Reviewerly/exHarmony huggingface dataset:
Design and Build a Website-Based Mechanic Information System in Bangkinang City (Analysis System)

In this day and age, information technology is increasingly developing and supporting the presentation of information quickly and precisely. It can be known that in the business world, information systems have become an important part of a company and organization. Therefore, a company and an organization are very dependent on the information and knowledge possessed, because with the strength of a company and organization we can see. With the existence of information systems in various fields of companies and other industries, making a company / industry become developed. An example of one of the businesses in the automotive sector, such as a workshop, can develop by using an information system as one of its business strategies. The increasing number of motor vehicle users today, competition in business the workshops are also increasing. Such as service facilities that come to the place where vehicle users are located to provide ease of use of service facilities through the creation of web-based applications.
Enhancing Math Word Problem Solving Through Salient Clue Prioritization: A Joint Token-Phrase-Level Feature Integration Approach

Recently, deep learning-based methods, such as sequence-to-sequence and sequence-to-tree models, have shown remarkable success in Math Word Problem (MWP) solving. However, a predominant drawback of them is their insufficient attention to the inherent characteristics of math problems, including the varying significance of words within the problem. To address this issue, our paper introduces a novel approach termed “Salient Clue Prioritization (SCP)” for MWP. This approach revolves around understanding MWP from a fresh perspective by emphasizing salient clues. Our proposed approach involves two key training procedures: integrating a coarse-fine representation for multi-level comprehension during the first training phase, and dynamically adjusting attention weights based on identified keywords in the second training phase. Through extensive experimentation, our approach overcomes the limitations of existing methods by effectively identifying and adaptively utilizing salient clues to solve MWP. Our results indicate superior performance compared to other existing models, demonstrating the enhanced understanding of both the MWP itself and the relationship between words and final mathematical solutions.
Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment

Super-Resolution (SR) algorithms aim to enhance the resolutions of images. Massive deep-learning-based SR techniques have emerged in recent years. In such case, a visually appealing output may contain additional details compared with its reference image. Accordingly, fully referenced Image Quality Assessment (IQA) cannot work well; however, reference information remains essential for evaluating the qualities of SR images. This poses a challenge to SR-IQA: How to balance the referenced and no-reference scores for user perception? In this paper, we propose a Perception-driven Similarity-Clarity Tradeoff (PSCT) model for SR-IQA. Specifically, we investigate this problem from both referenced and no-reference perspectives, and design two deep-learning-based modules to obtain referenced and no-reference scores. We present a theoretical analysis based on Human Visual System (HVS) properties on their tradeoff and also calculate adaptive weights for them. Experimental results indicate that our PSCT model is superior to the state-of-the-arts on SR-IQA. In addition, the proposed PSCT model is also capable of evaluating quality scores in other image enhancement scenarios, such as deraining, dehazing and underwater image enhancement. The source code is available at https://github.com/kekezhang112/PSCT.
Fuzzy Dynamic Modelling for Accurate Control of Uncertain Mechanical Systems

As mechanical systems become more complex, achieving precise control becomes increasingly challenging due to uncertainty. This study presents a fuzzy dynamics-based strategy for precise control of uncertain mechanical systems and investigates the use of robust control theory to assess system performance and stability under constraints. Fuzzy theory enables uncertainties to be addressed, resulting in a more precise description of system behaviour. The study findings demonstrate that utilising the constraint invariant dynamics analysis method led to a decrease in control input amplitude, resulting in an average total input U reduction of approximately 60 volts and improving system stability. The constraint invariant dynamics analysis method led to an average reduction of 40 volts in u1 amplitude and an average position error of 1 mm under motor control. The experimentation undertaken on the permanent magnet synchronous motor angular trajectory exhibits that each test was successful in following the anticipated path. The average angular discrepancies between experiments A, B, C, and D were 0.5, 1, 0.3, and 0.8 degrees respectively. The experimental trajectories for A and B occasionally surpassed the upper limit, while C and D remained consistently within the upper and lower bounds. The implementation of the state-dependent control strategy resulted in a 10% reduction in standard deviation of current fluctuation on average, further enhancing the stability and efficiency of the motor system. The research results are expected to provide more stable and efficient control solutions for a wide range of industrial and engineering applications, thereby making a positive contribution to sustainable development and technological progress in society.
Discriminative and Robust Autoencoders for Unsupervised Feature Selection

Many recent research works on unsupervised feature selection (UFS) have focused on how to exploit autoencoders (AEs) to seek informative features. However, existing methods typically employ the squared error to estimate the data reconstruction, which amplifies the negative effect of outliers and can lead to performance degradation. Moreover, traditional AEs aim to extract latent features that capture intrinsic information of the data for accurate data recovery. Without incorporating explicit cluster structure-detecting objectives into the training criterion, AEs fail to capture the latent cluster structure of the data which is essential for identifying discriminative features. Thus, the selected features lack strong discriminative power. To address the issues, we propose to jointly perform robust feature selection and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -means clustering in a unified framework. Concretely, we exploit an AE with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$</tex-math> </inline-formula> -norm as a basic model to seek informative features. To improve robustness against outliers, we introduce an adaptive weight vector for the data reconstruction terms of AE, which assigns smaller weights to the data with larger errors to automatically reduce the influence of the outliers, and larger weights to the data with smaller errors to strengthen the influence of clean data. To enhance the discriminative power of the selected features, we incorporate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -means clustering into the representation learning of the AE. This allows the AE to continually explore cluster structure information, which can be used to discover more discriminative features. Then, we also present an efficient approach to solve the objective of the corresponding problem. Extensive experiments on various benchmark datasets are provided, which clearly demonstrate that the proposed method outperforms state-of-the-art methods.
dAn-oNo: Learning Environment for Data Journalists Teaching Data Analytics Principles

Several journalistic abilities are required to derive narratives from data, including the skill to discover and construct compelling stories (data storytelling), employ data-driven techniques to research and analyze information (data literacy), utilize visualization methods effectively (visualization literacy), and approach data with a combination of creativity and critical thinking. Despite their expertise in journalism, journalists often encounter challenges in comprehending and utilizing novel visual representations or understanding data analysis methods. The main objective of the dAn-oNo learning environment is to guide journalists through the data analytics process by removing coding hurdles and allowing them to experiment with and understand the code. The learning environment utilizes a Jupyter notebook with Markdown sections. It incorporates a step-by-step approach, covering various stages of the data analytics workflow, such as data importing, inspection, statistical analysis, and in-depth analysis. The learning environment also includes an automated profiler that translates warnings and information into human-understandable insights. The design and implementation of the dAn-oNo learning environment were informed by a literature review, user research, interviews with Austrian data journalists, a phase of exploring different technical possibilities for the learning environment, and rapid prototyping. We also report on the results of a preliminary study with students. The prototype is accessible here: https://github.com/stemrich/SEVA_DA-Onboarding-Tool
Multimodal Consistency Co-Assisted Training for Person Re-Identification

Person Re-identification is a popular research area in computer vision, which is mainly used to solve the problem of matching target persons in cross-camera pedestrian image gallery. Traditional methods typically fuse all modal inputs into a fixed feature for subsequent key feature extraction and training, which greatly limits the sensitivity of network learning to independent modal specific information. Meanwhile, the concerns of multiple views on the same data will provide extra regulation. To this end, we propose a Multi Modal Collaborative Fusion Network (MMCF) which consists of three components: Multi Branch Cross Attention (MBCA), Hierarchical Attention Coding (HAC), and Consistency assisted Co training Branch (CCB) MBCA can absorb complementary information from other modalities while retaining unique features of the modality; HAC can embed local key information into global features, thereby improving the expression of pedestrian key features. CCB further improves recognition accuracy through collaborative training between multimodal branches and auxiliary branches. Extensive experiments on RGBNT201 dataset verify the effectiveness of our method and demonstrate superior performance over state of the art pedestrian recognition methods.
A New Dataset and Parsing Model for Chinese Multiparty Dialogue Discourse Structure

With the advancement of Natural Language Pro-cessing (NLP) technology, discourse parsing in multi-party dia-logues has become increasingly significant ina pplications such as machine reading comprehension, machine translation, and text summarization. However, there remains a lack of discourse parsing datasets specifically designed f or Chinese multi-party dialogues, and the fusion of speaker information has not been fully investigated. This research, based on the script of the TV series “I Love My Family”, has constructed a multi-party dialogue discourse parsing dataset named DialogueDSA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> • In this study, we also propose a speaker-aware parsing model for multi-party dialogue discourse. Our method addresses the problem of representing excessively long texts in DialogueDSA by encoding each Elementary Discourse Unit individually, and further interactions are facilitated through the use of BiGRU, attention mechanisms, and Transformer. The effectiveness and robustness of the proposed method are empirically demonstrated on both the English Molweni and STAC datasets, as well as the Chinese DialogueDSA dataset.
Peran Dinas Perpustakaan dan Arsip Kabupaten Karo dalam Meningkatkan Minat Baca Anak yang Terdampak Erupsi Gunung Sinabung

This research discusses the role of the Karo Regency Library and Archives Service in increasing interest in reading for children affected by the eruption of Mount Sinabung. This research was conducted with the aim, to find out the role of the Karo Regency Library and Archives Service in increasing reading interest in children affected by the eruption of Mount Sinabung, to find out the role of the library and archives service in socializing libraries to children affected by the eruption of Mount Sinabung, to find out the obstacles faced by the Office of the Republic of Indonesia. Karo Regency Library and Archives in increasing interest in reading for children affected by the eruption of Mount Sinabung. The research method used in this thesis is a descriptive method with a qualitative approach. The data collection technique used is by means of observation, interviews and documentation, while the data analysis technique is carried out in three stages, namely data reduction, data presentation and conclusion drawing. The results of this study are that the role of the Karo Regency Library and Archives Service has been carried out as a public library for children and the community in Karo district, but for children who were affected by the eruption of Mount Sinabung there is not yet. The role of the Karo Regency Library and Archives Service in socializing the Karo Regency Library to children affected by the eruption of Mount Sinabung is still carried out in schools only, however, a special program has not been made for visits to relocation villages. The first obstacle faced by the Karo Regency Library and Archives Service in increasing the reading interest of children affected by the eruption of Mount Sinabung was the first budget, inadequate facilities and low interest in reading.
MTSTR: Multi-task learning for low-resolution scene text recognition via dual attention mechanism and its application in logistics industry

Recognizing texts in images plays an important role in many applications, such as industrial intelligence, robot vision, automatic driving, command assistance, and scene understanding. Although great progress has been achieved in various fields, research on complex systems modeling using text recognition technology requires further attention. To address this, we propose a new end-to-end multi-task learning method, which includes a super-resolution branch (SRB) and a recognition branch. To effectively learn the semantic information of images, we utilize the feature pyramid network (FPN) to fuse high- and low-level semantic information. The feature map generated by FPN is then delivered separately to the super-resolution branch and the recognition branch. We introduce a novel super-resolution branch, the SRB based on the proposed dual attention mechanism (DAM), designed to enhance the capability of learning low-resolution text features. The DAM incorporates the residual channel attention to enhance channel dependencies and the character attention module to focus on context information. For the recognition branch, the feature map generated by FPN is fed into an RNN sequence module, and an attention-based decoder is constructed to predict the results. To address the issue of low-resolution text recognition in numerous Chinese scenes, we propose the Chinese super-resolution datasets instead of relying on traditional down-sampling techniques to generate training datasets. Experiments demonstrate that the proposed method performs robustly on low-resolution text images and achieves competitive results on benchmark datasets.
Estimating Circular Economic Potential of Organic Fraction of Municipal Solid Waste in Small City

The current waste management paradigm leads to a circular economic approach. To implement it, it is necessary to know the potential for resource recovery from waste, including organic fraction of municipal solid waste (OFMSW). This research aims to investigate the potential for resource recovery from OFMSW generated in Buleleng Regency, Bali Province, Indonesia. Five technologies were assessed for their potential to transform OFMSW into resources, namely anaerobic decomposition (AD), densification and drying to produce solid fuel, composting, processing with black soldier fly (BSF), and processing for eco-enzymes production. The potential for resource recovery is estimated using a simple linear relationship mathematical model using data available from the literature and secondary data of waste generation in Buleleng Regency. The study estimated 37,489.08 tons OFMSW is generated in Buleleng in 2023. The estimate shows that revenue potential from densification and drying about 9,336 million IDR, followe by composting about 2,471 million IDR, anaerobic digestion about 1,939 million IDR, BSF about 145 million IDR, and eco-enzyme about 13 million IDR. Finally, by estimating the quantity of resources available in OFMSW and their potential market value, it can be taken into consideration in planning and managing the circularity of OFMSW.
No-reference quality assessment for depth image based rendering-synthesized images based on multi-feature fusion

The depth image based rendering (DIBR) technique plays a crucial role in free viewpoint video. However, the DIBR technology is not yet mature and still needs to be improved. In addition, depth image acquisition is inaccurate and prone to distortion, which affects human visual perception. The DIBR-synthesized image quality assessment (IQA) method, which simulates the human visual perception system, can be used as feedback to improve the DIBR technique and depth video quality. The existing IQA algorithms are insensitive to the diversity of geometric distortion produced by the DIBR technique and do not consider the impact of geometric distortion on the image quality from multiple perspectives. In this work, a no-reference IQA method for DIBR-synthesized images based on multi-feature fusion is proposed. Different from the existing IQA algorithms, this paper focuses on the diversity of geometric distortions in DIBR-synthesized images, measures the distortions of the edge region and the hole region, and integrates the global sharpness and the natural mixed distortion. Specifically, the global sharpness is first measured, and the image is divided into blocks to further improve the accuracy of the results. Secondly, the geometric distortions of the edge and hole regions are measured comprehensively. In addition, considering the impact of the natural mixed distortion on the quality of the DIBR-synthesized image, a Gaussian mixed statistical model is used to extract the natural mixed distortion of the DIBR-synthesized image. Finally, the quality score of the DIBR-synthesized image is generated by pooling regression. Experimental results on two synthesized image databases demonstrate that the method proposed in this paper outperforms classical and state-of-the-art IQA methods and is more aligned with the human visual perception system.
Camera Motion Estimation Method using Depth-Normalized Criterion

For translationally moving objects with fixed cameras, such as robots and cars, blurring can often be more pronounced in objects that are closer to the camera. A depth-normalized, least-squares objective function is proposed for the simultaneous recovery of shape and motion parameters from optical flow, together with an efficient iterative optimization algorithm. Simulation and experiments demonstrate that for scenes with sufficient depth variation, our algorithm provides robust, statistically consistent estimates of shape and motion.
Try and Fail, But Never Fail to Try – A Study on the NFT Investments Through the Theoretical Lens of Trying

Non-fungible token (NFT) trading has gained prominence; hence, determining whether people are interested in investing in NFTs is critical. The current study expands on the theory of trying by considering two significant elements when discussing adopting new technology in emerging economies: general self-confidence and cynicism. Primary data was collected from 281 respondents who had experience exploring NFT marketplaces. Structural equation modeling was applied to analyze data collected through structured questionnaires. The findings reveal that NFT investments are determined by trying attitude toward NFTs, which in turn is determined by attitude toward success and attitude toward failure. Both attitudes are significantly influenced by general self-confidence and cynicism. However, the attitude towards the process was found to be insignificant. The study can significantly contribute to investment strategies, providing essential guidelines to facilitate NFT exchange and allowing investors to diversify their portfolios.
Energy Profiling and Potential Energy Saving of a Keropok Lekor Small Industry

Malaysia Statistical Business Register (MSBR) released by the Department of Statistics Malaysia (DOSM) highlighted that the total number of SMEs in Malaysia was 97.2% of the total business establishment in 2020. Keropok lekor is a favourite snack in Malaysia and this small industry blossomed in the area near the ocean fishing industry. This study aimed to conduct an energy profiling study to identify the energy consumption profile, electricity usage analysis, and propose cost-benefit energy conservation measures (ECMs) for a selected keropok lekor small industry in Terengganu, Malaysia. The energy audit starts with a preliminary energy usage analysis by reviewing three years of electricity bills from 2019 to 2021. Then, the field data collection was conducted by measuring the illuminance level, air velocity, and air temperature at the shop area and two manufacturing (manufacturing 1 and 2) spaces. Results of the analysis showed that the freezers and chillers consumed the highest 71.3% of overall electrical usage. Applicable Energy Counter Measures (ECMs) were proposed to the small industry management from no cost to high-cost investment such as energy-saving training programs for the workers, organized and efficient usage of the freezers and chillers, replacing electrical appliances with efficient-energy appliances, retrofitting LED lamps, and installation of solar panels. The cost analysis showed 0.73 years of the pay-back period for LED tubes retrofitting and 6 years for solar panel installation. Overall, the energy profiling study was successfully conducted and applicable ECMs with cost-benefit analysis were also had been proposed to the small industry for effective energy management.
Point cloud based hand gesture recognition using template matching

Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97% . Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images.