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Let's Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models' Understanding of Sports
(2025-01-01)
Singh, Punit Kumar
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Kumar, Nishant
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Ghosh, Akash
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Pasad, Kunal
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Soni, Khushi
Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. To address this gap, we introduce CultSportQA, a benchmark designed to assess LMs' understanding of traditional sports across 60 countries and 6 continents, encompassing four distinct cultural categories. The dataset features 33,000 multiple-choice questions (MCQs) across text and image modalities, each of which is categorized into three key types: history-based, rule-based, and scenario-based. To evaluate model performance, we employ zero-shot, few-shot, and chain-of-thought (CoT) prompting across a diverse set of Large Language Models (LLMs), Small Language Models (SLMs), and Multimodal Large Language Models (MLMs). By providing a comprehensive multilingual and multicultural sports benchmark, CultSportQA establishes a new standard for assessing AI's ability to understand and reason about traditional sports.
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Local Sigmoid Method: Non-Iterative Deterministic Learning Algorithm for Automatic Model Construction of Neural Network
(2020-01-01)
Alfarozi, Syukron Abu Ishaq
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Pasupa, Kitsuchart
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Sugimoto, Masanori
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Woraratpanya, Kuntpong
A non-iterative learning algorithm for artificial neural networks is an alternative to optimize the neural network parameters with extremely fast convergence time. Extreme learning machine (ELM) is one of the fastest learning algorithms based on a non-iterative method for a single hidden layer feedforward neural network (SLFN) model. ELM uses a randomization technique that requires a large number of hidden nodes to achieve the high accuracy. This leads to a large and complex model, which is slow at the inference time. Previously, we reported analytical incremental learning (AIL) algorithm, which is a compact model and a non-iterative deterministic learning algorithm, to be used as an alternative. However, AIL cannot grow its set of hidden nodes, due to the node saturation problem. Here, we describe a local sigmoid method (LSM) that is also a sufficiently compact model and a non-iterative deterministic learning algorithm to overcome both the ELM randomization and AIL node saturation problems. The LSM algorithm is based on 'divide and conquer' method that divides the dataset into several subsets which are easier to optimize separately. Each subset can be associated with a local segment represented as a hidden node that preserves local information of the subset. This technique helps us to understand the function of each hidden node of the network built. Moreover, we can use such a technique to explain the function of hidden nodes learned by backpropagation, the iterative algorithm. Based on our experimental results, LSM is more accurate than other non-iterative learning algorithms and one of the most compact models.
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Square Wave Quadrature Amplitude Modulation for Visible Light Communication Using Image Sensor
(2019-01-01)
Alfarozi, Syukron Abu Ishaq
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Pasupa, Kitsuchart
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Hashizume, Hiromichi
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Woraratpanya, Kuntpong
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Sugimoto, Masanori
Most visible light communication (VLC) technologies use a light emitting diode (LED) as a data transmitter and a photodiode as a receiver. In this paper, we alternatively focus on the use of an image sensor or camera as a receiver due to its wide availability. However, the successful use of an image sensor mainly depends on the efficiency of the encoder-decoder and the modulation scheme. Thus, this paper proposes a novel modulation scheme based on a square wave signal called a square wave quadrature amplitude modulation (SW-QAM) method. This method can accommodate different camera settings and overcome the problem of LED flicker that is generally sensed by human eyes when the LED frequency is low. At the transmitter side, multiple LEDs can be used to increase the transmission bit rate, while, at the receiver side, a Wiener filter is used as a complementary technique to SW-QAM for solving the light interference phenomenon due to the closeness of one LED to another. Our experimental results show that the proposed SW-QAM scheme can decode symbols very well either the for close or far communication distances, dark or bright lighting conditions, and single or multiple LED points.
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Robust and Unified VLC Decoding System for Square Wave Quadrature Amplitude Modulation Using Deep Learning Approach
(2019-01-01)
Alfarozi, Syukron Abu Ishaq
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Pasupa, Kitsuchart
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Hashizume, Hiromichi
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Woraratpanya, Kuntpong
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Sugimoto, Masanori
We have proposed a square wave quadrature amplitude modulation (SW-QAM) scheme for visible light communication (VLC) using an image sensor in our previous work. Here, we propose a robust and unified system by using a neural decoding method. This method offers essential SW-QAM decoding capabilities, such as LED localization, light interference elimination, and unknown parameter estimation, bundled into a single neural network model. This work makes use of a convolutional neural network (CNN) that has a capability in automatic learning of unknown parameters, especially when it deals with images as an input. The neural decoding method can provide good solutions for two difficult conditions that are not covered by our previous SW-QAM scheme: unfixed LED positions and multiple point spread functions (PSFs) of multiple LEDs. Responding to the above solutions, three recent CNN architectures - VGG, ResNet, and DenseNet - are modified to suit our scheme and other two small CNN architectures - VGG-like and MiniDenseNet - are proposed for low computing devices. Our experimental results show that the proposed neural decoding method performs better in terms of error rate than the theoretical decoding, an SW-QAM decoder with a $Wiener$ filter, in different scenarios. Furthermore, we experiment on the problem of moving camera, i.e., the unfixed position of LED points. For this case, a spatial transformer network (STN) layer is added to the neural decoding method for solving the moving camera problem, and the method with the new layer achieves a remarkable result.
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LexiPal: Kinect-based application for dyslexia using multisensory approach and natural user interface
(2018-01-01)
Saputra, Muhamad Risqi Utama
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Alfarozi, Syukron Abu Ishaq
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Nugroho, Kuntoro Adi
Considered as an effective learning strategy for dyslexia, multisensory approach demands visual, auditory, and kinesthetic activity. While development of software application implementing multisensory approach has shown promising result, previous applications did not accurately resemble multisensory strategy because there are no kinesthetic implementations. This research proposes a Kinect-based application, termed LexiPal, which incorporates kinesthetic activity in multisensory implementation by using Natural User Interface (NUI). NUI provides more intuitive ways of interacting with the application by using body movement and gesture. LexiPal implements NUI by fusing several technologies including Augmented/Mixed Reality (AR/MR), non-contact human-computer interaction, skeletal tracking, hand tracking, and gesture recognition. For evaluation purpose, LexiPal was tested on 40 dyslexic children and they considered LexiPal user interaction as easy to use and enjoyable, which attract them to play the learning content again in the near future.
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Fractal dimension for classifying 3D brain MRI using improved triangle box-counting method
(2017-02-23)
Kaewaramsri, Yothin
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Alfarozi, Syukron Abu Ishaq
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Woraratpanya, Kuntpong
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Kuroki, Yoshimitsu
Although many papers have used fractal dimension (FD) to analyze magnetic resonance imaging (MRI) for detecting various brain diseases, especially Alzheimer's disease (AD), they have been unsuccessful to classify the AD patients in case of healthy and AD brain-MRIs. The significant problems are from (i) the lack of the efficient FD estimation method and (ii) the failure of applying statistical analysis to discriminate the subjects in MRIs. Therefore, this paper proposes an alternative way to overcome these problems by using an improved triangle box-counting method (ITBC) for effective FD estimation and using machine learning for brain-MRI discrimination. The proposed method is evaluated its performance with the Alzheimer's disease patient discrimination dataset of open access series of imaging studies (OASIS). The experimental results show that the pro-posed method can achieve the classification accuracy rate up to 86.20% whereas the statistical analysis approaches cannot discriminate healthy and AD.
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Hinge loss projection for classification
(2016-01-01)
Alfarozi, Syukron Abu Ishaq
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
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Sugimoto, Masanori
Hinge loss is one-sided function which gives optimal solution than that of squared error (SE) loss function in case of classification. It allows data points which have a value greater than 1 and less than −1 for positive and negative classes, respectively. These have zero contribution to hinge function. However, in the most classification tasks, least square (LS) method such as ridge regression uses SE instead of hinge function. In this paper, a simple projection method is used to minimize hinge loss function through LS methods. We modify the ridge regression and its kernel based version i.e. kernel ridge regression so that it can adopt to hinge function instead of using SE in case of classification problem. The results show the effectiveness of hinge loss projection method especially on imbalanced data sets in terms of geometric mean (GM).
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Analytical incremental learning: Fast constructive learning method for neural network
(2016-01-01)
Alfarozi, Syukron Abu Ishaq
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Setiawan, Noor Akhmad
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Adji, Teguh Bharata
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
Extreme learning machine (ELM) is a fast learning algorithm for single hidden layer feed-forward neural network (SLFN) based on random input weights which usually requires large number of hidden nodes. Recently, novel constructive and destructive parsimonious (CP and DP)-ELM which provide the effectiveness generalization and compact hidden nodes have been proposed. However, the performance might be unstable due to the randomization either in ordinary ELM or CP and DP-ELM. In this study, analytical incremental learning (AIL) algorithm is proposed in which all weights of neural network are calculated analytically without any randomization. The hidden nodes of AIL are incrementally generated based on residual error using least square (LS) method. The results show the effectiveness of AIL which has not only smallest number of hidden nodes and more stable but also good generalization than those of ELM, CP and DP-ELM based on seven benchmark data sets evaluation.

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