Reviewerly/exHarmony huggingface dataset:
Intraoperative CT Augmentation for Needle-Based Liver Interventions

This paper addresses the need for improved CT-guidance during needle-based liver procedures (i.e., tumor ablation), while reduces the need for contrast agent injection during such interventions. To achieve this objective, we augment the intraoperative CT with the preoperative vascular network deformed to match the current acquisition. First, a neural network learns local image features in a non-contrasted CT image by leveraging the known preoperative vessel tree geometry and topology extracted from a matching contrasted CT image. Then, the augmented CT is generated by fusing the labeled vascular tree and the non-contrasted intraoperative CT. Our method is trained and validated on porcine data, achieving an average dice score of 0.81 on the predicted vessel tree instead of 0.51 when a medical expert segments the non-contrasted CT. In addition, vascular labels can also be transferred to provide additional information. Source code of this work is publicly available at https://github.com/Sidaty1/Intraoperative_CT_augmentation .
Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning

Recently, spatiotemporal graphs have emerged as a concise and elegant manner of representing video clips in an object-centric fashion, and have shown to be useful for downstream tasks such as action recognition. In this work, we investigate the use of latent spatiotemporal graphs to represent a surgical video in terms of the constituent anatomical structures and tools and their evolving properties over time. To build the graphs, we first predict frame-wise graphs using a pre-trained model, then add temporal edges between nodes based on spatial coherence and visual and semantic similarity. Unlike previous approaches, we incorporate long-term temporal edges in our graphs to better model the evolution of the surgical scene and increase robustness to temporary occlusions. We also introduce a novel graph-editing module that incorporates prior knowledge and temporal coherence to correct errors in the graph, enabling improved downstream task performance. Using our graph representations, we evaluate two downstream tasks, critical view of safety prediction and surgical phase recognition, obtaining strong results that demonstrate the quality and flexibility of the learned representations. Code is available at github.com/CAMMA-public/SurgLatentGraph.
IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA C4.5 UNTUK PREDIKSI EVALUASI ANGGOTA SATUAN PENGAMANAN STUDI KASUS PT. YIMM PULOGADUNG

The inability to perform security duties is a problem that has serious consequences for both the company and the members of the security force. In today's increasingly competitive environment, where companies must search for highly competent people, a more sophisticated technical approach is needed to overcome this problem. One of the techniques used in data analysis is data mining. The researchers implemented a classification method using decision trees. Decision trees were chosen because they are one of the most used techniques in data analysis and provide rules that are easy to apply. This study uses the C4.5 algorithm to generate classification rules that decide whether a member of a security unit should be retained in a company. The results of this study showed an accuracy rate of 99.84%, demonstrating excellent ability to predict the incompetence of security unit members. Through this approach, data regarding the incompetence of security force members can be presented in a more professional and effective manner, helping companies make more informed decisions about personnel. their affairs
The Degree of Practicing Leadership Entrepreneurial Skills of Public Secondary School Principals in Jordan and its Relationship to some Variables

This study aimed to find out the degree of leadership entrepreneurial skills practiced by the heads of government secondary school principals in Jordan. The descriptive survey methodology was used, and the study sample reached (115) male and female principals, who were chosen by the random cluster method. To collect study data, a questionnaire was developed, consisting of (58) items, distributed over five domains: strategic vision, initiative, creativity innovation, risk taking, and achievement. The results of the study showed that the degree of entrepreneurial leadership skills among public secondary school principals from their point of view was high, and all tool domains were high. The results indicated that there were no statistically significant differences attributed to the variables: gender, academic qualification, region and years of experience for all domains.
An Mentoring Listening Skills: YouTube Practical Psychology for Enhancing English Competence of Students at University

Listening is one of the language skills that students must master. However, it is difficult for students to master it. Some factors make it difficult for students to master it, and one of them is that the method or media used by lecturers in the teaching and learning process is not suitable or comfortable. This study aimed to determine if the English study program students of BBG University who are taught listening skills through watching YouTube Channel Practical Psychology are more successful than those who are taught listening skills without watching YouTube Channel Practical Psychology. This research employed quantitative methods and a pre-experiment design with a single group: pre-test - post-test. The population of this study was the entire third-semester English department students at BBG University, was 26 students divided into one class. The necessary data was obtained through testing, and the obtained data was analyzed using the N-Gain Score. N-Gain Score was calculated to be 0.41. It indicated a disparity between the students’ listening abilities before being taught via the YouTube Channel Applied Psychology. After they were taught by watching the YouTube Channel Practical Psychology, the difference was 41%. It means that the students’ listening competence increased by 41% after they were taught through watching YouTube Channel Practical Psychology.
Rhythmic lyrics translation: Customizing a pre-trained language model using stacked fine-tuning

Neural machine translation (NMT) is a software that uses neural network techniques to translate text from one language to another. As NMT models are on the rise, the focus is on translating everyday mundane sentences. However, it is also necessary to start paying attention to the translation of domain-specific text, such as lyrics or poetry. For example, even one of the most famous NMT models—Google Translate—failed to give an accurate English translation of a famous Korean nursery rhyme, "Airplane" (비행기). To teach the model to retain specific information other than semantics, we need specific data which contains the exact information that we are attempting to teach. In the case of rhythmically accurate lyrics translation—translated lyrics that can be used to sing along to the original melody—we need corresponding data, containing lyrical and rhythmical properties, all the while being semantically accurate. However, as there is not enough data that fits our criteria, we propose a novel method we call 'stacked fine-tuning'. We fine-tuned a pre-trained model first with a dataset from the lyrics domain, and then with a smaller dataset containing the rhythmical properties, to teach the model to translate rhythmically accurate lyrics. To evaluate the effectiveness of our approach, we translated two famous Korean nursery rhymes to English and matched them to the original melody. Our stacked fine-tuning method resulted in an NMT model that could maintain the rhythmical characteristics of lyrics during translation while single fine-tuned models failed to do so.
Collaboration beats heterogeneity: Improving federated learning-based waste classification

Classifying waste is a simple but useful exercise to protect the environment. Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. As we attempted to make our very own model to address waste classification, we recognized a common issue that other researchers reported as well: the lack of training data. This is an especially big problem when attempting to train a model for a task that has not been commonly explored before. Further research led us to federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. However, instead of sending the data from the clients to the server for training, FL trains the model on the clients’ machines using the clients’ data, and then aggregates the trained models into the server’s model to protect the clients’ private information. This means that there is a higher chance that the data used to train local models are heterogeneous, decreasing the overall performance. To overcome this issue, we ran a multitude of tests to determine how to maximize performance. For these tests, we hypothesized that the impact of data heterogeneity on an FL framework can be diminished through increasing the client participation ratio. Then, we measured the accuracies of the models trained with varying data heterogeneity, participation ratio, and the number of clients. From the results, we discovered that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.
EKSTRAKSI CITRA PADA PROSES KEAMANAN KRIPTOGRAFI MEMANFAATKAN ALGORITMA SECURE HASH (SHA)

The development of increasingly sophisticated information and communication technology is inseparable from the growing importance of long distance communication. Current information exchange is not only in the form of text, but can also be in the form of images. All that can be done using an internet connection. With this internet connection, you can connect with many people. Cryptography aims to prevent images from being seen by unauthorized persons so that information stored on a computer is safe or sent through an internet connection. And can protect the confidentiality of the image from various threats that arise. SHA-256 is one of the hashing functions that is safe enough to be used to transform a byte of data into a hash string. The system built has been able to implement the SHA 256 algorithm which can change the original image file in the form of unknown files and encrypted images that cannot be known, unless read using an application that has been built. the image when encrypted and reused when decrypted with as many as 45 characters and may be in the form of numbers or letters. The SHA 256 process when encrypted is only one cycle, can be played with decryption.
KEAMANAN CITRA DIGITAL DENGAN MEMANFAATKAN PROSES PENERAPAN ALGORITMA DATA ENCRYPTION STANDART (DES) PADA EKTRAKSI PIXEL

In today's digital technology, almost all activities can be documented in the form of digital images. The problem that arises is that images that are confidential can be stolen and accessed by people who are not entitled. To overcome this security problem, image files can be encrypted using cryptographic algorithms. One cryptographic algorithm that can be used is the Data Encryption Standard (DES) algorithm. This algorithm is a block cipher with a size of 64 bits or 8 bytes. Therefore, DES is used to encrypt image files per 8 bytes. Each encryption process will pass 16 cycles and produce cipher bytes. The result of encryption is an image file with an unrecognized format and cannot be opened because the contents of the byte file have been randomized. To return the scrambled file back to the original image file, perform the decryption process using the same key as the encryption key. The application can be used to encrypt and decrypt images using the DES algorithm. The application can also display the steps of the encryption and decryption process towards the contents of the byte in the file.
Perbandingan Metode Pembelajaran Kontekstual dengan Resitasi terhadap Prestasi Belajar Siswa Pada Mata Pelajaran PAI di Kelas VIII SMP NU Dukuhjati Krangkeng Indramayu

The formulation of the problem underlying this research is as follows: a) How is the application of contextual learning methods in PAI subjects in class VIII SMP NU Dukuhjati Krangkeng Indramayu? b) How is the application of the recitation learning method in PAI Subjects in class VIII of NU Dukuhjati Middle School Krangkeng Indramayu?, c) What are the student learning outcomes in PAI Subjects in class VIII of NU Dukuhjati Middle School Krangkeng Indramayu?, and d) Between the two methods, Which method has the greatest relationship with students in PAI subjects in class VIII SMP NU Dukuhjati Krangkeng Indramayu? Meanwhile, this research aims to conduct comparative testing experiments on the relationship between contextual learning methods and recitation on student learning achievement in PAI subjects in class VIII SMP NU Dukuhjati Krangkeng Indramayu. This research is included in experimental research and is survey in nature, because the entire number of students in class VIII SMP NU Dukuhjati Krangkeng Indramayu as many as 32 people were sampled. Based on the results of inferential statistical calculations using comparative formulas, it is known that there was an increase in the average score of the experimental class seen from the comparison of the average pretest score of 47.25 while the posttest was 82.87, whereas in the control class the average score of the pretest was 46.87 whereas The posttest score was 61.62, there was no improvement. Furthermore, from the results of independent samples test calculations that the probability value is &lt;0.05 then H0 is rejected, meaning that there is a difference in the increase in student learning outcomes between classes that use the contextual learning method and classes that use the recitation method. Based on the results of the research that has been carried out, several conclusions can be drawn, including: (a) The contextual learning method has a significant effect on increasing the learning achievement of NU Dukuhjati Krangkeng Indramayu Middle School students in class VIII in learning PAI in the odd semester of 2014/2015, and (b) Method Recitation also had a significant effect on improving the learning outcomes of NU Dukuhjati Krangkeng Indramayu Middle School class VIII students in the odd semester of PAI learning in 2014/2015. The different test of the Contextual learning method is significantly different from the Recitation learning method in increasing the learning achievement of NU Dukuhjati Krangkeng Indramayu Middle School students in class VIII in PAI lessons.
ANNIHILATING IDEAL AND EXACT ANNIHILATING IDEAL GRAPH OF RING Z_n

The existence of annihilator in the ring motivates the emergence of studies on Annihilating Ideal and Exact Annihilating Ideal Graphs. The purpose of this research is to describe the characteristics of an (exact) annihilating ideal of ring . The method used in this research is literature study. The results of this study discuss finiteness, adjacency, connectedness, vertices, and types of and . Furthermore, the number of vertices of an Annihilating Ideal Graph is determined by the factorization of . The adjacency of two vertices is determined by the divisibleness of . The results also show that is a subgraph of . can be represented as a union of several complete graphs.
A Survey on AI Chatbots

Abstract: Chatbots powered by artificial intelligence (AI) have become a game-changing technology with a significant impact on a number of industries, including customer service, healthcare, education, and more. This survey paper offers a thorough overview of the development, state, and potential of AI chatbots. This survey's main objective is to look into the many ways that AI chatbots are being used across various sectors. The use of chatbots for customer service, virtual assistants, healthcare diagnosis, language translation, and educational help is covered. Case studies and examples from the real world demonstrate how well chatbots have been incorporated into different fields.
The impact of HR practices and Ethical Leadership on Knowledge Hiding. Is it Organizational Justice or Organizational Support for Creativity that mediates this relationship

This paper aims to investigate the impact of Ethical Leadership and Human Resources Management (HRM) Practices on Knowledge Hiding and the mediating role of Organizational Support for Creativity, and Organizational Justice. A total of 120 professionals from different countries and various levels of seniority completed an online questionnaire a linear and multiple regression test was conducted to analyse hypothesized model. The finding shows that both Ethical Leadership and HRM Practices have direct negative impact and indirect negative impact through Organizational Justice and Organizational Support for Creativity on Knowledge Hiding. The main originality of this study is to suggest to managers to not only adapt Ethical leadership and good implemented HRM Practices in order to decrease Knowledge Hiding, but also make sure to maintain high level of justice, equality, fairness, freedom and creativity support within the organization. Keywords: HRM Practices, Knowledge Hiding, Ethical Leadership, Organizational Support for Creativity, Organizational Justice.
PENERAPAN ALGORITMA TF-IDF DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN BERBASIS ASPEK ULASAN APLIKASI FLIP PADA GOOGLE PLAY STORE

The development of the internet has changed people's lifestyle with the existence of FinTech. One of the popular FinTech innovations is the Flip digital wallet application. In this study, aspect-based sentiment analysis was carried out on Flip user reviews using the naive bayes algorithm. The test results show high accuracy, with an average accuracy of 0.84. The naive bayes algorithm is effective in classifying user reviews based on aspects of speed, security, and cost, with accuracies of 0.80, 0.87, and 0.84, respectively. This research provides important insights for service providers to improve service performance and innovation. The labelling data generated the most sentiment 0 (no sentiment), followed by sentiment 1 (positive) and 2 (negative). Negative sentiments have a high frequency on speed and security aspects, while positive sentiments have a high frequency on cost aspects. Thus, improvements are needed to the security system and speed of the Flip application to increase user satisfaction in these aspects. The naive bayes algorithm can be a useful tool in processing review data on e-wallet applications and similar services.
ANALISIS SENTIMEN KEUANGAN (DATA FIQA AND FINANCIAL PHRASEBANK) MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE

Finance is a very vital sector in a Company and institution because it has a very important strategic role in creating a conducive environment, especially for the improvement of the national economy. Through a combination of FiQA and Financial PhareBank text datasets, an analysis of positive, negative and neutral sentiments related to finance is carried out that can be taken into consideration to make a policy in the financial sector or context in achieving this strategic role. Application of sentiment analysis using hyperparameter tuning in Logistic Regression and Support Vector Machines algorithms, with TF-IDF and Smote weighting on training data. The best model results of 70.70% accuracy on the Support Vector Machine algorithm during model training using training data that is not done Smote class imbalance.
ASPECT-BASED SENTIMENT ANALYSIS TERHADAP ULASAN APLIKASI FLIP MENGGUNAKAN PEMBOBOTAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) DENGAN METODE KLASIFIKASI K-NEAREST NEIGHBORS (K-NN)

The rapid growth of online transactions in Indonesia has increased the demand for efficient interbank transfer solutions. However, the costs associated with such transactions have become a significant obstacle. Flip, a company with a vision to become a global leader in customer satisfaction-driven services, offers a solution to this challenge. This study proposes an aspect-based sentiment analysis method using the K-Nearest Neighbors (K-NN) algorithm to analyze user sentiment on key aspects, namely speed, security, and the cost of using the Flip application. The results of this research provide valuable information that can be used as a basis to provide insights, suggestions and recommendations to businesses, so they can create better solutions and promote optimal user experience. The research results show that the K-NN model has the ability to predict user psychology well in all aspects, with a significant level of accuracy, specifically speed (73.04%), security (86, 05%) and costs (80.11%). In addition, this study also compares two model validation methods: simple data splitting method and K-Fold cross-validation. Although the simple data splitting method has a higher average accuracy, K-fold cross-validation is considered superior as it provides a more accurate and reliable estimate of the overall performance of the model. Sentiment analysis results show that Flip app users tend to give negative feedback on speed and security, while they give positive feedback on cost. Therefore, the main recommendation is that the company PT Fliptech Lentera Inspirasi Pertiwi improves the speed and security aspects to increase user satisfaction with the Flip application. Therefore, this customer-centric service will continue to prioritize user satisfaction as its primary goal.
Evaluating the Performance of Explanation Methods on Ordinal Regression CNN Models

This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.
Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers

Breast ultrasound videos contain richer information than ultrasound images, therefore it is more meaningful to develop video models for this diagnosis task. However, the collection of ultrasound video datasets is much harder. In this paper, we explore the feasibility of enhancing the performance of ultrasound video classification using the static image dataset. To this end, we propose KGA-Net and coherence loss. The KGA-Net adopts both video clips and static images to train the network. The coherence loss uses the feature centers generated by the static images to guide the frame attention in the video model. Our KGA-Net boosts the performance on the public BUSV dataset by a large margin. The visualization results of frame attention prove the explainability of our method. We release the code and model weights in https://github.com/PlayerSAL/KGA-Net .
The Development Path of Disciplinary Characteristics of Specialized Universities in China

In the development of universities, disciplines carry many functions, such as personnel training, scientific research and social services. All kinds of organizations and activities of universities are centered on disciplines and developed around the development of disciplines. As an industrial university, the discipline characteristics are accumulated in the long-term history of running a school, and also created in the service of economic and social development, which is different from other universities. Based on the analysis of the influencing factors of discipline development in industrial universities, this paper holds that industrial universities should persist in optimizing discipline layout, concise discipline direction, take the road of characteristic development and win by comparison, which is also the only way to build a "double-first-class" world-class university.