Miracle-dz/UniFAR-FTUs huggingface dataset:
The doxastic profile of the compulsive re-checker

ABSTRACT Incessant checking is undeniably problematic from a practical point of view. But what is epistemically wrong with checking again (and again)? The starting assumption for this paper is that establishing what goes wrong when individuals check their stove ten times in a row requires understanding the nature of the doxastic attitude that compulsive re-checkers are in, as they go back to perform another check. Does the re-checker know that the stove is off, and is thus looking for more of what she already has (Whitcomb, D. 2010. “Curiosity was Framed.” Philosophy and Phenomenological Research 81 (3): 664–687. )? Or is she an inquirer who repeatedly loses her knowledge and finds herself inquiring again and again into the same question (Friedman, J. 2019. “Checking Again.” Philosophical Issues 29 (1): 84–96. )? I present what I see as the three main hypotheses currently available, and propose a refinement to Taylor's ‘what-if questioning’ account (2020).
Option Pricing with Extreme Events

The aim of this paper is to introduce the Câmara-Heston model for pricing European options and determine if this model can adjust the Black-Scholes model for biases related with out-of-the-money options. We will show (through simulation) that this model can generate volatility smiles and skews as they are observed in the market.
An Interpolation Method with Weights and Relaxation Parameters

Abstract : This paper presents a new interpolation method that is based on blending a nonuniform rational B-spline (NURB) curve with a singularly reparametrized (SR) linear spline. The resulting curve is called the alpha-spline. It has weights and relaxation parameters. Given the data points to be interpolated, a NURB curve is obtained by using these data points as its control points. The SR linear spline is then determined by imposing constraints on the alpha-spline to interpolate the data points. The alpha-spline is parametrically continuous. It involves only simple computation, and does not require solving linear systems. This approach is extended to produce interpolatory surfaces, and can be used as a modeling tool for deforming polygonal shapes into smooth spline surface models.
Entrepreneurial Finance: Exploring the Drivers and Challenges Faced by Young Pakistani Entrepreneurs

Within the last decade, the popularity of entrepreneurship rose exponentially all over the world; however, research in this field within the context of developing countries is still lacking. Generally speaking, accessibility and availability of financial support is one of the most significant factors affecting an entrepreneur’s venture when it is in its initial stages. This is especially true in the case of developing countries such as Pakistan. For this reason, this study explored the drivers and challenges faced by Pakistani entrepreneurs regarding the access to and availability of financial support. Data was collected through 65 semi-structured interviews of budding Pakistani entrepreneurs until the saturation point was reached. Afterwards, the interviews were transcribed and imported to NVivo 12 plus, which was used to conduct a thematic analysis of the interviews. The major findings of the research revealed that entrepreneurs face many challenges with regards to the access and availability of financial support in Pakistan. Additionally, according to the respondents, the entrepreneurial journey in Pakistan is laden with challenges and has minimum drivers (opportunities available to the entrepreneurs). The respondents also expressed a lack of awareness and downright distrust of the government-sponsored financial support schemes.
Equilibrium Manipulation Planning for a Soft Elastic Rod Considering an External Distributed Force and Intrinsic Curvature

Previous work has shown that the set of equilibrium shapes of a rod is a smooth 6-dimensional manifold. We develop the Kirchhoff rod model in a differential equation form using Darboux vector that easily adds distributed force and intrinsic curvature. It can be seen that in this letter, the manifold of the equilibrium set is the same. Furthermore, we address the rod manipulation planning problem which has the certain point on rod following a predefined spatial track. We propose a novel rod manipulation planning algorithm that considers the spatial configuration distance between the current and the goal, and follows its quickest descent direction in each planning step. The experiments show that it has a much higher successful rate than straight-line path planning in the Euclidean space of the gripper and significantly less planning time than the commonly used sampling method. The hardware experiments are also conducted on a platform that consists of a manipulator, an F/T sensor and two cameras. We choose soft rubber rods in the experiments, and the results validate the proposed model and methods.
New Insights into Phase Separation Processes and Membraneless Condensates of EIN2

Recent technological advances allow us to resolve molecular processes in living cells with high spatial and temporal resolution. Based on these technological advances, membraneless intracellular condensates formed by reversible functional aggregation and phase separation have been identified as important regulatory modules in diverse biological processes. Here, we present bioinformatic and cellular studies highlighting the possibility of the involvement of the central activator of ethylene responses EIN2 in such cellular condensates and phase separation processes. Our work provides insight into the molecular type (identity) of the observed EIN2 condensates and on potential intrinsic elements and sequence motifs in EIN2-C that may regulate condensate formation and dynamics.
Improved garbage image classification for inception-Resnet-V2

In view of the current situation that the development of deep learning convolutional neural network models tends to be high in depth, the number of model parameters is large and the training time is continuously increasing. A structure based on the combination of Inception-ResNet-V2 and depthwise separable convolution is proposed, using depthwise separable volume. The integration separates channels and regions, thereby reducing a large number of parameters; at the same time, in order to extract more critical information in the feature map and improve the classification effect of the model, the attention mechanism CBAM module is introduced. In the experiment, the image of the garbage dataset is used as the experimental object, and the model is trained by data enhancement methods such as image cropping, rotation, and flipping of the original data. In order to verify the best performance of the improved model in the deep network, the experiment conducted model tests of different depths. The experimental results show that as the network continues to deepen, the classification effect of the model shows a trend of increasing first and then decreasing. When the number of Inception-ResNet-A, B, and C modules is 7,14, and 7, the model of the improved Inception ResN et- V2has the highest classification accuracy, reaching 95.80%, which is 4.58% higher than the Inception-Resnet-V2 classification accuracy, and has less parameters and better classification effect, which has higher practical value.
Digital Game Development: Shaping the Storyline by Solving Time Paradoxes

Video games are continuously gaining popularity not only as an entertainment medium, but also in fields such as art, entertainment and health. It is necessary to gain a deeper understanding of how players engage with games. A number of studies have shown connections between how players relate to their player character, the storyline, game contents, and basic components, and their resulting satisfaction with the game. The primary aim of this paper is to develop recommendations for improving creation of a game design document for digital game development. It discusses the visualization of an n-dimensional space, and presents a model for a multi-world interpretation to shape an accurate storyline. As a result, we expect to solve logical problems in the storyline of the game that uses time-travel storyline, increase players’ satisfaction and engagement, and avoid an outflow of players. The study enables a better understanding of how to create a game design document without problems associated with a time-travel storyline.
The COVID-19 Pandemic and Sexual Debut Among South Korean Adolescents

Abstract Objective: This study assesses the impact of the SARS-CoV-2 (COVID-19) pandemic on the first time Korean adolescents have sex. Methods: The study examines 2017–2021 data from an annual, cross-sectional survey. Results: There is a significant drop in the percentages of male adolescents initiating sexual intercourse in 2020 and noticeable rebounds in 2021. However, the sexual debut among female students shows no significant change. Conclusions: We suggest that strict social distancing measures in 2020 and loosening of them in 2021, different orientations toward having sex by gender, and a strong sexual double standard are responsible for these findings.
An execution engine for aerial robot mission plans

The goal of the work presented in this paper is to develop a practical solution for mission plan execution to simplify the way in which operators configure the missions of robots. This work has been done to promote a more extensive use of the software framework for aerial robotics Aerostack. We have designed a computer system called execution engine that includes technical solutions from general robotics and artificial intelligence. The system follows a behavior-based approach and a symbolic representation of beliefs. The execution engine has been designed to be part of Aerostack but it can also work independently, so that it can be reused for building other type of robot architectures. This paper has been written as a specification and software design to be used as a guide for software implementation of the execution engine.
High-Dimensional Multi-Label Data Stream Classification With Concept Drifting Detection

Multi-label data streams such as Web texts and images have been popular on the Web. These data present the characteristics of multiple label, high dimensionality, high volume, high velocity and especial concept drift etc. Thus, multi-label data stream classification is a very challenging and significant task especially in the handling of high-dimensional data with concept drifts. However, this challenge has received little attention from the research community. Therefore, we propose the max-relevance and min-redundancy based algorithm adaptation approach for the efficient and effective classification on multi-label data streams with high-dimensional attributes and concept drifts. In order to reduce the impact from the high-dimensional data with noisy attributes, we first refine the minimal-redundancy-maximal-relevance criterion based on mutual information to select qualified features in multi-label data streams. Second, we propose the data distribution based concept drifting detection approach to distinguish concept drifts hidden in data streams. Finally, we build an incremental ensemble classification model for efficiently classifying multi-label data streams. Extensive studies show that our approach can get optimal subsets of features while maintaining a good performance in the multi-label classification, as compared to several state-of-the-art multi-label feature selection algorithms using two efficient multi-label classification methods as base classifiers. Meanwhile, our approach is superior to three well-known multi-label data stream classification approaches in the effectiveness and efficiency.
The Development of Human Navigation in Middle Childhood: A Narrative Review through Methods, Terminology, and Fundamental Stages

Spatial orientation and navigation are fundamental abilities in daily life that develop gradually during childhood, although their development is still not clear. The main aim of the present narrative review was to trace the development of navigational skills in middle childhood (6 to 12 years old) by means of studies present in the literature. To this aim, this review took into account the terminology, methodologies, different paradigms, and apparatuses used to investigate egocentric self-centered and allocentric world-centered representations, besides the different types of spaces (reaching/small/large; physical/virtual). Furthermore, this review provided a brief description of the development of navigational strategies and competences in toddlers and preschool children (0–5 years). The main result of this review showed how middle childhood is a crucial period for the improvement and development of allocentric strategies, including metric information. In fact, during this developmental window, children learn to handle proximal and distal cues, to transpose paper and virtual information into real environments, up to performing similarly to adults. This narrative review could represent a starting point to better clarify the development of navigation and spatial orientation, finalized to trace a development curve useful to map normal development and to have a term of comparison to assess performance in atypical development.
Stability Analysis of an Anaerobic Wastewater Treatment Model

In this paper, we consider the stability analysis of an anaerobic digestion dynamical model where the gaseous flow and the alkalinity are incorporated. A detailed estimation of the carbon dioxide gas outflow is carried out and the positivity of the solution is demonstrated. We show that the process environment may lead to the coexistence of a stable and an unstable steady-state. The stability analysis and the stabilization of the process are illustrated by a simulation study including the phase portraits related to methanogenic bacteria.
Camera-Based Meal Type and Weight Estimation in Self-Service Lunch Line Restaurants

Individuals, restaurant owners and health organizations are all interested in accurate information about food intake, but collecting the information in sufficiently automated way remains a practical challenge. In controlled environments, such as lunch line restaurants, the food intake can be estimated by measuring the portions upon purchase and separately monitoring the food waste, but even this requires often complicated setups such as repeated weighing of the plate after every meal component. In this work we explore the feasibility of using a combination of ceiling-mounted cameras and computer vision for estimating both the types and weights of individual food items the customers are taking in a lunch line restaurant. We describe the imaging system and weighing-based sensing for obtaining ground truth training data, and develop and evaluate deep learning models for the computer vision tasks. We demonstrate high accuracy especially in meal type identification and hence validate the feasibility of the approach. We release the annotated dataset for further development of improved methods.
Bulletin of Electrical Engineering and Informatics

Received Apr 6, 2022 Revised Jul 7, 2022 Accepted Jul 27, 2022 Plant diseases consider the most dominant matter for farmers' concerns because the operation of discovering and dealing with them requires accuracy, experience, and time. Therefore, this paper proposes an approach to classify seven varieties of tomato diseases using deep learning models. A dataset of 10448 images from PlantVillage and google utilize to train the deep learning (CNN models). The trained models proved their ability to classify with high accuracy, as the highest testing accuracy reached 95.71% for the proposed model for 50 epochs only. The resulted best model is published to a mobile application using the android studio platform, this application enables the farmer to classify plant diseases accurately and easily. The proposed model and mobile application could be extended to classify as many plant diseases as possible.
Integrating Lattice-Free MMI Into End-to-End Speech Recognition

In automatic speech recognition (ASR) research, discriminative criteria have achieved superior performance in DNN-HMM systems. Given this success, the adoption of discriminative criteria is promising to boost the performance of end-to-end (E2E) ASR systems. With this motivation, previous works have introduced the minimum Bayesian risk (MBR, one of the discriminative criteria) into E2E ASR systems. However, the effectiveness and efficiency of the MBR-based methods are compromised: the MBR criterion is only used in system training, which creates a mismatch between training and decoding; the on-the-fly decoding process in MBR-based methods results in the need for pre-trained models and slow training speeds. To this end, novel algorithms are proposed in this work to integrate another widely used discriminative criterion, lattice-free maximum mutual information (LF-MMI), into E2E ASR systems not only in the training stage but also in the decoding process. The proposed LF-MMI training and decoding methods show their effectiveness on two widely used E2E frameworks: Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Compared with MBR-based methods, the proposed LF-MMI method: maintains the consistency between training and decoding; eschews the on-the-fly decoding process; trains from randomly initialized models with superior training efficiency. Experiments suggest that the LF-MMI method outperforms its MBR counterparts and consistently leads to statistically significant performance improvements on various frameworks and datasets from 30 hours to 14.3 k hours. The proposed method achieves state-of-the-art (SOTA) results on Aishell-1 (CER 4.10%) and Aishell-2 (CER 5.02%) datasets. Code is released1.
STUDENT ERRORS IN WRITING THE ENGLISH MATHEMATICAL OPERATION IN HIGHER EDUCATION

English has been well-known as the world's international language. As a means of global communication, it should be actively mastered both orally and in writing. Writing is one of the skills that must be mastered by the students. It is related to the words, phrases, clauses and sentences. Then, if the meaning is unclear, people will misunderstand about the meaning of the sentences. The research investigated student errors in writing the English mathematical operations. The research is focused on the operation of addition, subtraction, multiplication and division. The research used qualitative approach using test and interview to collect the data. After conducting the research, it shows that there are some types of errors found such as incomplete materials error, technical errors, errors in understanding the previous material, and language misinterpretation. Meanwhile, student errors in the type of data misuse, theorems and definition of misuseand also unverified solutions are not found on the research.
Vulnerability Assessment of Phasor Estimation Algorithms under CT Saturation: A Comparative Analysis

Saturation phenomena in the current transformer (CT) may impose severe deformation in the secondary current of a CT. Obviously, CT saturation profoundly impacts the performance of the phasor estimation algorithms which can result in the mal-operation of the protective relays. Besides of CT saturation, presence of decaying DC component is another factor that affects the performance of the phasor estimators. This paper provides a comparative analysis between full-cycle and sub-cycle phasor estimators to evaluate their abilities under the CT saturation with/without decaying DC component. Through the following investigation, the sensitivity of the different phasor estimators is evaluated under CT saturation, DC decaying, and noisy conditions. The precision and robustness of the different phasor estimator are evaluated under different simulation results in MATLAB software and furthermore the requirements about phasor estimator for dealing with CT saturation are discussed.
PolSAR Covariance Structure Detection and Classification based on the EM Algorithm

This paper proposes a new method for clustering polarimetric synthetic aperture radar images by leveraging the peculiar characteristics of the polarimetric covariance matrix (PCM). Specifically, the feature used for classification is the PCM structure. To this end, the problem of detecting and classifying spatial variations in PCM structure is formulated as a multiple hypothesis test, where one null hypothesis and multiple alternative hypotheses are present. The estimation problems are solved by resorting to hidden random variables representative of covariance structure classes in conjunction with the expectation-maximization algorithm. These estimates are then used to form a penalized likelihood ratio test. The effectiveness of the proposed detection strategies is demonstrated on real polarimetric SAR data.
Dual-Polarized Wide-Angle Scanning Linear Phased Array Antenna Design With High Polarization Isolation

This letter presents a dual-polarized low-profile linear phased array antenna design achieving one-dimensional wide-angle scanning with high polarization isolation. The dual-polarized antenna is constructed based on a metasurface serving different purposes for two polarization cases to achieve high polarization isolation while providing a low profile as a whole. It works as artificial magnetic conductor for x-polarization and radiator for y-polarization. To achieve wide-angle scanning, surface wave effect is exploited for x-polarization while for y-polarization widebeam active element patterns is implemented with a small array spacing of 0.42 λ0 applied. To verify the design, a 1×8 linear array was fabricated and tested. Measured and simulated results both demonstrate that, within a wide operating band, the proposed dual-polarized antenna has a high port-to-port polarization isolation over 29 dB with a low profile of 0.06 λ0. The measured results show that for x-polarization, the linear array can steer its main beam up to 81°, while for y-polarization, it can steer the main beam up to 70°. The cross-polarization levels can maintain below −20 dB during scanning in the general.
Fatty acid and glucose metabolism in upper body obesity : effects of diet and exercise compared to pioglitazone administration

We report that direct uptake of circulating free fatty acids (FFA) into adipocytes occurs in vivo, in humans. After validating a method to measure direct adipocyte FFA uptake we measured this process in normal weight, postabsorptive men and women. Direct FFA uptake was greater in women than men 8.2 ± 0.6 % vs. 4.0 ± 0.5% (P < 0.0001) of FFA were taken up by subcutaneous adipose tissue, respectively. Abdominal subcutaneous fat took up FFA more avidly than femoral fat in men, but this did not occur in women. Similar sex and regional difference were found to occur in obese women and men. These findings are uniquely consistent with the greater subcutaneous body fat mass and the lower body fat distribution in women. Gene expression of fatty acid transporters was greater in abdominal than femoral fat in men (P < 0.05), but not in women (P = 0.80). We conclude that sex and site specific recycling of circulating FFA into subcutaneous fat is a novel FFA disposal pathway that may also play a role in the development or maintenance of body fat distribution. Regional variations in facilitated fatty acid transport may contribute to this process. 1 Drs Shadid and Koutsari contributed equally to this paer Endocrine Research Unit, Mayo Clinic, Rochester, Minnesota, USA