Miracle-dz/UniFAR-FTUs huggingface dataset:
Population Biology of a Neotropical Frog (Rana vaillanti)

We studied the population dynamics, food habits, and growth of a population of Rana vail- lanti in a permanent lake in southern Veracruz, Mexico from April 1984 through March 1985. We collected frogs five consecutive nights each month, after which we measured, marked, stomach flushed, and released them on the same night We captured 1851 frogs, 608 of which were recaptured at least once. The population size estimate, 1622, did not vary greatly throughout the year. Adults composed the greatest proportion of the population (41.5% males, 52.4% females, 6.1% subadults). Average life expectancy was estimated to be eight months. Females grew faster than males and attained larger sizes. Rana vaillanti is a sit and wait predator with a diverse diet including birds, fish, and conspecifics. They consume a higher proportion of invertebrates than vertebrates, principally insects (Coleoptera and Odonata) and spiders.
Effect of Entrepreneurial Orientation on Profitability of Women Owned Enterprises in Pokhara City, Nepal

Entrepreneurial orientation is defined as an organization's strategic orientation, which seizure an organization's strategic making practices, managerial philosophy, and the organization's behavior, which are entrepreneurial. This study investigates the effect of entrepreneurial orientation on the profitability of women-owned enterprises in Pokhara City, Nepal, through mixed research method qualitative and quantitative analysis. The primary data were obtained from an in-depth interview with two experts of government offices and sixteen personnel/founder from women-owned enterprises. Secondary data were collected from Nepal Government and The World Bank. The study found that entrepreneurial orientation and its various dimension (Proactiveness, Innovativeness, Risk-taking, Competitive Aggressiveness, and Autonomy) are the major influencing factors to increase the profitability of women-owned enterprises. Furthermore, in the present context, women-owned enterprises should concentrate more on innovativeness to achieve profitability. Women should overcome all the issues and challenges they face at the personal level, social level, financial level, and government level, which will be vital in improving women-owned enterprises' business growth. The finding of this study helps the women-owned enterprises be more entrepreneurial to sustain and grow in the competitive market environment amid huge challenges and barriers.
Neural Network based Classification of Flowers using Transfer Learning

One of the classical problems in the field of computer vision and machine learning and subsequently deep learning is image classification. While Deep Learning solves the much difficult hurdles like feature extraction and presents us with better optimizations like gradient descent and Adam optimizer, most deep learning models still need a lot of raw computational power to train models on local Graphical Processing Units (GPUs) or Tensor Processing Units (TPUs) in the cloud. All of this computational power is not readily available in all environments and systems and hence the concept of pre-trained models can help to reduce training time by a huge margin. Initial models get trained on large array of GPUs and do feature extraction. The classification part is for the end-user to customize in accordance to the problem at hand and can be completed in very less time. We tackled the multi-class classification botanical problem of identifying flowers of 5 types, namely, Sunflower, Rose, Dandelion, Daisy, and Tulip. The feature extraction part is done with the model (Google’s Inception-v3) and fully connected softmax layers were trained on local machine on a Nvidia GeForce GTX 950 (with CUDA activated) within 30 minutes time and total steps/epochs were 4000 only. The total number of training images is 3,500 (approx.). The finished model produced results with final test accuracy as 91.9% on new images (N=664).
Classify SMS in Mobile System Using IBK and K-Star

: The intense growth of smart mobile phones and users has contributed to the expansion of online or offline Instant Messaging and SMS usage as an alternate way of Transaction and communication. Along with the faith they instinctively have in their devices, makes this kind of messages a congenial environment for spammers. In fact, reports distinctly shows that volume of spam over Instant Messaging and SMS is rapidly increasing year by year. This represents a challenging problem for classical filtering methods these days. Smishing this term represents a phishing in SMS/Messages called as SMS-phishing is a cyber-security attack, which utilizes Short Message Service (SMS) to steal personal data/credentials of mobile users. The faith level of mobile users on their smartphones has attracted attackers to perform various mobile security attacks like SMS-Phishing. In this paper, we implement the SMS-Case-based data mining classification approach to classify them subpart of SMS category by detecting of legitimate, Illegitimate/Smishing messages and these classified messages will further categorize in three parts Primary, Other, Fake. In this research paper IBL and K-Start is used to classify the SMS and moreover we will analyses the classified data model in detail using Weka tool. During the lockdown period due to Pandemic affected by COVID19 SMS-Phishing becomes more active and the attackers send fraud messages to the mobile users intensively
Estimating daily solar radiation using soft computing method

The objective of this study is to develop generalized regression neural networks (GRNN) model for estimating daily solar radiation using limited weather variables at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using GRNN model. From the performance evaluation and scatter diagrams of GRNN model, GRNN 3 (three input) model produces the best results for both stations. Results obtained indicate that GRNN model can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois . These results testify the generation capability of GRNN model and its ability to produce accurate estimates in Illinois.
Predicting distribution of plant species in arid rangelands of central Iran using probabilistic methods

The quantification of complex relationships between environmental variables and plant habitat distribution is difficult and crucial. The present study employed Logistic Regression (LR), Maximum Entropy (MaxEnt) and Artificial Neural Network (ANN) methods to model plant habitat distribution and identifies the most appropriate modeling approach. The study was conducted in Poshtkouh rangelands, Yazd Province, central Iran. Vegetation was sampled using randomize-systematic sampling method. Soil samples were taken from 0-30 and 30-80 cm depths. The highest values of Kappa index (0.57) belonged to the ANN. Average Kappa values for the MaxEnt and LR were 0.56 and 0.48, respectively. The performance of LR model was higher for species with high marginality and low tolerance, e.g. Cornulaca monacantha, and lower for species with low marginality and high tolerance, e.g. Artemisia sieberi. The ANN and MaxEnt provided better models for species with complex distribution patterns such as widespread species. In fact, differences in the optimal ecological range of plant species, could affect the accuracy of predictive distribution models.
A new stochastic approach for advection-diffusion problems with uncertain parameters

We propose a new numerical method for solving advection-diffusion equations with uncertainty. The stochastic effects are introduced either in the velocity field or in the initial data resulting into a class of stochastic partial differential equations. Chaos polynomials are used to represent the stochastic processes in the considered model. This procedure, known also by spectral decomposition, results into a system of deterministic advection-diffusion equations. For each chaos coefficient, the method of characteristics is used to integrate in time its associated advectiondiffusion equation. Central finite differencing is implemented for the space discretization. The proposed method is verified for an advection-diffusion equation with known analytical solution. We also apply the method for simulation of the one-dimensional viscous Burgers equation. In both examples, the method demonstrates its ability to better maintain the shape of the solution in the presence of uncertainty and shocks.
Physicochemical models of formation of gold–silver mineralization at the Rogovik deposit (Northeastern Russia)

The T,X-parameters of the main ore-forming stages of the Rogovik epithermal gold-silver deposit (northeastern Russia) were determined using microthermometric techniques on fluid inclusion assemblages. On the basis of these data we carried out thermodynamic calculations that model the formation conditions of three types of ore mineral assemblages: gold-silver – at the early volcanic stage, silver-polymetallic and silver-gold-polymetallic – at the late volcanoplutonic stage. The silver-gold-polymetallic mineralization is located at the sites where silverpolymetallic and gold-silver zones overlap. The calculations were made using the “Selektor-C” software within a complex geochemical multicomponent system. Several scenarios of formation of gold-silver mineralization at an early volcanic stage were considered: 1) boiling of a hydrothermal solution; 2) interaction of residual aqueous solution with host rocks; 3) mixing of an ore-bearing gas phase with meteoric waters. For the late volcanoplutonic stage, we modeled formation of “silver mineralization” using an ore-forming solution genetically related to a deep-seated granite massif and its interaction with host rocks and gold-silver zones of early volcanic stage. These physicochemical models substantially improve our understanding of formation of the three types of mineral assemblages at the Rogovik deposit and can be applied to other epithermal deposits with similar mineralization styles.
Multimodal Fusion of Appearance Features, Optical Flow and Accelerometer Data for Speech Detection

In this paper we examine the task of automatic detection of speech without microphones, using an overhead camera and wearable accelerometers. For this purpose, we propose the extraction of hand-crafted appearance and optical flow features from the video modality, and time-domain features from the accelerometer data. We evaluate the performance of the separate modalities in a large dataset of over 25 hours of standing conversation between multiple individuals. Finally, we show that applying a multimodal late fusion technique can lead to a performance boost in most cases.
Exploring the relationship between coping humor and subjective happiness:belongingness and forgiveness as serial mediators

results The results indicated a significant and positive link among subjective happiness, coping humor, belongingness, and forgiveness. In addition, serial mediation analysis revealed that coping humor was associated with higher belongingness, which was associated with increased forgiveness, which were then associated with more subjective happiness. Therefore, coping humor was indirectly associated with more subjective happiness via belongingness and forgiveness, in a serial fashion.
The strategy of coping with stress and selected health related behavioural patterns of demobilized battle participants

Background: The aim of the herein paper is to express knowledge of the role of ties (relations) between mental factors such as trust in your own possibilities of coping with pain and availing of the active strategies of coping with stress, while also the degree of readiness to limit stress with the aid of the use of alcohol. Methods: The research covered the group of 38 demobilized ATO participants (21-46 years) undergoing treatment and rehabilitation in the Hospital. A measurement was conducted with the aid of certified psychological questionnaires and from medical documentation. Results: A correlation occurred between the active strategy of coping with the stress felt and the strategy of coping with pain of the ICS type – Internal control of pain (r=0.71; p<0.05), as well as the negative correlation between the readiness to utilize stimulants with the aim of easing stress and the strategy of coping with pain of the ICS type (r=-0.58; p<0.05). Conclusion: The ways of coping with stress and pain by the hospitalized participants of combat action may be significant factors (of a psychological nature) that have an impact on their readiness to undertake behaviour that is significant for their health (such as alcohol and others) with the aim of reducing the symptoms of nervous tension felt.
Fire and Smoke Detection in Video Sequences

In this paper we present a fire and smoke detection system that uses a block-based approach. It detects smoke and fire separately in each block of a video sequence. The detection is based on spatio-temporal features of the smoke and fire, such as the fire flickering and smoke spread. Then these block detections are merged into the fire and smoke events, which are the final detections. Two versions of the algorithm are presented: in the first version the block classification is made by a heuristic classifier that doesn’t use a machine learning, and in the second version the block classifiers are trained using videos from the trained set. We performed training and evaluation on a private dataset, consisting of 120 videos. Experimental results are presented.
Impact of Knowledge Capability and Mindset on Fraud Risk Assessment within Nigerian Deposit Money Banks

Introduction of digital technology into the banking sector created opportunities for banks to prevent and detect various forms of frauds. To ensure this, deposit money banks (DMBs) employed various forms of electronic products and services to facilitate ethical banking. This study presents an assessment of knowledge capability (KC) and mindset of bank examiners in fraud risk assessment (FRA) within DMBs. The study examines regulators’ capacity for fraud prevention and detection within the Nigerian DMBs. This study deployed both primary and secondary sources for data collection. A total of 150 questionnaires were distributed out of which 120 were returned by targeted respondents (bank examiners) all employees of the Central bank of Nigeria (CBN), Nigerian Deposit Insurance Corporation (NDIC) and 10 DMBs. Out of the 120 questionnaires returned, 85 were retained for analysis after removal of outliers. Subsequently, the study employed IBM SPSS version 25 and PLS-SEM (Smart-PLS) software version 3 for data analysis. The results revealed significant positive relationship between bank examiners’ knowledge capability, mindset and (task performance) fraud risk assessment and by implication general reduction in the incidence of fraud and forgeries within DMBs. Consequently, the study contributes to enhancement of institutional and legal frameworks for banking examination, compliance and reporting. The empirical study would result in overall reduction in the escalating occurrence of fraud and forgeries within Nigerian DMBs. Researchers may explore interviews as primary source of data collection in addition to questionnaire administration and mixmethods, that is, qualitative and quantitative research methodology.
STABILITY AND PERFORMANCE ANALYSIS OF CLASSICAL DECENTRALIZED CONTROL OF IRRIGATION CANALS

Irrigation canals have a series structure which is generally used to design multivariable controllers based on the aggregation of decentralized monovariable controllers. SISO controllers are designed for each canal pool, assuming that the interactions will not destabilize the overall system. It is shown that, when the canal pools are controlled using the discharge at one boundary, the multivariable decentralized control structure is stable if and only if the SISO controllers are stable. The performance of the multivariable system is also investigated, and it is shown that the interactions decrease the overall performance of the controlled system. This loss of performance can be reduced by using a feedforward controller. Experimental results show the effectiveness of the method.
Cancerous skin quantitative assessment based on morphological features using PS-FF-OCT system

Skin cancer is a leading cause for death. Worldwide around 80 percent of all skin cancers are due to Non-Melanoma Skin Cancers (NMSC), Basal Cell Carcinomas (BCC) and Squamous Cell Carcinomas (SCC). To characterize the breast cancerous tissue from normal one, three parameters were extracted i.e. mean, standard deviation and entropy. Quantitative information from optical coherence tomography images can be used for high resolution, label free, rapid skin diagnosis and also helpful to the clinicians.
3D magnetized jet break-out from neutron-star binary merger ejecta: afterglow emission from the jet and the ejecta

We perform three-dimensional (3D) general-relativistic magnetohydrodynamic simulations to model the jet break-out from the ejecta expected to be produced in a binary neutron-star merger. The structure of the relativistic outflow from the 3D simulation confirms our previous results from 2D simulations, namely, that a relativistic magnetized outflow breaking out from the merger ejecta exhibits a hollow core of $\theta_{\rm core}\approx4^{\circ}$, an opening angle of $\theta_{\rm jet}\gtrsim10^{\circ}$, and is accompanied by a wind of ejected matter that will contribute to the kilonova emission. We also compute the non-thermal afterglow emission of the relativistic outflow and fit it to the panchromatic afterglow from GRB170817A, together with the superluminal motion reported from VLBI observations. In this way, we deduce an observer angle of $\theta_{\rm obs}= 35.7^{\circ \,\,+1.8}_{\phantom{\circ \,\,}-2.2}$. We further compute the afterglow emission from the ejected matter and constrain the parameter space for a scenario in which the matter responsible for the thermal kilonova emission will also lead to a non-thermal emission yet to be observed.
Latent Representation Prediction Networks

Deeply-learned planning methods are often based on learning representations that are optimized for unrelated tasks. For example, they might be trained on reconstructing the environment. These representations are then combined with predictor functions for simulating rollouts to navigate the environment. We find this principle of learning representations unsatisfying and propose to learn them such that they are directly optimized for the task at hand: to be maximally predictable for the predictor function. This results in representations that are by design optimal for the downstream task of planning, where the learned predictor function is used as a forward model. To this end, we propose a new way of jointly learning this representation along with the prediction function, a system we dub Latent Representation Prediction Network (LARP). The prediction function is used as a forward model for search on a graph in a viewpoint-matching task and the representation learned to maximize predictability is found to outperform a pre-trained representation. Our approach is shown to be more sample-efficient than standard reinforcement learning methods and our learned representation transfers successfully to dissimilar objects.
Cross-Modal Alignment with Mixture Experts Neural Network for Intral-City Retail Recommendation

In this paper, we introduce Cross-modal Alignment with mixture experts Neural Network (CameNN) recommendation model for intral-city retail industry, which aims to provide fresh foods and groceries retailing within 5 hours delivery service arising for the outbreak of Coronavirus disease (COVID-19) pandemic around the world. We propose CameNN, which is a multi-task model with three tasks including Image to Text Alignment (ITA) task, Text to Image Alignment (TIA) task and CVR prediction task. We use pre-trained BERT to generate the text embedding and pre-trained InceptionV4 to generate image patch embedding (each image is split into small patches with the same pixels and treat each patch as an image token). Softmax gating networks follow to learn the weight of each transformer expert output and choose only a subset of experts conditioned on the input. Then transformer encoder is applied as the share-bottom layer to learn all input features' shared interaction. Next, mixture of transformer experts (MoE) layer is implemented to model different aspects of tasks. At top of the MoE layer, we deploy a transformer layer for each task as task tower to learn task-specific information. On the real word intra-city dataset, experiments demonstrate CameNN outperform baselines and achieve significant improvements on the image and text representation. In practice, we applied CameNN on CVR prediction in our intra-city recommender system which is one of the leading intra-city platforms operated in China.
Energy Content in Seeds

Seeds from five species of plants were collec+. ed monthly between October 1965 and April 1967 for energy determination. Additionally, seeds of those species plus another were collected, held four monms under controled conditions, and analyzed for energy content monthly. Seeds in the monthly field collections exhibited no significant timbrelafed energy changes. Only one of the six species had a significant timbre'ated change under controlled laboratory conditions. Energy contents of some seeds collected from the same stands during different years differed significantly. Trans. Kans. Acad. Sci., 75 (4), 1972.
Selection of Regression Models under Linear Restrictions for Fixed and Random Designs

Many important modeling tasks in linear regression, including variable selection (in which slopes of some predictors are set equal to zero) and simplified models based on sums or differences of predictors (in which slopes of those predictors are set equal to each other, or the negative of each other, respectively), can be viewed as being based on imposing linear restrictions on regression parameters. In this paper, we discuss how such models can be compared using information criteria designed to estimate predictive measures like squared error and Kullback-Leibler (KL) discrepancy, in the presence of either deterministic predictors (fixed-X) or random predictors (random-X). We extend the justifications for existing fixed-X criteria Cp, FPE and AICc, and random-X criteria Sp and RCp, to general linear restrictions. We further propose and justify a KL-based criterion, RAICc, under random-X for variable selection and general linear restrictions. We show in simulations that the use of the KL-based criteria AICc and RAICc results in better predictive performance and sparser solutions than the use of squared error-based criteria, including cross-validation.