Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Based Admission Data Processing for Early Forecasting Students' Learning Outcomes

In this paper, the authors explore the factors to improve the accuracy of predicting student learning outcomes. The method can remove redundant and irrelevant factors to get a “clean” data set without having to solve the NP-Hard problem. The method can improve the graduation outcome prediction accuracy through logistic regression machine learning method for “clean” data set. They empirically evaluate the training and university admission data of Hanoi Metropolitan University from 2016 to 2020. From data processing results and the support from the machine learning techniques application program, they analyze, evaluate, and forecast students' learning outcomes based on admission data, first-year, and second-year academic performance data. They then submit proposals of training and admission policies and methods of radically and quantitatively solving problems in university admissions.
Euler class groups and motivic stable cohomotopy

We study motivic cohomotopy sets in algebraic geometry using the Morel-Voevodsky ${{\mathbb A}^1}$-homotopy category: these sets are defined in terms of maps from a smooth scheme to a motivic sphere. Following Borsuk, we show that in the presence of suitable dimension hypotheses on the source, our motivic cohomotopy sets can be equipped with abelian group structures. We then explore links between these motivic cohomotopy groups, Euler class groups \`a la Nori--Bhatwadekar--Sridharan and Chow-Witt groups. Using these links, we show that, at least for $k$ an infinite field having characteristic unequal to $2$, the Euler class group of codimension $d$ cycles on a smooth affine $k$-variety of dimension $d$ coincides with the codimension $d$ Chow-Witt group. As a byproduct, we describe the Chow group of zero cycles on a smooth affine $k$-scheme as the quotient of the free abelian group on zero cycles by the subgroup generated by reduced complete intersection ideals; this answers a question of S. Bhatwadekar and R. Sridharan.
A Comparative Study on the Temporal Effects of 2D and VR Emotional Arousal

Previous research comparing traditional two-dimensional (2D) and virtual reality with stereoscopic vision (VR-3D) stimulations revealed that VR-3D resulted in higher levels of immersion. However, the effects of these two visual modes on emotional stimulus processing have not been thoroughly investigated, and the underlying neural processing mechanisms remain unclear. Thus, this paper introduced a cognitive psychological experiment that was conducted to investigate how these two visual modes influence emotional processing. To reduce fatigue, participants (n = 16) were randomly assigned to watch a series of 2D and VR-3D short emotional videos for two days. During their participation, electroencephalograms (EEG) were recorded simultaneously. The results showed that even in the absence of sound, visual stimuli in the VR environment significantly increased emotional arousal, especially in the frontal region, parietal region, temporal region, and occipital region. On this basis, visual evoked potential (VEP) analysis was performed. VR stimulation compared to 2D led to a larger P1 component amplitude, while VEP analysis based on the time course of the late event-related potential component revealed that, after 1200 ms, the differences across visual modes became stable and significant. Furthermore, the results also confirmed that VEP in the early stages is more sensitive to emotions and presumably there are corresponding emotion regulation mechanisms in the late stages.
Design and Enhanced Equivalent Magnetic Network Modeling of a Fractional-Slot Spoke-Array Vernier PM Machine With Rotor Flux Barriers

The spoke-type arrangement of permanent-magnets (PMs) in rotor of Vernier-PM (VPM) machines was first introduced in a dual-stator topology to overcome low power drawback of VPM machines. However, this special structure is mostly applicable for high-power motor drives. This paper presents a fractional-slot spoke-array VPM machine with a special rotor structure for small scale electric-vehicles. The leakage flux lines tend to be aligned with the main flux routes due to special design of flux barriers in end portion of PM housings. High torque-density, low weight, torque-speed curve with a wide constant torque region, promising efficiency and power-factor are the advantages of the proposed machine. An equivalent-magnetic-network (EMN) model is established for predicting the complex behavior of flux lines and operating quantities of the proposed structure with reasonable accuracy. The contribution of the modeling method includes a special mesh cell for the air-gap permeance network to more accurately capture the flux routes, continuous positioning method for omitting spikes on post-processing waveforms due to numerical errors and a stable solving flowchart with a high convergence rate. The validity of EMN model is proved by comparing its operating waveforms with the finite-element results and experimental measurements.
PolyCosGraph: A Privacy-Preserving Cancelable EEG Biometric System

Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacy-preserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based on Polynomial transformation embedding Cosine functions with Graph features of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.
A Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis

Unequal data distribution among different classes usually cause a class imbalance problem. Due to the class imbalance, the classification models become biased toward the majority class and misclassify the minority class. Class imbalance issue becomes more complex when it occurs in multi-class data. The most common method to handle the class imbalance is data resampling that involves either over-sampling minority class instances or under-sampling majority class instances. In the case of under-sampling, there is a chance of losing some crucial information, whereas over-sampling can cause an overfitting problem. Therefore, we propose a novel Cluster-based Hybrid Sampling for Imbalance Data (CBHSID) approach to address these issues. The CBHSID calculates the mean of the data observations based on the number of classes. It uses the calculated mean as a threshold value to segregate majority and minority classes. CBHSID applies affinity propagation cluster analysis to each class to create sub-clusters and calculates the distance of each data item of sub-cluster using centroid mean. CBHSID removes data observations that are away from the center of sub-cluster during under-sampling. On the other hand, during the over-sampling, it generates synthetic samples using data observations near to the center of sub-cluster. We compared CBHSID with a few state-of-the-art data balancing methods on 12 binary and 4 multi-class benchmark datasets. Based on Geometric-Mean (G-Mean), Recall, and F1-score, our method outperformed the other compared methods on 14 datasets out of 16. Results also revealed that CBHSID is suitable for addressing class imbalance issues in both binary and multi-class classifications. In the current state, we have only validated CBHSID on stationary data streams. Consequently, CBHSID can further be tested on non-stationary data streams in online learning environments.
Recurrence and winding number for a revolving random walk

We consider simple random walks on two partially directed square lattices. One common feature of these walks is that they are bound to revolve clockwise; however they exhibit different recurrence/transience behaviors. Our main result is indeed a proof of recurrence for one of the graphs, solving a conjecture of Menshikov et al. ('17). For the other one, we analyze the asymptotics of the return probabilities, providing a new proof of its transience. Furthermore, we study the limiting laws of the winding number around the origin for these walks.
Some remarks about q-Chebyshev polynomials and q-Catalan numbers and related results

The moments of the Lucas polynomials and of the Chebyshev polynomials of the first kind are (multiples of) central binomial coefficients and the moments of the Fibonacci polynomials and of the Chebyshev polynomials of the second kind are Catalan numbers. In this survey paper we present some generalizations of these results together with various q-analogues.
Stress-driven solution to rate-independent elasto-plasticity with damage at small strains and its computer implementation

Quasistatic rate-independent damage combined with linearized plasticity with hardening at small strains is investigated. Fractional-step time discretization is devised with the purpose of obtaining a numerically efficient scheme, possibly converging to a physically relevant stress-driven solution, which however is to be verified a posteriori using a suitable integrated variant of the maximum-dissipation principle. Gradient theories both for damage and for plasticity are considered to make the scheme numerically stable with guaranteed convergence within the class of weak solutions. After finite-element approximation, this scheme is computationally implemented and illustrative 2-dimensional simulations are performed.
Upper bounds for some Brill-Noether loci over a finite field

Let C be a smooth projective algebraic curve of genus g over the finite field F_q. A classical result of H. Martens states that the Brill-Noether locus of line bundles L in Pic^d C with deg L = d and h^0(L) >= i is of dimension at most d-2i+2, under conditions that hold when such an L is both effective and special. We show that the number of such L that are rational over F_q is bounded above by K_g q^(d-2i+2), with an explicit constant K_g that grows exponentially with g. Our proof uses the Weil estimates for function fields, and is independent of Martens' theorem. We apply this bound to give a precise lower bound of the form 1 - K'_g/q for the probability that a line bundle in (Pic^(g+1) C)(F_q) is base point free. This gives an effective version over finite fields of the usual statement that a general line bundle of degree g+1 is base point free. This is applicable to the author's work on fast Jacobian group arithmetic for typical divisors on curves.
Neutrophil to High-Density Lipoprotein Ratio is Associated with Hemorrhagic Transformation in Patients with Acute Ischemic Stroke

Background Hemorrhagic transformation (HT) is one of the most common and severe complications in patients with acute ischemic stroke (AIS). It indicates a poor prognosis in AIS patients. However, the association of neutrophil to high-density lipoprotein ratio (NHR) with HT remains unclear. Purpose This study examined whether the NHR has a predictive effect on HT in AIS patients and explored the predictive cutoff value of the NHR. Methods This is a retrospective study and consecutively included AIS patients admitted to the Department of Neurology of the First Affiliated Hospital of Wannan Medical College between December 2019 and January 2022. All subjects had blood samples collected within 24 h of admission, and neutrophil counts and high-density lipoprotein counts were detected. HT was diagnosed with hemorrhage on subsequent magnetic resonance imaging (MRI) or computed tomography (CT) of the brain. Univariate logistic regression analysis was performed to identify confounding factors, and multivariate logistic regression analysis determined the correlation between NHR and HT. Receiver operating characteristic (ROC) curves were used to evaluate the clinical predictive value of NHR. Results A total of 725 patients were finally included in this study, of which 87 (12%) developed HT. The median NHR value in the HT group was 4.31, which was significantly higher than that in the non-HT group, and the difference was statistically significant [4.31 (3.54–6.24) vs 3.63 (2.68–4.64), p < 0.001]. The binary logistic regression analysis showed that NHR was independently associated with HT in AIS patients (OR: 1.180, 95% CI: 1.036–1.344, p = 0.013). The area under ROC curve (AUC) of NHR for predicting HT in AIS patients was 0.633 (95% CI: 0.567–0.699, p < 0.001), and its optimal cutoff were 3.52. Conclusion The NHR was a reliable and simple independent predictor of HT in AIS patients.
Monitoring ecological and environmental stress patterns over the past two decades in the Mekong River basin

ABSTRACT Among the most biodiverse environments in the world, the Mekong River Basin has experienced extensive socio-economic development in the past few decades resulting in land use change and associated ecological and environmental stresses. To assess possible impacts on sensitive ecosystems and their services in the Lower Mekong River Basin (LMB), we examine spatial and temporal changes in forest community extent, as well as tree density and canopy coverage, in relation to carbon uptake and climate change. We employed the Mann–Kendall test to identify regions of likely significant ecological and environmental change within forest ecosystems. We developed a new approach to eliminate the classification of false positives and false negatives. We quantified the annual rate of ecological changes using Sen’s slope. Ordinary Least Squares (OLS) regression analysis was performed to analyze the impact of tree and leaf density changes on the regional environment, particularly with regard to net primary productivity (NPP) and surface temperature trends. Across the LMB, a total of 2.81% of forest extent has been lost. Cambodia experienced the most prominent forest loss among all countries, while Thailand and Myanmar had net forest gain. NPP trends were starkly divergent from the north to the south and declined across the majority of northern Laos. The LMB region experienced increasing trends for both daytime and nighttime temperatures except for small portions of Thailand and Vietnam. An annual 0.0876°C increase in surface temperature can be expected for every 1% decrease in tree density in forest degraded regions, while a substantial annual carbon productivity increase can be expected in reforested regions. In addition, the effectiveness of policy enforcement at the government level is critical for reforestation in LMB.
Becoming ‘good men’: Teaching consent and masculinity in a single-sex boys’ school

ABSTRACT While consent forms part of the Health, Wellbeing and Relationships strand of the New South Wales (NSW) Personal Development, Health and Physical Education (PDHPE) curriculum in Australia, it is not consistently or effectively delivered within schools. The recent e-campaign, Teach Us Consent in NSW, drew attention to the alarmingly high rates of sexual assault experienced by young women during their school years or shortly after. The campaign called for mandatory, inclusive and comprehensive consent education in schools. This paper presents research from a case study of an elite, same-sex, Catholic boys’ school in NSW, Australia, examining the types of knowledge and attitudes young men and their PDHPE teachers had about sexual consent and the types of consent pedagogies mobilised. Semi-structured, in-depth interviews were conducted with the five male PDHPE teachers, and a focus group was conducted with five Year 11 male students (aged 16–17). The findings highlight that while participants recognised the importance of sexual consent in intimate relationships, consent pedagogies were grounded in individualising discourses of risk, responsibility and care, which rearticulated hegemonic gender norms. Findings from this study contribute to discussions of how schools approach consent education, and the limitations presented by outsourcing relationships and sex education.
TOWARDS MULTILINGUAL EDUCATION: PRE-SERVICE ENGLISH TEACHERS’ PERCEPTIONS OF TRANSLANGUAGING PEDAGOGY

Recent research has shown a paradigm shift towards the rise of translanguaging pedagogy as opposed to the so-called monolingual principles. Translanguaging pedagogy in educational contexts refers to the utilisation of all the linguistic and semiotic resources of students to foster learning. This crosslinguistic approach, thus, challenges language separation and softens the boundaries between languages. Translanguaging is also seen as a transformative pedagogy as it empowers multilinguals to embrace their whole repertoires. This present study involved the pre-service English as a Foreign Language (EFL) teachers in an Indonesian private university taking the English language teaching programme designed to prepare them to be future English teachers. This research aimed to investigate their perceptions of translanguaging pedagogy in order to promote reflections in pre-service English teachers regarding their own views of English language teaching. Six pre-service English teachers were invited to participate in a group interview to shed light on future teachers’ reflective stance regarding the potential use of translanguaging pedagogy. The research results demonstrated that the participants held the maximal position, which put a strong emphasis on maximising the use of English. They, however, expressed their interests in the implementation of translanguaging pedagogy in their own classrooms, but solely for a scaffold to learn rather than seeing it as opportunities to create a safe space for students to legitimate their multilingual dynamic practices.
An All-1.8-V-Switch Hybrid Buck–Boost Converter for Li-Battery-Operated PMICs Achieving 95.63% Peak Efficiency Using a 288-m DCR Inductor

In this article, a hybrid step-up/down converter is presented with improved efficiency, smaller form factor, and enhanced reliability for Li-ion-battery-operated power management integrated circuits. Utilizing a hybrid structure with one inductor and two flying capacitors, the dual-path buck–boost (DPBB) converter reduces the average inductor current, switch currents, and the required blocking voltage for the power switches. To support a mid-3-V output from a Li-ion battery, the converter can be realized using 1.8-V rated devices, solely. In addition, the DPBB facilitates a continuous input and output current delivery, thus improving the reliability of the converter significantly. Besides, leveraging the Li-ion battery charge profile, the topology provides an optimal performance during downconversion, in which the converter operates for most of its usage time. With reduced inductor current and smaller voltage rated switches, a more compact inductor with a large dc resistance (DCR) can be used to maintain similar efficiency, which, in turn, helps reduce the converter board space and associated manufacturing cost. Fabricated in a 180-nm BCD technology, the prototype achieves a peak efficiency of 95.63% even for a large 288-m$\Omega$ inductor DCR with an output ripple below 20 mV for a load current up to 700 mA.
Asymptotic behaviour methods for the Heat Equation. Convergence to the Gaussian

In this expository work we discuss the asymptotic behaviour of the solutions of the classical heat equation posed in the whole Euclidean space. After an introductory review of the main facts on the existence and properties of solutions, we proceed with the proofs of convergence to the Gaussian fundamental solution, a result that holds for all integrable solutions, and represents in the PDE setting the Central Limit Theorem of probability. We present several methods of proof: first, the scaling method. Then several versions of the representation method. This is followed by the functional analysis approach that leads to the famous related equations, Fokker-Planck and Ornstein-Uhlenbeck. The analysis of this connection is also given in rather complete form here. Finally, we present the Boltzmann entropy method, coming from kinetic equations. The different methods are interesting because of the possible extension to prove the asymptotic behaviour or stabilization analysis for more general equations, linear or nonlinear. It all depends a lot on the particular features, and only one or some of the methods work in each case.Other settings of the Heat Equation are briefly discussed in Section 9 and a longer mention of results for different equations is done in Section 10.
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features

Speech emotion recognition (SER) is essential for understanding a speaker’s intention. Recently, some groups have attempted to improve SER performance using a bidirectional long short-term memory (BLSTM) to extract features from speech sequences and a self-attention mechanism to focus on the important parts of the speech sequences. SER also benefits from combining the information in speech with text, which can be accomplished automatically using an automatic speech recognizer (ASR), further improving its performance. However, ASR performance deteriorates in the presence of emotion in speech. Although there is a method to improve ASR performance in the presence of emotional speech, it requires the fine-tuning of ASR, which has a high computational cost and leads to the loss of cues important for determining the presence of emotion in speech segments, which can be helpful in SER. To solve these problems, we propose a BLSTM-and-self-attention-based SER method using self-attention weight correction (SAWC) with confidence measures. This method is applied to acoustic and text feature extractors in SER to adjust the importance weights of speech segments and words with a high possibility of ASR error. Our proposed SAWC reduces the importance of words with speech recognition error in the text feature while emphasizing the importance of speech segments containing these words in acoustic features. Our experimental results on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset reveal that our proposed method achieves a weighted average accuracy of 76.6%, outperforming other state-of-the-art methods. Furthermore, we investigated the behavior of our proposed SAWC in each of the feature extractors.