Reviewerly/exHarmony huggingface dataset:
Aplikasi Web Prediksi Dampak Gempa di Indonesia Menggunakan Metode Decision Tree dengan Algoritma C4.5

An event or problem sometimes needs to be predicted to determine the impact caused. One of the events that need to be predicted is the impact of the earthquake. The Meteorology, Climatology and Geophysics Agency (BMKG) classifies earthquake impacts based on the BMKG Earthquake Intensity Scale (SIG-BMKG) which consists of 5 scales. In making predictions on a problem, you can use data mining that extracts data into useful information. Grouping the impact of an earthquake is one of the tasks of data mining, namely classification. Prediction can be viewed as a classification that groups data into predefined classes. One classification method is the Decision Tree. This method can handle both categorical and numerical data on large data. Some of the algorithm of the Decision Tree method are ID3, CART, and C4.5. The C4.5 algorithm is an improved ID3 algorithm so that it can handle missing values and continuous data. This study aims to construct a model and analyze the performance of the model obtained using the Decision Tree method with the C4.5 algorithm. In determining the best model, you can utilize Split Validation and k-fold Cross Validation. The best model was obtained in the first iteration of 10-fold Cross Validation. The best model is then used in a web application that can be used by the community to predict the impact of earthquakes that occur in Indonesia.
Chinese Earth Sciences Review

The Yangshan gold deposit in Gansu province is a super large gold deposit in the West Qinling metallogenic belt, with a cumulative gold accumulation of 400 tons. The soil environment conditions in Anba- Getiaowan ore section were investigated and analyzed to provide a reference basis for mine construction, development and treatment. A total of 120 samples (95 horizontal profile samples and 25 vertical profile samples) were collected, taking into account the topography, cover type, soil use type and soil type in the study area, and the distribution characteristics and change patterns of soil heavy metals As, Cd, Hg and Pb in the study area were evaluated sterically from both horizontal and vertical dimensions. Meanwhile, the distribution characteristics of F and Se elements in soil were compared and analyzed. The results of the study showed that: (1) the value of heavy metal elements As exceeded the standard, and the rest of the heavy metal elements were above the second grade of soil quality, among which the exceedance rate of heavy metal elements As in Anba area was 15%, and that in Getiaowan area was 84%; (2) the average value of As, F and Se content was higher than the background value of Longnan City, which was speculated to be significantly related to the presence of high arsenic and high fluorine water in the evaluation of poor quality water in the study area; (3) the excessive content of As element in the soil in the mining area is related to the occurrence of arsenic-containing minerals in gold, and As element pollution problem should be emphasized in the later construction and development of the mine.
Implementasi Media Puzzle Stick pada Minat dan Keterampilan Membaca Permulaan Kelas II Sekolah Dasar

This research aims to determine the results of implementing puzzle stick media on the interest and initial reading skills of class II elementary school students. The research approach is quantitative, quasi-experimental methods with a nonequivalent control group design. The sampling technique used is a saturated sampling technique which uses the entire population as the sample in the research. The data obtained came from questionnaires and tests. Questionnaire sheets are used to collect reading interest data and tests are used to collect data on initial reading skills. This research was analyzed using hypothesis testing, namely the sample t-test using the gain score. The results of data analysis were obtaining the mean of the experimental and control classes using the gain score. In terms of interest, the experimental class obtained an average gain score of 10.90 and 7.15 for the control class. The results of the average gain score for interest in reading show that the experimental class average is 3.75 greater than the control class. Meanwhile, in the initial reading skills data, the average gain score in the experimental class was 19.86 and 11.50 for the control class. The results of the average gain score for initial reading skills show that the experimental class average is 8.36 greater than the control class. For the independent sample t-test hypothesis test results, the gain score is used as follows: (1) The results of data analysis show significant results, as evidenced by the t test results obtained with a t value of 2.083 &gt; t table 2.022 and a sig value of 0.044 &lt; 0 .05 then Ho is rejected and Ha is accepted. Which means, there is a significant increase in interest in reading by implementing the puzzle stick media. (2) The t test results obtained a t value of 4.641 &gt; t table 2.022 and a sig value of 0.000 &lt; 0.05, so Ho was rejected and Ha was accepted. There is a significant increase in beginning reading skills by implementing the stick puzzle media.
Transfer Learning-Based Physical Layer Authentication for Wireless Networks Handover

In dynamic access networks, terminals face frequent network handover which requires authentication handover to ensure the security of network access. Physical layer authentication based on machine learning will cause large computation overhead and communication delay for network. Therefore, this paper designs a fast authentication handover mechanism based on transfer learning, which makes full use of the source network authentication model and adopts transfer learning algorithm to transfer the previously trained model to target network. Target network continues to train on the basis of the model trained by source network, which simplifies the process of retraining authentication model during network handover greatly.
Sports Assistant: Similarity Calculation for Sports Activity

When participating in general sports activities, proper posture and movement are important to improve performance and prevent injury. In this paper, we propose a system to observe sports activity and calculate the difference between two people (for example, beginners and professional layers). The pose difference could help people improve their posture. The system consists of deep learning methods and similarity calculation. The deep learning method will detect human pose and output keyjoints. If the sport requires extra equipments, e.g. baseball bat, the system uses object detection to extract the keyjoints. Next, it calculates the pose similarity between two people. The developed system could help people know whether their posture is correct and which body parts could be improved.
Middleware for Quantum: An orchestration of hybrid quantum-classical systems

Quantum computing has the potential to revolutionize the way we solve complex problems in various fields. Its unique features, such as superposition and entanglement, allow for speedup in certain computations compared to classical computers. In this paper, we show that incorporating quantum computing into a heterogeneous computation architecture is possible and potentially can greatly enhance the overall performance and efficiency of solving a wide range of problems. We propose a heterogeneous computation architecture that integrates quantum computing and classical computing. We describe how this architecture can be implemented using widely available opensource software and provide a reference implementation. We also discuss potential benefits and challenges. Our goal is to highlight the importance of seamless inclusion of quantum computing in the future of computation and how it can be achieved with tools we already have.
Realistic Pedestrian Shadow Generation by 2D-to-3D Object-Lifting

This paper studies the shadow generation problem in an outdoor street scene. Previous methods only rely on GAN-based model with the sun position to reconstruct the street view. Lacking in related dataset, we propose an evaluation method to make sure of the effectiveness. Our model applies a 2D-to-3D lifting method, casts the 3D object with sun estimation, and finally merges the shadow with a predicted sun brightness. Limited with lifting objects, our model casts more precise shadows than GAN-based model. Tuning with the brightness, the cast shadow will be in consistence with the whole street view. Then in our evaluation, we demonstrate better performance over the state-of-the-art models by 0.009 in LPIPS. Therefore, casting with a 3D human object is a feasible solution for shadow generation in the future.
SCU-Net: A Shape-Supervised Contextual-Fusion U-Net for the Dilated Biliary Tree Segmentation

Segmentation of the dilated biliary tree in abdominal CT is very important for the diagnosis and later treatment of biliary diseases. However, heterogeneity, abrupt deformation, and blurred boundary of the dilated biliary tree pose a challenge for current deep learning based methods, which have not been sufficiently studied. This work proposes a shape-supervised contextual-fusion U-Net (SCU-Net) to address this. Specifically, the network adds to the classic U-Net a novel feature fusion module to fuse the coarse-to-fine information while supervising segmentation integrity by the shape-aware distance map. Experiments showed that our model outperforms existing segmentation algorithms, obtaining a dice score of 76.4%, which was 3% higher compared with the advanced segmentation method nnU-Net.
Optical encoding and hiding scheme for a double image based on chaotic fingerprint phase masks and phase-shifting digital holography

This paper proposes a novel, to the best of our knowledge, double-image hiding scheme based on the chaotic fingerprint phase masks (CFPMs) and three-step phase-shifting digital holography (PSDH). First, the two images to be hidden are encoded into a complex amplitude image, and then with the help of the CFPM located in the Fresnel transform (FrT) domain and the three-step PSDH, the complex amplitude image can be encoded into three noise-like interference holograms. Finally, the three noise-like interference holograms are hidden into the texture part of the host image by the discrete wavelet transform based fusion approach and variational image decomposition technique. This scheme can simultaneously hide two images into one host image, and the invisibility and robustness of the hiding scheme can be well balanced by embedding the secret image in the texture of the host image. Additionally, the introduction of a biometric feature increases the association of the key and the authorized user, and the parameters of the chaotic map and FrT can also provide additional security to the proposed scheme. We have verified the scheme's feasibility, security, and robustness through extensive experiments.
Fostering the Development and Implementation of Digital Learning Strategies for Digitization in Schools

In today's fast-paced and rapidly changing world, teachers must continually update their skills and competencies to meet the new requirements of education. As new technologies and teaching methodologies emerge, teachers need to stay abreast of these changes to be effective in the classroom.The paper discusses an approach for improvement of skills and competencies of the adult educators across Europe on how to use digital tools, digital content libraries and technology-enhanced methods, among them the “Episodes of Situated Learning”, to ensure engaging and meaningful virtual instruction in inclusive settings.
Combining Graph Contrastive Embedding and Multi-head Cross-Attention Transfer for Cross-Domain Recommendation

Abstract Cross-domain recommendation (CDR) has become an important research direction in the field of recommender systems due to the increasing demand for personalized recommendations across different domains. However, CDR faces multiple challenges, including data sparsity, popularity bias, and long-tail problems. To address these challenges, we propose a novel framework that combines graph contrastive embedding and multi-head cross-attention transfer for cross-domain recommendation, called GCE-MCAT. Specifically, in the pre-training process, we generate more uniform user and item embeddings through contrastive learning, effectively solving the problem of inconsistent data embedding space distribution and recommendation popularity bias. Moreover, we propose a multi-head cross-attention transfer mechanism that allows the model to extract user common and specific domain features from multiple perspectives and perform cross-domain bidirectional knowledge transfer. Finally, we propose a cross-domain feature fusion mechanism that dynamically assigns weights to common user features and specific domain features. This enables the model to more effectively learn common user interests. We evaluate the proposed framework on three real-world CDR datasets and show that GCE-MCAT consistently and significantly improves recommendation performance compared to state-of-the-art methods. In particular, the proposed framework has demonstrated remarkable effectiveness in addressing long-tail distribution and enhancing recommendation novelty, providing users with more diversified recommendations and reducing popularity bias.
Improving Students’ Writing Skill By Using Mind Mapping Technique At The Tenth Grade Of SMK Swasta Kristen BNKP Luzamanu In 2022/2023

Writing is the important skill that must be master by the students. However, the students had the several problems that caused the Minimum Competence Criterion (MCC) couldn’t achieve by the students they are: the students aren’t able to master the language features, generic structure, and social function of descriptive text; the topic is unfamiliar; the students don’t find interesting and appropriate writing technique; and the students are difficult to generate their ideas. The research aims to improve students’ writing skill in descriptive text by using Mind Mapping Technique at the Tenth Grade of SMK Swasta Kristen BNKP Luzamanu in 2022/2023. The research was conducted by using Classroom Action Research (CAR) and it was applied in two cycles. Each cycle consisted of two meetings and each meeting consisted of planning, acting, observing, and reflecting. Cycle I showed that there were 3 (10,71%) students in excellent category, 3 (10,71%) students in good category, 8 (28,57%) students in average category, 9 (32,14%) students in poor category, and 5 (17,85%) students who classify in very poor category. The result show that most of the students couldn’t pass the MCC. In the Cycle II, it showed that there were 13 students (46,42) who could be classified excellent category and there 15 (53,57) students who could be classified good category. There was no student classified in poor and very poor category”. The average of students’ value was 81. It can be concluded that Mind Mapping Technique improved the students’ writing skill especially in writing descriptive text.
Improving The Students’ Vocabulary Mastery By Using Fly Swatter Game At The Tenth Grade Of Sma Negeri 1 Gunungsitoli Idanoi In 2022/2023

In learning English, the first thing that must be learned is vocabulary. This means that learning vocabulary is very important, especially for students in high school. Based on the researcher observations at the tenth grade, the technique used in learning is monotonous which makes students tend to get bored so that student participation in class is lacking. The vocabulary problem must be solved, because it can make it difficult for students to continue to the next level or class. Also students may have low motivation in learning English because they feel English is difficult. The objectives of the researcher is to improve students vocabulary mastery by using Fly Swatter Game. Based on the identification of the problems above, the researcher wants to overcome the problems of the students in English vocabulary mastery. In this research, the researcher use Classroom Action Research as a research design that was held in two cycles. The subject of the research was the students of X-IPS 1 which consist of 23 students. In this research, the instrument are evaluation sheet and observation sheet. From the result of each cycle, it shown that; in Cycle I, there was not students’ value which could be classified in “good level”, there were 3 students’ value (13,04%) in “good level”, there were 3 students’(13,04%) in “enough level”, there were 7 students’ value (30,43%)in “less level”, there was 10 students’ value (43,47%) in “fail level”. The average of the students’ value was 46,3. It showed that Cycle I was not successful, so this research has continued to the next cycle after revising the plan based on the weaknesses in cycle I. The result of Cycle II research showed that there was not students’ (0%) values classified in the “enough“, “less” and “fail level.” There were 8 students’ values (34,77%) in the “good level”, and 15 students’ values (65,21%) in the “very good level”. The average value of the students’ was 86,5. Based on the result above the researcher concludes that Fly Swatter Game could be done in teaching vocabulary and could improve the students’ ability in mastering vocabulary.
Implementation of the SIWI Learning Strategy to Encourage Deaf Students' Writing Skills At Extraordinary School (SLB) Lamongan

This article focuses on answering questions regarding the SIWI implementation strategy for deaf children in Lamongan. This is because there are deaf children in the Lamongan SLB school who have difficulty receiving teacher instructions so that it is more difficult to learn to write. Given the importance of writing for deaf students, of course the teacher must find a solution, one of which can be using the SIWI strategy to spur deaf students' writing skills. So in this article we will look at the implementation of the SIWI strategy by classroom teachers to improve the writing skills of deaf students. This research is a type of qualitative research using a phenomenological approach. This aims to photograph the SIWI implementation strategy in Lamongan SLB. Data collection in this study used two techniques, namely interviews and field observations. The results of this study indicate the effectiveness of applying the SIWI strategy in class during the learning process at SL Lamongan and indications that SIWI can be applied properly so as to encourage the improvement of deaf students' ability to write.
Dual-Path Framework for Intra-Class Imbalance Medical Image Segmentation

The intra-class imbalance usually occurs in medical images due to external influences, such as noise interference and changes in camera angle. It leads to complex textures and varied appearances within the target object region and makes segmentation task challenging. To deal with this kind of problem, we proposed a dual-path framework in this paper. Considering that the object consists of two subclasses (majority- and minority-subclass), a deep learning model is adopted to separate them. We constructed two weighted maps for the dual paths, related to majority- and minority-subclass respectively. A fusion module was designed to generate the final output according to the results from the dual paths. The experimental results on two datasets shew our approach's validity and superiority for medical image segmentation compared with other competing methods.
TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

With the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain. In this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making. Specifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk. The proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes. The experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.