Reviewerly/exHarmony huggingface dataset:
Attention Is not Always What You Need: Towards Efficient Classification of Domain-Specific Text

For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of accurate classification of classes at the higher level in the hierarchies is crucial to avoid errors propagating to the lower levels. In the business world, an efficient and explainable ML model is preferred over an expensive black-box model, especially if the performance increase is marginal. A current trend in the Natural Language Processing (NLP) community is towards employing huge pre-trained language models (PLMs) or what is known as self-attention models (e.g., BERT) for almost any kind of NLP task (e.g., question-answering, sentiment analysis, text classification). Despite the widespread use of PLMs and the impressive performance in a broad range of NLP tasks, there is a lack of a clear and well-justified need to as why these models are being employed for domain-specific text classification (TC) tasks, given the monosemic nature of specialized words (i.e., jargon) found in domain-specific text which renders the purpose of contextualized embeddings (e.g., PLMs) futile. In this paper, we compare the accuracies of some state-of-the-art (SOTA) models reported in the literature against a Linear SVM classifier and TFIDF vectorization model on three TC datasets. Results show a comparable performance for the LinearSVM. The findings of this study show that for domain-specific TC tasks, a linear model can provide a comparable, cheap, reproducible, and interpretable alternative to attention-based models.
Real-Time DNI and DHI Prediction Using Weather Information via LGBM

Climate change is happening faster than expected, causing severe problems. Renewable energy is proposed as a solution due to its high environmental benefits and low energy costs. As solar power does not emit air pollutants or greenhouse gasses, this study focuses on the benefits of solar energy and how it is superior to other green energy sources. A problem that comes with solar energy is the difficulty of calculating DNI and DHI in non-capital cities in Korea. Constructing solar power plants and conducting research on solar energy are not accessible outside of developed capital cities. Therefore, this paper analyzes climate characteristics that affect solar energy production and proposes a method to calculate DNI and DHI in non-capital cities efficiently. As independent variables, climate information such as air pressure, wind direction, and wind speed was set, and DNI and DHI were set as dependent variables. LGBM was used as a basic model, and RMSE was calculated. When DNI was the dependent variable, RMSE 37.63 was obtained, and when DHI was the dependent variable, RMSE 200.15 was derived. In addition, through variable importance and correlation analysis, it was found that temperature, wind direction, relative humidity, and wind speed had a high effect on DNI and DHI. This study is significant as it solves the existing problem of the unpredictability of DNI and DHI in Korea through primary climate data while also contributing to the expansion of domestic solar energy research in the future.
Diagnosis of Parkinson’s Disease Through Spiral Drawing Classification via VGG19 and AnoGAN

Millions of people worldwide suffer from Parkinson’s disease, a neurodegenerative disease that comes with symptoms such as tremors and difficulty in bodily function. Around 60,000 new cases arise annually, and yet ironically, no standardized way of diagnosing Parkinson’s exists to this day. To enable a more effective process of medication, therapy, and treatment, early diagnosis of PD through a standardized measure is crucial. Previous attempts to achieve this include the analysis of MRI data, speech signals, and the drawing of spirals. Our objective in the study was to enhance the spiral drawing test by combining a CNN model (VGG19) and an anomaly detection system, AnoGAN. The initial trial with the VGG19 model produced an accuracy score of 94%, higher than that of an existing study which produced an accuracy score of 88%. To further refine this algorithm, we incorporated AnoGAN, which allowed for more effective detection of anomaly data and enabled the process of cross-checking and feedback, increasing the accuracy further. This algorithm produced an anomaly score for each of the spiral drawings in the data set; a larger score indicated a higher likelihood for the data to belong to a PD patient. Though we have not yet established an explicit standard for the minimum anomaly score one must have to be diagnosed of PD, this will be possible once more data is accumulated and trained in the algorithm. Overall, the high accuracy and the standardized nature of this design evince its possible application in hospitals in the future.
Mobile Application for Bidding Durians

Normally, there are intermediaries between durian farmers and the end consumers. As a result, the retail durian price in the market is generally twice the farm price. As durians are perishable and taste best when they are fresh, intermediaries are necessary in the distribution channel. This is because the production volume per single farmer is not large, and reaching the downstream customers would require a well-planned logistics and suitable transportation as well as strong marketing strategies and networking. With the popularization of the Internet, online bidding is suitable for both durian buyers and sellers. This paper presents the design and development of a mobile application to facilitate the process of selling and bidding durians known as BidDurian. A complete prototype testing has been conducted and the prototype performs well. Future enhancement may include the live video streaming auction function and inclusion of data analytics.
Fuzzy Time Series Optimization using Particle Swarm Optimization for Forecasting the Number of Fresh Fruit Bunches (FBB) of Palm Oil

Palm oil is a reliable vegetable oil producer because the oil produced has advantages than oils from other plants. The amount of Fresh Fruit Bunches (FFB) raw material from Palm oil has a significant impact on the palm oil production process. Therefore, we need a method to forecasting the amount of palm oil (FFB). One of the suitable forecasting methods is fuzzy time series (FTS). However, FTS still has shortcomings such as innacurate determination of the interval length. For this reason, we need to optimize FTS interval to get optimal forecasting. This research implements Particle Swarm Optimization as the optimization method, FTS Chen-Hsu as the forecast method, and Mean Absolute Percentage Error (MAPE) as the measurement of error. The optimization result using PSO produce an error value of 2.0262% smaller than FTS 3.7108%.
HUBUNGAN KEPERCAYAAN DIRI SISWA DENGAN KETERAMPILAN BERBICARA BAHASA INGGRIS SISWA KELAS IV DI SDN CIPONDOH 8

The relationship between students' self-confidence and the English speaking skills of grade IV students at SDN Cipondoh 8. This study aims to determine the relationship between students' self-confidence and the English speaking skills of grade IV students at SDN Cipondoh 8. This research is a quantitative study using a survey method. The population in this study were the fourth grade students at SDN Cipondoh 8 in 2 classes with a total of 52 students. The sample in this study were 20 students using the Random Sampling Technique. Data collection was carried out using an instrument in the form of a self-confidence questionnaire with a Likert scale model and an English speaking skill test. Data analysis in this study used simple linear regression. The results of this study indicate that there is a positive and significant relationship between students' self-confidence and the English speaking skills of fourth-grade students at SDN Cipondoh 8, with an r count or Pearson correlation of 0.530 with a significance value of 0.016, which means that if students' self-confidence is higher, the the better the English speaking skills of grade IV students at SDN Cipondoh 8, and vice versa. This is also evidenced by the coefficient of determination obtained which is equal to 28.1% and the remaining 71.9% is influenced by other factors, such as interest, motivation, etc.&#x0D;
Domain migration representation learning for blind magnetic resonance image super-resolution

Recently, magnetic resonance (MR) imaging has been widely used in clinical treatment to assist doctors in diagnosis because of its high resolution of soft tissue and no radiation. However, limited by imaging equipment and imaging time, the resolution of MR imaging is low. The previous super-resolution algorithms used for MR images use paired datasets to generate down-sampled images by artificially assuming the image degradation process, and forming a sample pair to monitor and train the super-resolution network. However, the super-resolution network obtained by this way will cause serious degradation of the network performance in practical application. In this paper, we proposed DMN, a blind super-resolution framework based on domain migration, which decouples the blind super-resolution network with the idea of domain migration, and avoids the false detail generation problem of the blind super-resolution network in the MR images super-resolution task through independent training. At the same time, we propose a multi-level parallel transformer feature enhancement module (MPTB), which takes noise as the original input and embeds noise features step by step to enhance the fitting ability of the network while reducing the complexity of the network structure design. Besides, we also use the k-space loss function to form a constraint function, so that the domain migration network can fully consider the k-space characteristics of MR images. The experimental results on the MR images of the knee joint and brain in the FastMRI dataset show that the super-resolution performance of our proposed DMN network is better than the previous super-resolution networks.
Context Aware Document Binarization and Its Application to Information Extraction from Structured Documents

Document binarization plays a key role in information extraction pipelines from document images. In this paper, we propose a robust binarization algorithm that aims at obtaining highly accurate binary maps with reduced computational burden. The proposed technique exploits the effectiveness of the classic Sauvola thresholding set within a DNN environment. Our model learns to combine multi-scale Sauvola thresholds using a featurewise attention module that exploits the visual context of each pixel. The resulting binarization map is further enhanced by a spatial error concealment procedure to recover missing or severely degraded visual information. Moreover, we propose to employ an automatic color removal module that is responsible for suppressing any binarization irrelevant information from the image. This is especially important for structured documents, such as payment forms, where colored structures are used for better user experience and readability. The resulting model is compact, explainable and end-to-end trainable. The proposed technique outperforms the state-of-the-art algorithms in terms of binarization accuracy and successfully extracted information rates.
Search for Hyphenated Words in Probabilistic Indices: A Machine Learning Approach

Hyphenated words are one of the most common challenges in historical handwritten documents. For information retrieval, users issue an entire-word query and expect to retrieve all occurrences of this word, including the hyphenated ones. Thus, methods for predicting hyphenated word fragments and joining them must be developed. In this paper, we build upon and extend the work of Vidal and Toselli (2021) based on probabilistic indexing. We propose a new probabilistic framework to merge prefix/suffix word fragments into “combined spots”, searchable through entire-word queries, and assess different techniques to estimate the corresponding relevance (or “spotting”) probabilities. Additionally, we also consider the use of a hyphenation tool to join these text fragments at query time. We discuss the obtained retrieval results and storage cost using either probabilistic indices or plain automatic 1-best transcripts. The results show that it is possible to train a machine-learning system to join prefix/suffix word fragments automatically, with good information retrieval performance and reasonable storage usage.
EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition

Printed mathematical expression recognition (PMER) aims to transcribe a printed mathematical expression image into a structural expression. The task is useful in a wide spectrum of applications, including personalized question recommendation and automatic problem solving. In this paper, we propose a new method named EDSL, shorted for an Encoder-Decoder architecture with Symbol-Level features, to recognize printed mathematical expressions from input images. Its encoder consists of a segmentation module to identify all symbols and their spatial information from the image in an unsupervised manner, and a reconstruction module to recover symbol dependencies after symbol segmentation. Furthermore, we employ a position correction attention mechanism to capture the spatial relationship between symbols, and apply a transformer model to alleviate the negative impact from long output. We conduct extensive experiments on two real datasets to verify the effectiveness and rationality of our proposed EDSL model. The experimental results illustrated that EDSL outperformed state-of-the-art methods by an accuracy margin of $$3.47\%$$ and $$4.04\%$$ in the two datasets, respectively.
SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation

Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of 93.72, 54.39, 84.65 and 98.04 respectively under one billion parameters. The code is made publicly available at: github.com/ayanban011/SwinDocSegmenter .
Generating Personalized Abstract Art Paintings by using People's Life Energy Distribution

Data visualization, which is essentially the visual expression of data through mathematical calculations, is a highly effective method for transferring and understanding data. The right data visualization, selected or developed with data and people in mind, has an important role in improving the quality of conveying the relationships, meanings, information, structures and hidden properties of data to individuals from different fields of expertise and enables them to work together. Data visualization has been a subject that has evolved and progressed in different ways throughout history and has finally reached today's computer technology and conditions. The improved facilities of today's computer such as high computing power has brought data visualization methods to a new horizon. Also the changing definition of the data has expanded the boundaries of the data visualization. Today, data is obtained from many different sources such as; IoT, Embedded systems, Social data, Business data and Real-Life data. Our study is also inspired by the Real-Life. We developed a visualization system based on real-life botanical trees and using humans as data. Throughout history, computer science and other branches of science have used art as a subject in various studies. In our study, we bring science and art together again to develop a way of expressing people through art. Thus, we developed an artistic visualization system that uses the activities of people's lives as data and generates artistic visuals inspired by abstract art paintings. Through this work, we have created an art style with a visualization system that manages to produce artistic visuals while adhering to its mathematical foundations.
Transferability prediction among classification and regression tasks using optimal transport

Abstract Transfer learning is a method for improving generalization performance by training a model for a different task first and then additionally training the pre-learned weights for the target task. However, transferability—the ease with which source task can be effectively transferred to which target task—is often unknown. Existing works proposed methods of measuring the transferability between classification tasks using images and discrete labels, but it cannot be applied to regression tasks. In this work, we investigate transferability among classification and regression tasks, and propose a method for predicting transferability by extending the optimal transport theory. Our transferability prediction model also can be applied to subjective tasks (e.g., aesthetics and memorability), which are usually regression tasks. We show that the appropriate source (pre-training) tasks can be predicted for the chosen target task without conducting actual pre-training and transferring trials. Experimental results demonstrated high prediction accuracy (correlation coefficient of $$\rho =0.791$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ρ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.791</mml:mn> </mml:mrow> </mml:math> ) and a speed improvement of approximately 300 times compared with the above-mentioned greedy approach.
English for Tourism : Peningkatan Kemampuan Komunikasi Bahasa Inggris Bagi Anggota Geopark Youth Forum Kabupaten Lebak

Improving English language communication skills is crucial for members of the Geopark Youth Forum (GYF) in Kabupaten Lebak to face the growing tourism sector. This study aims to conduct a community engagement program entitled "English for Tourism: Enhancing English Communication Skills for Members of the Geopark Youth Forum Kabupaten Lebak" to enhance the quality of tourism services through improved English language proficiency. The community engagement program includes an "English for Tourism" workshop that covers tourism-related vocabulary, daily conversations with tourists, and presentation of tourist attraction Geopark Bayah Dome. The implementation method involves needs analysis, curriculum development, facilitator training, pre-test, and post-test evaluation to measure the participants' progress in language proficiency. The results of the community engagement program indicate a significant improvement in the English language communication skills of GYF participants in Kabupaten Lebak. Pre-test and post-test scores show an average increase of 30 points in English language proficiency, showing the workshop's success in enhancing language skills. Participant evaluations also indicate that the workshop provides tangible benefits, boosting their confidence in speaking English and enhancing their understanding of tourism-related terms. This community engagement program is expected to make a positive contribution to the development of the tourism sector in the region of Kabupaten Lebak. As English communication skills of the participants improved, GYF members can provide better services to foreign tourists, elevate tourists' experiences in the region, and support the development of tourism potential. Furthermore, the successful implementation of this program emphasizes the importance of collaboration between the university and stakeholders in supporting community development and the tourism sector in Kabupaten Lebak.
Feature matching of remote‐sensing images based on bilateral local–global structure consistency

Abstract The goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness.
A real-time Arabic avatar for deaf–mute community using attention mechanism

Abstract Speech-impaired people use Sign Language (SL), an efficient natural form of communication, all over the world. This paper aims to use deep learning technology in the realm of SL translation and identification. In order to ease communication between hearing-impaired and sighted individuals and to enable the social inclusion of hearing-impaired people in their daily lives, it presents a transformer as a neural machine translation model. The article details the creation of a machine translation system that converts Arabic audio and text into Arabic Sign Language (ArSL) automatically. It does this by utilizing an animated character to produce the correct sign for each spoken word. Since Arabic has few resources, it was challenging to obtain an Arabic-Sign dataset, so we created our own Arabic–Arabic sign gloss, which consists of 12,187 pairs, to train the model. We use bidirectional encoder representations from transformers as an embedding layer to interpret input text tokens and represent an appropriate natural language vector space for deep learning models. To represent the structure of each Arabic word, the Ferasa Part-of-Speech Tagging module was used and then the extracted rules from the ArSL structure were applied. This paper shows a detailed description of a natural language translator (for converting an Arabic word sequence into a sequence of signs belonging to the ArSL) and a 2D avatar animation module (for playing back the signs). In our prototype, we train the software-based module using the attention mechanism. The evaluation was carried out in our developed Arabic sentences with the corresponding Arabic gloss. The proposed model achieves promising results and indicates significant improvements to direct communication between hearing and deaf people, with a training accuracy of 94.71% and an 87.04% testing accuracy for Arabic–Arabic sign gloss translation.