Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic Expert System for Analyzing Student Performance

Assessing the effectiveness of academics at various tiers has grown progressively difficult when employing traditional approaches that predominantly depend on numerical ratings to gauge their teaching and research accomplishments. With the indexing of academic performance in international databases using impact indices at various scales, the demand for advanced computer models has arisen. In this study, we suggest employing expert systems that utilize fuzzy logic to tackle the evaluation of educators, even when confronted with imprecise data and uncertainties. As a novel contribution, this research develops a fuzzy logic model and implements an algorithm using Python and Anaconda, which allows for fuzzy logic rule inference and comprehensive analysis. The implementation of the system is done using Python and leveraging Anaconda's data science environment, providing a seamless and efficient development process. The results of this pilot study involve testing and validating the proposed model through a user-friendly graphical interface, providing comprehensive insights based on minimum criteria along with additional explanations. This approach opens up new possibilities for more accurate assessment of academic performance, particularly when dealing with ambiguous data and uncertain conditions.
A method for text-based person search in Vietnamese language based on correlation filtering

Text-based person search that aims to associate pedestrian images with free-form natural language descriptions has recently emerged thanks to its wide range of applications, such as searching for missing people and tracking criminals. While the majority of existing methods focus on English and obtain promising results, text-based person in other languages, including Vietnamese, is still in its infancy due to the different characteristics of the languages. In this paper, we propose a method for person search through queries in Vietnamese. In this method, to tackle the specific characteristics of Vietnamese, a Vietnamese language parser and textual feature extractor have been integrated into a model based on the correlation filtering named SRCF that has been proposed for text-based person search in English [1] which named Correlation Filter For Vietnamese Language (CFFVL). Extensive experiments have been conducted on VNPersonSearch3000, a large-scale dataset for person search in Vietnam, to evaluate the effectiveness of different Vietnamese language parsers and textual feature extractors. The experimental results show that the proposed method outperforms the state-of-the-art method by 18.42% in top-1 and 23.10% in top-5 on the VNPersonSearch3000 dataset.
Efficient Local Coherent Structure Learning via Self-Evolution Bipartite Graph

Dimensionality reduction (DR) targets to learn low-dimensional representations for improving discriminability of data, which is essential for many downstream machine learning tasks, such as image classification, information clustering, etc. Non-Gaussian issue as a long-standing challenge brings many obstacles to the applications of DR methods that established on Gaussian assumption. The mainstream way to address above issue is to explore the local structure of data via graph learning technique, the methods based on which however suffer from a common weakness, that is, exploring locality through pairwise points causes the optimal graph and subspace are difficult to be found, degrades the performance of downstream tasks, and also increases the computation complexity. In this article, we first propose a novel self-evolution bipartite graph (SEBG) that uses anchor points as the landmark of subclasses, and learns anchor-based rather than pairwise relationships for improving the efficiency of locality exploration. In addition, we develop an efficient local coherent structure learning (ELCS) algorithm based on SEBG, which possesses the ability of updating the edges of graph in learned subspace automatically. Finally, we also provide a multivariable iterative optimization algorithm to solve proposed problem with strict theoretical proofs. Extensive experiments have verified the superiorities of the proposed method compared to related SOTA methods in terms of performance and efficiency on several real-world benchmarks and large-scale image datasets with deep features.
Supporting Guided Exploratory Visual Analysis on Time Series Data with Reinforcement Learning

The exploratory visual analysis (EVA) of time series data uses visualization as the main output medium and input interface for exploring new data. However, for users who lack visual analysis expertise, interpreting and manipulating EVA can be challenging. Thus, providing guidance on EVA is necessary and two relevant questions need to be answered. First, how to recommend interesting insights to provide a first glance at data and help develop an exploration goal. Second, how to provide step-by-step EVA suggestions to help identify which parts of the data to explore. In this work, we present a reinforcement learning (RL)-based system, Visail, which generates EVA sequences to guide the exploration of time series data. As a user uploads a time series dataset, Visail can generate step-by-step EVA suggestions, while each step is visualized as an annotated chart combined with textual descriptions. The RL-based algorithm uses exploratory data analysis knowledge to construct the state and action spaces for the agent to imitate human analysis behaviors in data exploration tasks. In this way, the agent learns the strategy of generating coherent EVA sequences through a well-designed network. To evaluate the effectiveness of our system, we conducted an ablation study, a user study, and two case studies. The results of our evaluation suggested that Visail can provide effective guidance on supporting EVA on time series data.
ANALISIS JARINGAN SOSIAL PENGGUNA TWITTER DALAM SKEMA PENYEDIAAN LAPTOP BAGI TOKO KOMPUTER

Twitter is a social media that provides information for its users. Interactions on Twitter may increase the amount of data from its users. Business development cannot be separated from product competition between business actors. One way for companies to win the competition is to know the purchasing power of customers. A product's selling power can be known one way or another through user interaction on Twitter. User-generated content (UGC) results from data on Twitter. UGC data can be used to determine the product supply scheme taken by laptop products in this study. The method used to analyze the interaction is Social Network Analysis (SNA) by comparing the properties of the network. Social network modeling on UGC data was carried out on three laptops: Lenovo v14, Asus ZenBook S, and Acer Swift 3. From the graph visualization, it was found that Acer excels in two properties, namely "Size" and "Average Degree." Meanwhile, the Asus ZenBook S dominates the "Modularity" property.
Innovative stacked liquid crystal panel for automotive display: driver-oriented privacy and passenger accessibility

We propose a switchable privacy display with liquid crystals for co-driver displays (CDDs) in vehicles. The display minimizes driver distraction while allowing the co-driver to enjoy infotainment content. By combining electrically controllable birefringence (ECB) and hybrid-aligned nematic (HAN) cells, we control the viewing angle of the display. Our device achieves an operating angle of 30°, effectively blocking the driver's view while providing a wide viewing angle for the co-driver. It maintains excellent transmittance properties in privacy and public modes, with symmetric luminance characteristics. The versatility of the proposed display extends to various technologies like LCDs, OLEDs, and micro-LEDs. This switchable privacy display has the potential to enhance driver safety and infotainment experiences in vehicles.
FiberNet: A Compact and Efficient Convolutional Neural Network Model for Image Classification

Creating a convolutional neural network (CNN) with a minimal number of trainable parameters can offers benefits across diverse application domains. In our paper, presents FiberNet, an efficient CNN model with reduced trainable parameters that guarantees both high accuracy and swift inference. Through empirical analysis confirming its efficacy, the FiberNet model achieved a remarkable 96.25% accuracy on the sisal dataset, 74.90% on the CIFAR10 dataset, and incorporates a total of 754,345 trainable parameters.
Effect Of Using Online Games And Social Media On Students' Learning Interest At Smk Muhammadiyah Burau, East Luwu District

This study aims to determine the description of the use of online games, social media, student learning interests, the consequences of the use of online games on student learning interests, the influence of social media use on student learning interests, and the impact of using online games and social media on student learning interests in Muhammadiyah Burau Vocational High School (SMK). To achieve this goal, researchers use information collection methods through questionnaires, observation, and documentation. The information obtained from the results of the study was processed by utilizing quantitative analysis to determine the influence of the use of online games and social media on the learning interest of students at Muhammadiyah Burau Vocational High School (SMK). The results of the experiment proved that if the online game variable is in the high category, the social media variable is in the medium category, while the interest in learning students is in the medium category. Based on the results of the experiment, the relationship between product moment variables and learning interest is moderate, while the relationship between social media variables and student learning interest is high. Based on the results of multiple linear regression analysis, it is concluded that there is a negative and significant influence of online games and social media on the interest in training students at Muhammadiyah Burau Vocational High School (SMK).
Education management in times of the COVID-19 pandemic at the Tecnológico Nacional de México Campus Region Carbonífera

The present work explains the development carried out in the Tecnológico Nacional de México Campus Region Carbonífera when migrating from the face-to-face educational modality to virtual. Once the Covid-19 health contingency was controlled, classrooms were adapted to come back to school in a hybrid and staggered manner in order to maintain the educational quality that characterizes the Institution. This was carried out in four stages, 1) dissemination in order to preserve the life of the entire community within the Institution, 2) steps were taken to start virtual classes, 3) the Tecnologico focused on reviewing the infrastructure conditions of both classrooms, laboratories, furniture, equipment and internet broadband, and 4) hybrid return phases were implemented.
Hand Assessment Device Prototype

The assessment done during physiotherapy is a critical step, since it provides information needed to determine the best possible treatment for the patient. Specifically, hands are essential tools for humans; reason for which developing limitations causes a significant drop in patients’ quality of life. As a result, it is necessary to have tests that record patients’ development in quantitative terms, independent of the physiotherapists’ interpretation. Nevertheless, the instruments used for the task possess limitations that lead to errors during the assessment. Hence, the aim of this work was to develop a device capable of measuring the range of motion (ROM) and grip strength, simultaneously. To fulfil the project’s objective, force and position sensors were analysed, resulting in the selection of the FSR400 for strength and P090 for ROM measurements. All sensors were calibrated to obtain the correct units (Newtons and degrees). Subsequently, the interface was developed using NI LabVIEW. This software allows for fast analog data input and processing, using specialised data acquisition devices (DAQ). Tests for adjustability and accuracy were taken to ensure the correct behaviour of the prototype. Accuracies of 81.62% in ROM and 52.47% in grip strength were found, demonstrating its overall functionality and possibility for improvement.
Proper management of patients using doxorubicin: Request for a transthoracic echocardiogram prior to initiating therapy.

383 Background: Certain antineoplastic agents exhibit specific organ toxicity, such as anthracyclines which main adverse effect is cardiotoxicity. Guidelines recommend cardiac assessment prior to initiating treatment. The initial transthoracic echocardiogram (TTE), performed up to 30 days before the first dose, is essential for recognizing the clinical status of the patient and planning the treatment. Consequently, we assessed compliance with the TTE request and implemented interventions to enhance patient safety. Methods: This project aims to achieve continuous improvement through the PDSA tool. We identified low adherence to the TTE request before initiating doxorubicin by retrospectively analyzing medical records from August 2021 to July 2022. We then evaluated the effectiveness of interventions implemented from August 2022 in the Oncology and Hematology units of a large institution in Brazil, which includes private facilities and public partnerships. Results: Between August 2021 and July 2022, a total of 306 patients initiated treatment with Doxorubicin. Among them, 149 (49%) were inadequately managed without undergoing a TTE within 30 days before starting therapy. Of these, 44 (43%) were from the private site, while 105 (51%) were from the public site. The main reasons for this non-compliance were the lack of standardized protocols and knowledge among members of the care team, as well as the patients' lack of understanding and recognition regarding the importance of the test. To address these issues, initial actions were prioritized based on their impact on patient safety. Educational strategies were implemented, including meetings and the creation of alerts with decision support in electronic medical records, from August to November 2022. During this period, 166 medical records were analyzed, revealing 63 cases (38%) of non-conformities. Among these, 16 patients were from the private site, and 47 patients were from the public site. The initial analysis, guided by the implemented actions, indicated a potential shift in the process, highlighting the need for continuous efforts and ongoing effectiveness assessment. Subsequently, between December 2022 and May 2023, an additional 178 medical records were evaluated. This assessment identified 68 cases (38%) of non-compliance, with 16 patients (25%) from the private site and 52 patients (46%) from the public site. Conclusions: Based on the presented results, there is an improvement in adherence to conducting the TTE among patients treated at the private site, while the indicator shows a smaller change in the population treated at the public site. This difference underscores the necessity for a deeper understanding of the challenges and the implementation of tailored actions to address them. Cardiotoxicity is the main anthracycline adverse effect, so interventions must prioritize safe, effective, equitable treatment.
The Impact of Virtual Museum on Learning Achievement: A Meta-Synthesis Study

This meta-synthesis study aims to explore the impact of virtual museum on learning achievement. With the advancements in technology, virtual museum and augmented reality museum applications have gained popularity in the field of education and museum studies. However, there is a need to examine their effectiveness in enhancing learning outcomes. This research reviews examines 17 studies to evaluate their effects on learning achievement. The findings reveal that virtual reality (VR) and augmented reality (AR) are frequently used in science, art, and history museums to support the acquisition of conceptual knowledge. These technologies are commonly employed for purposes such as integrating additional materials with physical exhibits, animating complex events or concepts, and simulating virtual exhibitions and story scenarios. However, the study also highlights some challenges and limitations of these applications. Technical issues, such as hardware requirements and connectivity problems, can hinder the effectiveness of the learning experience. Additionally, the availability of high-quality content and the need for instructional guidance are important factors to consider for successful implementation. Overall, this meta-synthesis study suggests that virtual museum and augmented reality museum applications have a positive influence on learning achievement. It emphasizes the potential of these technologies in enhancing the educational experience and recommends further research in this area to explore their long-term effects and address the identified challenges.
Enhancing Satisfied User Ratio (SUR) Prediction for VMAF Proxy through Video Quality Metrics

In adaptive video streaming, optimizing the selection of representations for the encoding bitrate ladder has a significant impact on the quality and economics of media delivery. An efficient way to select representations for the bitrate ladder of a given clip is to consider the Satisfied User Ratio (SUR) of the perceived quality of consecutive representations. This ensures that only representations with one Just Noticeable Difference (JND) are encoded and streamed by avoiding encoding similar-quality representations. VMAF (Video Multi-method Assessment Fusion) presently stands as the most commonly utilized quality metric for constructing bitrate ladders. Hence, the precise determination of JND-optimal encoding step-sizes for the VMAF proxy holds paramount importance; nevertheless, this task is intricate and can present considerable challenges.In this paper, we evaluate the effectiveness of different Video Quality Metrics (VQMs) in predicting SUR for the VMAF proxy to better capture content-specific characteristics. Our experimental results provide evidence that incorporating VQMs can improve the precision of the SUR prediction for the VMAF proxy. Compared to a state-of-the-art approach that utilizes video complexity metrics, our proposed approach, which incorporates two quality metrics—specifically, VMAF and SSIM calculated at an optimized quantization parameter (QP)—achieves a substantially reduced Mean Absolute Error (MAE) of 1.67. In contrast, the state-of-the-art approach yields an MAE of 2.01. Hence, we recommend using the above quality metrics to improve the accuracy of the SUR prediction for the VMAF proxy.
Heuristics for Supporting Cooperative Dashboard Design

Dashboards are no longer mere static displays of metrics; through functionality such as interaction and storytelling, they have evolved to support analytic and communicative goals like monitoring and reporting. Existing dashboard design guidelines, however, are often unable to account for this expanded scope as they largely focus on best practices for visual design. In contrast, we frame dashboard design as facilitating an analytical conversation: a cooperative, interactive experience where a user may interact with, reason about, or freely query the underlying data. By drawing on established principles of conversational flow and communication, we define the concept of a cooperative dashboard as one that enables a fruitful and productive analytical conversation, and derive a set of 39 dashboard design heuristics to support effective analytical conversations. To assess the utility of this framing, we asked 52 computer science and engineering graduate students to apply our heuristics to critique and design dashboards as part of an ungraded, opt-in homework assignment. Feedback from participants demonstrates that our heuristics surface new reasons dashboards may fail, and encourage a more fluid, supportive, and responsive style of dashboard design. Our approach suggests several compelling directions for future work, including dashboard authoring tools that better anticipate conversational turn-taking, repair, and refinement and extending cooperative principles to other analytical workflows.
Visualizing Large-Scale Spatial Time Series with GeoChron

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.
Automated identification of copy‐move forgery using Hessian and patch feature extraction techniques

Copy-move forgeries are a conceptual sort of image retouching that involves duplicating a portion of an image and moving it to a different position within the original image, whether by modifying the duplicated part or just moving it altogether. To identify cloned portions that have been reproduced into the same digitized image, the suggested hybrid features in this article combine using the Hessian and Raw patch features on gray-level images. Using a suggested model that combines patch features built on key points detected by the Hessian detector on gray-level image, localization of duplicated and pasted portions of the manipulated image were found. After using the combined features in the matching step, the parallelism condition was applied together with the random sample consensus method to eliminate mismatches. Two databases, GRIP and the image manipulation dataset (IMD), were used for the detection and characterization, and the empirical findings show that the approach was successful in achieving an F1 score 100% for the GRIP database. Additionally, with the IMD database, it produced a 92.13% F1 score. The proposed method was also shown to be effective by obtaining high F1 scores in images where noise, JPEG compression, and scaling attacks were applied to make forgery detection difficult.