicraft products are a small group having a difficult livelihood (ICZM Project, 2012). Therefore, an integrated, multidimensional and holistic approach to poverty eradication efforts is crucial to preserve and enhance the livelihoods of these rural poor. Thus a study is undertaken in coastal rural areas of West Bengal to explore the feasibility of blue economy being a part of green economy in blue world for rural development as a livelihood strategy in order to provide opportunities for improvement of living condition in a sustained way. The green economy in blue world or the blue economy is the integration of water based economy including inland water body and ocean economy development with the principles of social inclusion, environmental sustainability and innovative, dynamic business models.
Spontaneous dimerization and moment formation in the Hida model of the spin-1 kagome antiferromagnet

The Hida model, defined on a honeycomb lattice, is a spin-1/2 Heisenberg model of aniferromagnetic hexagons (with nearest-neighbor interaction, ${J}_{A}g0)$ coupled via ferromagnetic bonds (with exchange interaction, ${J}_{F}l0)$. It applies to the spin-gapped organic materials, $\mathrm{m}\text{\ensuremath{-}}\mathrm{MPYNN}\ifmmode\cdot\else\textperiodcentered\fi{}X$ (for $X=\mathrm{I},{\mathrm{BF}}_{4},{\mathrm{ClO}}_{4})$, and for $|{J}_{F}|\ensuremath{\gg}{J}_{A}$, it reduces to the spin-1 kagome Heisenberg antiferromagnet (KHA). Motivated by the recent finding of the trimerized singlet (TS) ground state for spin-1 KHA, we investigate the evolution of the ground state of the Hida model from weak to strong ${J}_{F}/{J}_{A}$ using mean-field triplon analysis and Schwinger boson mean-field theory. Our triplon analysis of the Hida model shows that its uniform hexagonal singlet (HS) ground state (for weak ${J}_{F}/{J}_{A})$ gives way to the dimerized hexagonal singlet (D-HS) ground state for $|{J}_{F}|/{J}_{A}\ensuremath{\gtrsim}1.26$ (which for strong ${J}_{F}/{J}_{A}$ approaches the TS state). From the Schwinger boson calculations, we find that the evolution from the uniform HS phase for spin-1/2 Hida model to the TS phase for spin-1 KHA happens through two quantum phase transitions: (1) the spontaneous dimerization transition at ${J}_{F}/{J}_{A}\ensuremath{\sim}\ensuremath{-}0.28$ from the uniform HS to D-HS phase and (2) the moment formation transition at ${J}_{F}/{J}_{A}\ensuremath{\sim}\ensuremath{-}1.46$, across which the pair of spin-1/2's on every FM bond begins to appear as a bound moment that tends to spin-1 for large negative ${J}_{F}$'s. The TS ground state of spin-1 KHA is thus adiabatically connected to the D-HS ground state of the Hida model. Our calculations imply that the $\mathrm{m}\text{\ensuremath{-}}\mathrm{MPYNN}\ifmmode\cdot\else\textperiodcentered\fi{}X$ salts realize the D-HS phase at low temperatures, which can be ascertained through neutron diffraction.
Music Style Classification Algorithm Based on Music Feature Extraction and Deep Neural Network

The music style classification technology can add style tags to music based on the content. When it comes to researching and implementing aspects like efficient organization, recruitment, and music resource recommendations, it is critical. Traditional music style classification methods use a wide range of acoustic characteristics. The design of characteristics necessitates musical knowledge and the characteristics of various classification tasks are not always consistent. The rapid development of neural networks and big data technology has provided a new way to better solve the problem of music-style classification. This paper proposes a novel method based on music extraction and deep neural networks to address the problem of low accuracy in traditional methods. The music style classification algorithm extracts two types of features as classification characteristics for music styles: timbre and melody features. Because the classification method based on a convolutional neural network ignores the audio’s timing. As a result, we proposed a music classification module based on the one-dimensional convolution of a recurring neuronal network, which we combined with single-dimensional convolution and a two-way, recurrent neural network. To better represent the music style properties, different weights are applied to the output. The GTZAN data set was also subjected to comparison and ablation experiments. The test results outperformed a number of other well-known methods, and the rating performance was competitive.
Sociodemographic profiles and predictors of news consumers

Introduction: This research article examines the sociodemographic profile of Spanish news consumers in the last decade. Methods: The study is based on ten surveys conducted by the Centre for Sociological Research (CIS, according to its initials in Spanish) between 2006 and 2017, on samples ranging from 2,455 to 3,191 participants, to determine whether sociodemographic profiles of news consumers are different across media platforms and whether they can efficiently predict news consumption. Results: It is concluded that the Internet has increased the consumption of print and digital newspapers. News consumption in radio and television is so widespread that the selected sociodemographic variables do not allow us to detect noticeable differences. However, newspaper readers are predominantly male, urban, middle-aged, middle and upper class, and highly educated. Internet news consumers basically match the previous profile, except in terms of age, as they are younger. In the case of newspaper and Internet news, the variables that predict their consumption more efficiently are education level, female sex (negative correlation) and social class (positive correlation). Finally, the correlation coefficients between news consumption in all three media platforms are significant, but not very high.
End User Satisfaction for Location Health Service Application with Analysis of Task Technology Fit

There are several types of health services that provide information about health care facilities, such as pharmacies, health centers, clinics, and hospitals. Application of health service facilities location is used to facilitate users in reaching the nearest health service facility. The application of the health care facilities location has not been optimally used by the user so often. The advantage of analyzing the system is to determine its direct and indirect effect on the end-user. This research analyzes task technology fit (TTF) of application for the location of health service facilities based on measures of end-user satisfaction and knowledge management system (KMS). The research began with an exploratory study through interviews with users of health service applications. With the results of interviews, the research hypothesis model was built to integrate health service applications with the task technology fit model based on end-user satisfaction. The results obtained from this study are the impact of the performance of a good application system can increase end-user satisfaction in optimizing all the modules that exist in the application. The intended system performance is the quality of information presented by the application including the location of the health service facility and the accuracy of information needed by the end which affects the compatibility of the health service facility application which significantly increase the end-user satisfaction, and this will automatically affect the TTF performance for the better. This needs to be responded to so that the application continues to be updated in real-time to continue to provide information about the application in accordance with the development and needs of end-users. This linkage shows that the role of task technology fit has a good impact on system development that affects system relationships and end-user satisfaction in applications.
Role of Organic Food Wastes on Soil Fertility , Growth and Yield of Stevia Crop

Stevia plant (Stevia rebaudiana Bertoni) is an herbaceous and perennial plant and native plant to Paraguay is a member of family Asteraceae. Steviosides, a white crystalline compound isolated from stevia is 300 times sweeter than sugar the two main glycosides are stevioside 10% and rebaudioside A 4 % of the dry weight of the leaves (Dzyuba 1998 and Yadav et al. 2010). The chemical compound obtained from stevia is considered to be the best alternative source of sugar especially for diabetes patients. So, the expanding in planting stevia will helping in decrease the gap between the production and consumption in the sugar in Egypt as well as used alternative source of sugar especially for diabetes patients (Allam et al., 2001).
The Effect of Long-Term Storage on Mycobacterium bovis

Abstract It was established that when stored for many years (10–13 years) in low-temperature conditions (3°C), without sub-culture on a nutrient medium, Mycobacterium bovis grew as visible colonies along the line of inoculation. However, due to long-term storage in conditions of low temperature (3°C) morphology of mycobacteria differed significantly from initial cultures formed by rod-shaped bacteria. Some of them became pigment-forming and smooth on the surface. Unlike the initial strain of mycobacteria, a perennial bacteria stored under hard conditions did not cause the death of guinea pigs or their sensitization to a purified protein derivative for mammals. Morphological forms of the perennial mycobacteria had the following changes: pigment forming, L-forms of the vesicular type, non-acid-fast thread-like (filamentous) bacillary forms, and elementary bodies when compared to the initial strain. There were also some genetic changes in the target DNA due to the long-term storage of M. bovis. It may indicate a mutation in the pathogen’s DNA. These mycobacteria had altered biochemical activity during storage. The number of passages on the solid nutrient medium did not affect their fermentative activity. However, the low cultivation temperature increases mycobacterial catalase activity and the ability to hydrolyze Tween-80.
65P30 Numerical bifurcation problems 65K10 Numerical optimization and variational techniques 37M20 Computational methods for bifurcation problems in dynamical systems 37G10 Bifurcations of singular points in dynamical systems

Nested-layer particle swarm optimization (NLPSO) detects bifurcation parameters in discretetime dynamical systems. Previous studies have proven the effectiveness of NLPSO for period-doubling bifurcations, but not for other bifurcation phenomena. This paper demonstrates that NLPSO can effectively detect saddle-node bifurcations. Problems in detecting saddle-node bifurcation parameters by conventional NLPSO are clarified, and are solved by imposing a simple condition on the NLPSO objective function. Under this conditional objective function, the NLPSO accurately detected both saddle-node and period-doubling bifurcation parameters regardless of their stability, without requiring careful initialization, exact calculations or Lyapunov exponents.
New Coronavirus ( 2019-nCoV ) : Analysis of the magnitude in the first two months of epidemic

| Objective: To analyze the magnitude of the New Coronavirus (COVID-19) in the State of São Paulo (SSP) in the first two months of the epidemic after the confirmation of the first case. Method: Ecological, descriptive study, considering the confirmed cases of COVID-19 captured by the SSP Epidemiological Surveillance Center for the period from February 26 to April 26, 2020. Result: It was found that the municipality of São Paulo presented a greater number of cases (13,989) and deaths (1,172). However, it was not the municipality that presented the highest magnitude for health indicators of COVID-19 incidence rate, mortality and lethality. Conclusion: Um explanation for such results is the lack of testing of COVID-19 in the municipalities studied. The potential of the epidemic is still of particular concern, given the large number of people potentially susceptible to COVID19 and the magnitude of the epidemic that extrapolates world indicators in some locations.
Performance Evaluation of CNN Based Pedestrian and Cyclist Detectors On Degraded Images

This paper evaluates the effects of input image degradation on performance of image object detectors. The purpose of the evaluation is to determine usability of the detectors trained on original images in adverse conditions. SSD and Faster R-CNN based pedestrian and cyclist detector performance with images degraded with motion blur, out-of-focus blur, and JPEG compression artefacts, most commonly occurring in mobile or static traffic systems. An experiment was designed to assess the effect of degradations on detection precision and cross class confusion. The paper describes the two datasets created for this evaluation, evaluation of a number of detectors on increasingly more degraded images, comparison of their performance, and assessment of their tolerance to different types of image degradation as well as a discussion of the results.
Angga Ade

This study investigated the effectiveness of mycorrhizal fungal multi-spores and inorganic fertilizers in increasing NPK uptake and growth of maize on ombrogenous peat soils in Riau. The experiment, which was carried out in a plastic house, was arranged in a completely randomized design (CRD) with two factors, consisting of five replications. The first factor was the application of mycorrhizal fungal multi-spores, consisting of two levels (with and without application). The second factor was the application of inorganic fertilizer, consisting of three levels (P0: without fertilizer, PM-1: mixture of 225 kg Urea + 100 kg SP-36 + 75 kg KCl + 1000 kg Dolomite, and PM-2: Mixture of 450 kg Urea + 200 kg SP-36 + 150 kg KCl + 2000 kg Dolomite). Observed data consisted of agronomic observations, soil observations, and mycorrhiza observations. Agronomic observations consisted of plant height, root dry weight, shoot dry weight, and N, P, K nutrient uptake, and soil observations consisted of total and available N, P, K nutrients in the soil. Meanwhile, mycorrhiza observations consisted of infected roots and spore populations. The data obtained were then analyzed using DMRT α=5% to see the significant effect of the treatments. There was no interaction effect of mycorrhiza and compound fertilizer on the variables of shoot and root dry weight, but the interaction effect was observed on the variables of of shoot N, P, K uptake and root P and K uptake. Received: 11th July 2019; Revised: 10th March 2020; Accepted: 17th March 2020
To study the effect and mechanism of dimethyl fumarate [DMF] in adjuvant induced arthritis model in rats

Rheumatoid arthritis [RA] is a chronic inflammatory autoimmune connective tissue disease which characteristically causes the gradual ongoing destruction of articular cartilage and resorption of bone, leading to disability and premature mortality [1-3]. It is the most common form of polyarticular inflammatory arthritis [1], and multiple epidemiological studies determined the 2000– 2015 incidence rate to be 18.5 cases per 100,000 personyears; the prevalence was 0.329% by January 2015 [4]. According to Handa et al., in 2015, the prevalence of RA in India was 0.2 0.75% [5].
Comparative Study of Classification Algorithm on Heart Disease Dataset

Abstract : Data classification is process of dividing the dataset into two or more different classes where each class contains similar type of data items. In this paper, we compare different classification algorithms using WEKA tool. Our goal is to analyze the performance of several classifiers on Heart Disease dataset. The analysis is done using six different classification algorithm. From the results of classifiers we found that KNN and J48 are the effective classifiers for medical dataset than other classifiers we have used. Weka provides implementations of learning algorithms that you can easily apply to our dataset. It also includes a variety of tools for transforming datasets, such as the algorithms for classification. You can preprocess a dataset, feed it into a learning scheme, and analyze the resulting classifier and performance.
Determination of Agarwood volatile compounds from selected Aquilaria species plantation extracted by Headspace-Solid Phase Microextraction (HS-SPME) method

A collection of inoculated heartwood of three Aquilaria species namely A. crassna, A. sinensis and A. subintegra were investigated for their volatile compounds. The volatile compounds were extracted using automated Headspace-Solid Phase Microextraction (HS-SPME) with fibre coating of 50/30 µm divinyl benzene/carboxen/poly-dimethylsiloxane (DVB/CAR/PDMS) and determined by gas chromatography/mass spectrometry (GCMS). The absence of proper scientific method in forming the agarwood has resulted in variations in agarwood quality and inconsistent grades in the market. Thus there is a need to quantify the amount of major volatile compounds found in the agarwood so that the quality can be scientifically verified. In this study, the commonly known volatile compounds present in agarwood sample were found but with varying amounts. At least three important compounds were obtained i.e. β-agarofuran, α-eudesmol and agarospirol. Based on the presence of these main volatile compounds, the study suggests that extraction by HS-SPME also useful instead of distillation process. The ability to identify key components in a short time can facilitate and shorten the analysis time. It further helps in developing the quality of agarwood industry standards.
Credit Card Fraud Detection using Different Machine Learning Models

: Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. It has cost banks and their customers a loss of billions of rupees. The techniques used now a day detects the anomaly only after the fraud transaction takes place. The intruders have found ways to crack the system loopholes and defeat the security. Thus, Artificial Intelligent (AI) algorithms are used to detect the behaviour of such activity by learning the past behaviour of the transaction of the user not only this, but data is also unbalanced so SMOTE is used. Various concepts and types have been introduced in this paper related to credit card anomaly detection systems like decision tree, random forest, Neural Networks, and Auto-encoder. A comparative study is done in this paper on the bases of accuracy, precision, recall and AUC curve.
A Review of Research on Identification of False Reviews in E-Commerce

The role of user online reviews in current personalized marketing and e-commerce platforms has attracted widespread attention from academia and enterprises, and has extremely important reference value for both service providers and demanders, while the identification of false reviews is the basis for effective use of user text reviews. This paper takes the problem of identifying users' false evaluation texts as the starting point, reviews the current research status at home and abroad, summarizes and outlines the relevant identification methods based on review texts and reviewers' perspectives in this field of research.
Investigating the Role of Inositol Polyphosphate 4-Phosphatase Type II (INPP4B) Overexpression on Autophagy in Acute Myeloid Leukemia (AML)

BACKGROUND: Inositol polyphosphate-4-phosphatase type-II (INPP4B) is a lipid phosphatase that dephosphorylates PI(3,4)P2 into PI(3)P. The depletion of PI(3,4)P2 prevents aberrant AKT signalling. Therefore, INPP4B is classically known as a tumour suppressor. Recent studies however demonstrate INPP4B overexpression may promote cancer generation and progression. We investigated effects of INPP4B overexpression in acute myeloid leukemia (AML). METHODS: AML cell lines that overexpress wild-type (INPP4B) and mutant (INPP4B) INPP4B were used to determine what phenotypes are governed by its phosphatase activity. RESULTS: Overexpression of wild-type INPP4B conferred advantages in growth, chemoresistance and colony forming assays, suggesting a role for INPP4B opposite to that of a tumour suppressor. These phenotypes were either absent or greatly diminished in INPP4B cells. INPP4B cells also had increased accumulation of autophagosomes with or without autophagy inhibitors when compared to control. CONCLUSION: INPP4B-mediated phenotypes in AML are phosphatase-dependent and this is subsequently associated with increased potential to undergo autophagy.