Miracle-dz/UniFAR-FTUs huggingface dataset:
DOTA 2 match prediction through deep learning team fight models

Esports are complex computer games that are played competitively. DOTA 2 is one of the most popular esports titles worldwide. Commentators, audiences, and players face tremendous challenges to keep up with events happening during live matches due to a rapidly evolving gameplay across a large virtual arena. This complexity leads to the question of whether esports analytics could detect important events and their subsequent impact on the match. One such important event is team fights, which can often determine the outcome of a match. Despite their significance across strategy, gameplay, and audience experience, team fights remain relatively unexplored in the literature. Their role and potential to support match prediction models are not well understood. This paper presents a novel definition of team fights in DOTA 2 and proposes an algorithm to extract and quantity them for use in match prediction.
Design of an Electrically Small, Folded Dipole Antenna for Wireless Electromyography Systems

An electrically small, folded dipole antenna is proposed for use in wireless electromyography systems with operation frequencies within the ISM band (2.4 GHz to 2.5 GHz). Using miniaturization techniques, full coverage of ISM band is achieved while keeping an electrical size, kr, of 0.65, a physical diameter under 1 inch, and a 2-dimensional design with no added thickness. The proposed antenna design achieves a -10-dB impedance bandwidth of 0.114 GHz covering from 2.391 GHz to 2.505 GHz and has a fractional bandwidth of 4.65%. This achieves full coverage of all channels presented by the ISM band with a minimum radiation efficiency of 98.9%.
Design of a 2D MIMO Radar Antenna for 77 GHz Automotive Application

In this paper, we propose the design of a 2D automotive MIMO Radar antenna. The antenna design composed by 3 transmitter (Tx) and 5 receiver (Rx) antenna elements. The individual Tx/Rx antenna system is a seven-element series fed modified binomial tapered array with side lobe level (SLL) less than 16 dB and 800 azimuth field-of-view (FoV). The 2D virtual array formation of the proposed antenna system is discussed. It is shown that the proposed antenna configuration exhibits both azimuth and elevation angular resolution such as 5.820 and 44.80 rather than its 1D counter part having same number of antenna elements.
Triangulation for Depth Estimation

The perception of depth is essential for the understanding and creation of a 3D world. It can be used in a wide variety of applications such as 3D scene reconstruction and AR but is especially useful in robotics, where information of three dimensions can be used for perception, navigation and planning [1]. Depth can be retrieved by a range of different sensors such as an active range sensor or a camera. While the first can provide highly precise depth information, it is generally more expensive. The camera however has a relative cheap production cost and recent developments in deep learning have made it a viable option to consider for retrieving depth. The task of measuring depth from either a monocular image or stereo images is called depth estimation and is seen as a computer vision task. In this paper I will primarily focus on the theory behind stereo vision, providing a mathematical background of the triangulation process and use this to show the differences and similarities between a metric depth, disparity and inverse depth map.
onMultiobjective Optimization of Sponge City Based on SWMM Model

Qingdao School of Landscape and Forestry Technology, Qingdao, Shandong 26600, China Qingdao Municipal Engineering Design & Research Institute, Qingdao, Shandong 26600, China Quality and Safety Supervision Station of Qingdao Landscaping Project, Qingdao, Shandong 26600, China Qingdao Garden and Forestry Comprehensive Service Center, Qingdao, Shandong 26600, China Qingdao Landscape Architecture Design Planning Co. Ltd, Shandong 26600, China
Evidence of a Warm Absorber that Varies with QPO Phase in the AGN RE J1034+396

A recent observation of the nearby (z=0.042) narrow-line Seyfert 1 galaxy RE J1034+396 on 2007 May 31 showed strong quasi-periodic oscillations (QPOs) in the 0.3-10 keV X-ray flux. We present phase-resolved spectroscopy of this observation, using data obtained by the EPIC PN detector onboard XMM. The "low" phase spectrum, associated with the troughs in the light curve, shows (at >4 sigma confidence level) an absorption edge at 0.86+/-0.05 keV with an absorption depth of 0.3+/-0.1. Ionized oxygen edges are hallmarks of X-ray warm absorbers in Seyfert active galactic nuclei (AGN); the observed edge is consistent with H-like O VIII and implies a column density of N_{OVIII}~3x10^{18} cm^{-2}. The edge is not seen in the "high" phase spectrum associated with the crests in the light curve, suggesting the presence of a warm absorber in the immediate vicinity of the supermassive black hole which periodically obscures the continuum emission. If the QPO arises due to Keplerian orbital motion around the central black hole, the periodic appearance of the O VIII edge would imply a radius of ~9.4(M/[4x10^6 Msun])^{-2/3}(P/[1 hr])^{2/3} r_g for the size of the warm absorber.
Automatic Classification of Russian Learner Errors

Grammatical Error Correction systems are typically evaluated overall, without taking into consideration performance on individual error types because system output is not annotated with respect to error type. We introduce a tool that automatically classifies errors in Russian learner texts. The tool takes an edit pair consisting of the original token(s) and the corresponding replacement and provides a grammatical error category. Manual evaluation of the output reveals that in more than 93% of cases the error categories are judged as correct or acceptable. We apply the tool to carry out a fine-grained evaluation on the performance of two error correction systems for Russian.
A S URVEY OF D EEP C AUSAL M ODELS

The concept of causality plays an important role in human cognition . In the past few decades, causal inference has been well developed in many ﬁelds, such as computer science, medicine, economics, and education. With the advancement of deep learning techniques, it has been increasingly used in causal inference against counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective optimization functions to estimate counterfactual data unbiasedly based on the different optimization methods. This paper focuses on the survey of the deep causal models, and its core contributions are as follows: 1) we provide relevant metrics under multiple treatments and continuous-dose treatment; 2) we incorporate a comprehensive overview of deep causal models from both temporal development and method classiﬁcation perspectives; 3) we assist a detailed and comprehensive classiﬁcation and analysis of relevant datasets and source code.
Tax Avoidance and Tax Risk Management Impacts on Earnings Response Coefficient

This research aims to analyse tax risk management role as a moderating variable in tax avoidance relationships with the earnings response coefficient. The research usesall samples of the non financial multinational companies on Indonesian and Malaysian stock exchanges. The outcome is tax risk management can strengthen the positive effect of tax avoidance on Earnings Response Coefficient. This proves that the tax risk management can be the control from the multinational corporations in handling tax avoidance. Companies that accomplish improved tax risk management can increase pre-tax income transparency that they present, thereby increasing earnings informativeness (ERC).
Density Functional Theory Study of Controllable Optical Absorptions and Magneto-Optical Properties of Magnetic CrI3 Nanoribbons: Implications for Compact 2D Magnetic Devices

A chromium triiodide (CrI 3 ) monolayer has an interesting ferromagnetic ground-state. In this work, we calculate band structures and magnetic moments of tensile-strained and bent zigzag CrI 3 nanoribbons with density functional theory. The edge iodine atoms form flat low-lying conduction bands and couple with chromium atoms ferromagnetically, while the non-edge iodine atoms weakly couple antiferromagnetically. Narrow CrI 3 nanoribbons have two locally stable magnetic moment orientations, namely out-of-plane and in-plane (along the nanoribbon periodic direction) configurations. This enables four magnetization states in CrI 3 nanoribbons, including two out-of-plane ones (up and down) and two in-plane ones (forward and backward along the nanoribbon periodical direction), increasing the operating controllability. Based on the one-dimensional Ising spin chain model, the spin correlation length of the narrow CrI 3 nanoribbon is estimated as about 10 Å at its estimated Curie temperature of 27 Kelvin, which is lower than the measured 45 Kelvin of the monolayer CrI 3 . The optical absorption and magneto-optical properties of CrI 3 nanoribbons are investigated with many-body perturbation GW-BSE (Bethe-Salpeter equation), including magnetic dichroism, Faraday and magneto-optical Kerr effects. The low-energy dark excitons are mainly from transitions between electrons and holes with unlike spins and are non-Frenkel-like, while the bright excitons have mixed spin configurations. The intrinsic lifetime of excitons can be over one nanosecond, suitable for quantum information processes. Tensile strains and bending manifestly modulate the absorption spectra and magneto-optical properties of CrI 3 nanoribbons within a technologically important photon energy-range of ~1.0-2.0 eV. The CrI 3 nanoribbons can be used in 1D or 2D magnetic storage nanodevices, tunable magnetic optoelectronics, and spin-based quantum information controls. the of the nanoribbon in controllable optoelectronics, or 2D nanodevices, and spin-based quantum information processes.
Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies

Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO–RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation.
Ethical decision-making: a culture influenced virtue specific model for multinational corporations

ABSTRACT Multinational corporations face a litany of challenges regarding ethical decision-making as they traverse new variables in each country they operate in. Presented here is a new approach to ethical decision-making research for multinational corporations with the inclusion of moral virtues, national culture, and a feedback mechanism. The new proposed model builds off of the existing work by Trevino’s Person-Situated Interactionist Model. Hofstede’s work on individual national culture characteristics is used to move the conversation forward by explaining the relationships between individual moderators in Trevino’s model and the effect of national culture on them. The new proposed model recommends the inclusion of moral virtues, with honesty and integrity as examples, as individual moderators in the decision-making process based on previous work. It contributes to the literature by introducing moral virtues into traditional ethical decision-making process models and introduces a new variable with a feedback mechanism for future learning. The paper concludes with recommendations for the future direction of research for ethical decision-making models for multinational organizations.
Discrete Dynamic Modeling Analysis of College Students’ Ideological and Political Education Based on Particle Swarm Optimization Algorithm

Today, with the rapid development of the Internet, the new carrier and platform of college students’ ideological and political education innovation need to be improved. In order to improve the timeliness of college students’ ideological and political research, in view of this characteristic, particle algorithm is studied. Combined with the principle of particle algorithm, the core idea of particle algorithm is applied. College students think about their past behaviors and conduct self-evaluation and reflection. On the other hand, it is the competition and cooperation between multiple academic groups. Combined with particle swarm optimization algorithm, through model optimization, deal with the fitness value between the two attributes to optimize the work. Positive and negative excitation measures are introduced in the experimental research, and the particle swarm optimization evaluation function and behavior weighting factor are analyzed to hypothetically describe the working method. At the same time, it is pointed out that the educational working methods should be adjusted in time according to the changes of student groups and individual behaviors, so as to achieve good work results. Research shows that positive incentives are better than no incentives, and the introduction of negative incentives can only prevent college students from becoming negative role models because they cannot give them the best state of consciousness.
Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study

Background Achieving herd immunity through vaccination depends upon the public’s acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread. Objective We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage. Methods We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles. Results The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative). Conclusions Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19.
Application of the Clinical Reasoning Process for the Rehabilitation of a Case with Cervicogenic Pain

Background: The method through which a therapist engages with a patient is known as clinical reasoning. During this process, the therapist gathers data, develops, and tests hypotheses, and then uses this data to determine the best course of action for diagnosis and therapy. It is described as an inferential procedure used by practitioners to gather and assess information and make decisions on the diagnosis and treatment of patient problems. Aim: The aim of this study is to describe the rehabilitation of a solo case with cervical radiculopathy by using clinical reasoning process Method: A case study of typical cervical radiculopathy managed by using clinical reasoning process and advance evidence-based physiotherapy treatment to reduce pain, increase ROM and improve functional ability. Result: After six weeks of treatment, there was a reduction in pain, an increase in range of motion, and a change in disability status as measured by the neck disability index. Conclusion: Finally, this case study states that application of the clinical reasoning process is beneficial for rehabilitation programs.
DeLightLCD: A Deep and Lightweight Network for Loop Closure Detection in LiDAR SLAM

Loop closure detection (LCD) is a critical yet still open technique to enhance the performance of simultaneous localization and mapping (SLAM). In this article, a very deep and lightweight neural network deep and lightweight loop closure detection (DeLightLCD) is proposed to enable efficient LCD. The architecture of the network contains two key modules: 1) a Siamese feature extraction module and 2) a dual-attention-based feature difference module. A very deep but lightweight feature extraction network is designed to extract high-dimensional and discriminative features. The dual-attention-based feature difference network generates feature difference maps to identify the difference between a pair of light detection and ranging (LiDAR) scans. Besides, a loop closure candidate rapid selection method is proposed to extract loop closure candidates from the LiDAR scan sequence. The workflow enables real-time LCD in large-scale environments. This approach was evaluated on the open-source KITTI odometry benchmark and Ford campus datasets. The experimental results show that this method outperforms the state-of-the-art methods. Although the model was trained only on the KITTI dataset, it also demonstrated superior performance on the Ford campus dataset. In particular, the proposed approach is more lightweight and highly efficient than the current existing approaches.
Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Recently, educational institutions faced many challenges. One of these challenges is the huge amount of educational data that can be used to discover new insights that have a significant contribution to students, teachers, and administrators. Nowadays, researchers from numerous domains are very interested in increasing the quality of learning in educational institutions in order to improve student success and learning outcomes. Several studies have been made to predict student achievement at various levels. Most of the previous studies were focused on predicting student performance at graduation time or at the level of a specific course. The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. Moreover, this study aims to identify the most commonly used techniques for predicting the student's academic level. In addition, this study summarized the highest influential features used for predicting the student academic performance where identifying the most influential factors on student’s performance level will help the student as well as the policymakers and will give detailed insights into the problem. Finally, the results showed that the RF and ensemble model were the most accurate models as they outperformed other models in many previous studies. In addition, researchers in previous studies did not agree on whether the admission requirements have a strong relationship with students' achievement or not, indicating the need to address this issue. Moreover, it has been noticed that there are few studies which predict the student academic performance using students’ data in arts and humanities major.
Cross-Corpus Speech Emotion Recognition Based on Transfer Learning and Multi-Loss Dynamic Adjustment

In this paper, we do research on cross-corpus speech emotion recognition (SER), in which the training and testing speech signals come from different speech corpus. The mismatched feature distribution between the training and testing sets makes many classical algorithms unable to achieve better results. To deal with this issue, a transfer learning and multi-loss dynamic adjustment (TLMLDA) algorithm is initiatively proposed in this paper. The proposed algorithm first builds a novel deep network model based on a deep auto-encoder and fully connected layers to improve the representation ability of features. Subsequently, global domain and subdomain adaptive algorithms are jointly adopted to implement features transfer. Finally, dynamic weighting factors are constructed to adjust the contribution of different loss functions to prevent optimization offset of model training, which effectively improve the generalization ability of the whole system. The results of simulation experiments on Berlin, eNTERFACE, and CASIA speech corpora show that the proposed algorithm can achieve excellent recognition results, and it is competitive with most of the state-of-the-art algorithms.