Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Using Audio Visual Media Toward Students’ Listening Skill at Eleventh Grade SMA N 9 Jambi

Listening skills are techniques that contribute to the ability to absorb information accurately when communicating with others. This type of research was a quasi-experimental study of 133 people. Through a simple random selection procedure, 36 XI IPS 3 students were selected for the control class and 33 XI IPS 1 students were selected for the experimental class. A technique for collecting data by distributing questions to respondents. In this research, the data analysis technique used the Statistical Package for the Social Sciences (SPSS) version 21 for Windows. The results of this research showed that, (1) Students' listening skills in English subjects taught using audio-visual media have an average arithmetic score of 80,44, (2) The average listening skill of students in English subjects taught only by listening media (speakers) was 77,31, (3) Students’ Listening skills in English subjects taught using the audio-visual media were higher than students’ Listening skills in English subjects taught using listening audio (speaker), with a value of Sig. 0.000 &lt; 0.05. The results showed that English students experienced a significant effect in listening skills after learning by video.
Emergence of Social Robot Technology and Its Friction-Free Diffusion in Society: From Engineering Aesthetics and Compatibility Perspective

A social robot utilizes artificial intelligence (AI) technology to engage and interact with humans and several studies have explored the various functionalities of social robots. However, limited research has been conducted on how factors such as aesthetics and compatibility can influence individuals' intention to use social robots. Consequently, the aim of this research study is to address this gap and provide insights in this area. The study adopts the technology acceptance model, theory of planned behavior, and innovation diffusion theory to investigate the cognitive and psychological factors that impact an individual's intention to use social robots. The study uses self-efficacy, and innovativeness as the predictors of compatibility. Also, the study uses personal norms, and social norms as predictors of aesthetics. Taking account of theories and current literature, we developed a conceptual model and validated it using the partial least squares structural equation modeling technique with 372 respondents. The study found that compatibility and aesthetics are important factors impacting individuals’ intentions to use social robots.
Motion Imitation of a Humanoid Robot via Pose Estimation

Due to the complex operation environments, it is with great significance to improve the flexibility, efficiency and usability of robots while reducing the professional requirements and workload of robot deployment. In this paper, we propose a novel motion imitation approach to a humanoid robot based on 3D human pose estimation. First, we present a sparse-tensor-based 3D human pose estimation approach with point cloud pruning, which solves the dependence of 3D pose estimation on 2D results by directly using the quantized RGB-D data. By effective pruning, the memory stability of 3D data can be guaranteed. Second, a simple yet effective motion retargeting method using coordinate transformation and space vector calculation is applied. Finally, experiments demonstrate the accuracy of our proposed human pose estimation method, thus making motion capture less dependent on expensive and complex motion capture devices and the performance of similarity, continuity and stability of motion imitation is verified.
MotionFormer: multi-object tracking with motion information

In this work, we propose MotionFormer, a multi-object tracking method incorporating motion information. MotionFormer is a Transformer-based architecture, which is an attention-based query-key mechanism. A dynamic Kalman filter is proposed to predict the position when the object is occluded and the appearance characteristics are destroyed. TIoU (Tracking Intersection over Union) uses motion to improve the IoU distance measure, making it possible to focus on effective location information around objects. On MOT17 benchmark, MotionFormer achieves 74.9% MOTA.
Introduction to Appsmith

Software development without coding has been the principal of various application development paradigms. There have been several attempts to build commercially variable solutions, like computer-aided software design (CASE) tools, rapid application development (RAD), fourth-generation programming languages (4GL), etc. All of these solutions were aimed at solving the issue of organizational productivity. Several organizations adopted these tools to build products quickly and with adequate quality.
Working with Datasources

Enterprises are creating massive amounts of data on all operating platforms, like cloud infrastructure, on-premises databases, and at-edge devices. All this data can be classified as structured, semi-structured, or unstructured and stored in several types of store, like RDBMS, datalake, NoSQL, time-series DB, and so on. Businesses can solve several challenges and unlock potential value by making this data transparently available beyond the primary stakeholders. However, low-cost development platforms (LCDPs), facilitating citizen development, must be able to read and update the full assortment of company data.
RRFAE-Net: Robust RGB-D Facial Age Estimation Network

Learning age estimation from face images has attracted much attention due to its favorable of various practical applications such as age-invariant identity-related representation. However, most existing facial age estimation methods usually extract age features from the RGB images, making them sensitive to the gender, race, pose and illumination changes. In this paper, we propose an end-to-end multi-feature integrated network for robust RGB-D facial age estimation, which consists of a 2D triple-type characteristic learning net and a 3D depth features leaning net. The triple-type characteristic learning net aims to extensively exploit multiple aging-related information including the gender, race features as well as the preliminary age features from RGB images, while the depth-feature learning net learns the pose and illumination-invariant age-related features from depth images. By incorporating these multi-dimensional feature nets, our proposed integrated network can extract the robust and complementary age features between RGB and depth modalities. Extensive experimental results on the widely used databases clearly demonstrate the effectiveness of our proposed method.
U-PISRNet: A Unet-Shape Palmprint Image Super-Resolution Network

Palmprint has gained significant attention in recent years due to its reliability and uniqueness for biometric recognition. However, most existing palmprint recognition methods focus only feature representation and matching under an assumption that palmprint images are high-quality, while practical palmprint images are usually captured by various cameras under diverse backgrounds, heavily reducing the quality of palmprint images. To address this, in this paper, we propose a Unet-shape palmprint image super-resolution network (U-PISRNet) by learning and recovering multi-scale palmprint-specific characteristics of palmprint images. First, we project the palmprint images into the high-dimensional shallow representation. Then, we employ the transformer-based Unet-shape Encoder-Decoder architecture with skip-connections to simultaneously learn multi-scale local and global semantic features of palmprint images. Lastly, we reconstruct the super-resolution palmprint images with clear palmprint-specific texture and edge characteristics via two convolutional layers with embedding a PixelShuffle. Experimental results on three public palmprint databases clearly show the effectiveness of the proposed palmprint image super-resolution network.
Artificial Intelligence RBC Recognition Program for Schistocyte Screening in Cytopenic Patients

Schistocyte identification is critical for the diagnosis of thrombotic microangiopathies (TMA). It is one of the most common consultative problems for hematologists. The count of schistocytes is poorly defined by automated complete blood count (CBC) machines. Furthermore, morphological evaluation using peripheral blood smear (PBS) staining is time-consuming and exhibits high variability among inexperienced internists. Our aim is to develop and assess the effectiveness of an in-house AI program for recognizing schistocytes. In phase I of this study, we developed an AI program using deep convolutional neural networks trained from schistocyte in blood smear pictures total 1066 pictures of 1066 patients and divided into training set and validation set. In the training set, 10,377 schistocytes were identified, resulting in an accuracy of 0.76977, precision of 0.78487, recall (sensitivity) of 0.97562, and F1-score (harmonic mean of precision and recall) of 0.86991. And in the validation set, 3,564 schistocytes were identified, resulting in an accuracy of 0.68462, precision of 0.73850, recall (sensitivity) of 0.90370, and F1-score of 0.81279. Then, during phase II, PBS from 133 patients from hematology consultation service due to anemia and/or thrombocytopenia were included and scanned by an automated, cell-locating image analysis system. The program's effectiveness was validated by comparing its detection of an increased in schistocytes from patient glass slides (&amp;gt;1%) with the conclusion from at least two out of three hematologists who conducted conventional microscopy readings. The Cohen's kappa agreement between the AI and hematologist readings was 0.583 (p &amp;lt; 0.001), representing moderate agreement. There were 58 slides with schistocytes &amp;gt;1% as determined by AI reading, while 34 PBSs were identified as showing a significant increase in schistocytes by hematologists. The sensitivity and specificity of significantly increased schistocyte detection were 97.1% and 74.7%, respectively. Blood smears with significantly increased schistocytes detected by the program had positive predictive and negative predictive values of 43.1% and 93.3%, respectively. Poikilocytosis from thalassemic red cells were the most misinterpreted. In sum, the developed AI program for schistocyte detection showed high sensitivity and acceptable specificity, especially in high prevalence thalassemia populations, indicating its potential to assist inexperienced clinicians and reduce the need for hematologist consultations. Adding more schistocyte samples could improve the program's specificity and efficiency of TMA diagnosis in the future.
The Implementation of Task-Based Learning to Improve Students’ Ability in Reading Comprehension of Historical Events Through Recount Text

Learning English as a second language requires reading skills. The ability to read helps students gain information, expand their vocabulary, and solve problems better. However, many students need help understanding reading, especially in a second language. As a result, effective approaches are needed to improve students' reading comprehension. This study uses task-based learning methods (TBL) to improve students' understanding of reading historical events through text recount. This research is carried out using qualitative methods and class action research. Checklists of observations, field records, and interviews are the tools used. At SMK Terpadu Ad-Dimyati Bandung, 25 class ten students were involved in this study. Research results show that using TBL to learn to read and understand historical events through text recount has a positive effect. Students better understand reading and are more actively involved in learning activities. Students can use TBL to discover social functions, text structure, and language elements in recount text. In addition, they acquire new vocabulary and improve their ability to understand reading. According to this study, TBL may improve students’ reading comprehension in a second language. To help students understand difficult reading, teachers should use these learning strategies. Using texts that narrate historical events can also help students learn more about history and improve their reading skills.
Key Technologies of Talent Portrait Based on Big Data Analysis

This paper studies a talent comprehensive quality platform based on big data analysis, including: unified data collection layer, which gathers talent data from various data sources to HDFS storage in an adaptive way; At the basic data platform layer, the talent data from the unified data collection layer is analyzed with big data to form a talent comprehensive quality evaluation report, which provides publishing and query; The application display layer displays the talent comprehensive quality evaluation report published or queried by the basic data platform layer. This paper realizes the comprehensive quality evaluation and management of talents, with high accuracy, convenience and security. In addition, this paper proposes a multimodal information fusion method, including image, text, and audio data, to comprehensively describe and predict the characteristics of talents. The experimental results show that the user portrait modeling method based on multi-modal information in this paper has achieved better performance in the talent feature prediction task.
PENGARUH MODEL PEMBELAJARAN TALKING STICK TERHADAP HASIL BELAJAR SISWA KELAS V PADA PEMBELAJARAN TEMA 2 SUBTEMA 1 DI UPTD SD NEGERI 122357 JL.JATI PEMATANG SIANTAR

The purpose of this research was to determine the influence of the Talking Stick Learning Model on the Learning Outcomes of Class V Students. This research method is an experimental method with a Pre-Experimental design type using a One Group Pretest Posttest Design design. The number of samples is all class V students at UPTD SD Negeri 122357 Jl. Jati, totaling 30 students. Data collection was carried out using test and observation techniques. Research data was analyzed using statistical and inferential analysis. Based on the data results, it is known that ttable is 1.699, tcount is 12.6638, significant 0.05. So it can be concluded that there is an influence of the Talking Stick Learning Model on Student Learning Outcomes in Learning Theme 2 Subtheme 1 at UPTD SD Negeri 122357 Jl. Teak Pematang Siantar. This shows that Ha is accepted and H0 is rejected. Based on the results of the data presented, the researcher concluded that the student learning outcomes before being given treatment, all students still had not reached the KKM, namely 30 students (100%) and after being given treatment, student learning outcomes increased, namely 27 students (90% ) has a value above the KKM.
A Comprehensive Study on Exploring the Potential Challenges of Chatbot Research as Future Directions for Advancing Knowledge in Artificial Intelligence Conversation System

Chatbots are a part of artificial intelligence (AI) computer software which can attempt to communicate through text or voice technology. The idea behind Conversation is artificial intelligence that analyzes customer data and responds to it. Contacting customers doing the live chat has become a way to provide true customer services are in many retail environments. Today, human communication tools are often replaced by chat or chat software; this is often an artificial intelligence (AI)-based machine designed to communicate with humans through natural language.. (Adam M et al.). To contribute to the knowledge of this new research area, we present the research in the form of future directions and problems that will be addressed through research interviews. A computer architecture specifically to reoccur a talk with a human user on the global Internet. Social factors and relationships: Chatbots facilitate change and increase social awareness. Key Words: artificial intelligence, chatbots, natural language processing, robots
Did: Dimension-Invariant Disentangling Model for Light Field Spatial Super-Resolution

4D light field (LF) images can be encoded from 3D scenes by LF cameras, have highly coupled spatial and angular information and cannot be directly utilized. To solve this problem, a disentangling mechanism is proposed in the DistgSSR method [1]. Despite delivering encouraging results of DistgSSR, the parameters of different feature extractors in the Distg-Block of DistgSSR are manually tweaked. Moreover, the concatenated features generated by different extractors should be fused by two convolutions, which will consume unnecessary resources. To address this issue, we propose a dimension-invariant disentangling (DiD) model, which is a plug-and-play model and can adaptively allocate the dimension of the features to fit the residual-in-residual structure. The computational complexity, GPU_Memory and FLOPs of our DiD model are further reduced without losing performance compared with the DistgSSR. Furthermore, our DiD model can be adapted to features of different sizes and may be applied to some networks for other tasks. To further improve the reconstruction result, we integrate a multi-stage spectral dimensional Transformer (MST++) into the DiD model to propose the DiD-MST++ for LF super-resolution. Extensive experimental results on five public LF datasets demonstrate that the DiD and DiD-MST++ can outperform state-of-the-art methods in terms of objective and subjective quality.
Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN

Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods
Pengembangan Aplikasi Kamus Bahasa Bima-Inggris-Indonesia Menggunakan Rapid Application Development

The dictionary is one of the solutions for learning about vocabulary and translating it. The use of dictionaries in the form of books is less effective and efficient, so it is necessary to develop electronic dictionaries in the form of dictionary applications available on smartphones. This study aims to develop an Android-based three-language dictionary application, namely Bima, Indonesian, and English, which includes a speech to text feature. The software development method uses the Rapid Application Development (RAD) method. This method has four main stages: requirements planning, design, construction, and cutover. Data collection methods in this research include observation, interviews, documentation, and literature study. The data analysis technique used is qualitative data analysis of data resulting from observations, interviews, and literature studies. The application was built using the Flutter and Codeigniter frameworks. In the final stage of dictionary application development, testing was carried out on the application's functionality using the black box method. The results of the test show that the application runs very well; all buttons and features work as they should after fixing bugs and problems found in the final test before launch.
Oriented Cascade Mask R-CNN for Biomedical Image Segmentation

Biomedical images play a very important role in the diagnosis and treatment of diseases. In recent years, the technology of computational pathology has developed rapidly. It can effectively help doctors diagnose illnesses, which not only reduces the workload, but also achieves remarkable results. How to accurately segment cells in biomedical images is a very basic but also very important work. In order to analyze multi-tissue histology images, we propose a cell segmentation network, called Oriented Cascade Mask R-CNN (Region-CNN). It is a cascade network based on Oriented Mask R-CNN and Cascade Mask R-CNN. The model can simultaneously segment and classify cells in immunohistochemistry (IHC) images, and achieve good results. Oriented Cascade Mask R-CNN is a two-stage instance segmentation model. The whole network can be trained end-to-end and automatically segment cells. On our IHC dataset, the classification accuracy and recall are 85.8% and 86.0% respectively, and the mAP is 78.5%. Compared with Mask R-CNN, the mAP is improved by 4.9%, and the accuracy and recall are respectively improved by 9.5% and 10.2%.
4D Trajectory Planning Based on Fast Marching Square for UAV Teams

Swarms composed of multiple unmanned aerial vehicles (UAVs) are emerging as a key tool in various application fields including aerial surveillance, urban search and rescue, and package delivery. In this context, trajectory planning remains one of the principal concerns regarding complex robotic applications. This paper proposes a 4D path planning algorithm based on the Fast Marching Square (FM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> ) method for multi-UAV teams in high-dimensional 3D environments with multiple obstacles. The 4D-FM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> algorithm integrates a time-dependent speed function within the Fast Marching (FM) framework. The proposed algorithm was tested in a simulated urban scenario and results demonstrate that the algorithm effectively plans safe, optimal solutions to the shortest path problem for UAVs while avoiding obstacles and other drones, even in complex situations. Additionally, the algorithm excels in providing smooth speed profiles for the vehicles. Furthermore, the study also successfully evaluated the effect of various implementation parameters on the algorithm’s outcome, such as the number of concurrent missions and the velocity of the vehicles. Moreover, computational time was kept within acceptable limits, and the method demonstrated overall good performance in terms of air traffic flow. The main contribution of this work is the introduction of a novel 4D trajectory planning algorithm that implicitly incorporates UAV movement and speed and hence the spatio-temporal realization of the path during the planning phase, leveraging the light propagation phenomenon. This approach efficiently addresses the presence of other UAVs and environmental obstacles, thereby preventing collisions and avoiding unnecessary airspace blockages.