Reviewerly/exHarmony huggingface dataset:
Penerapan Metode CART Dalam Klasifikasi Jurusan Siswa Baru

SMK Negeri 3 Pontianak is one of the vocational education schools in Pontianak City. Every new academic year SMK Negeri 3 Pontianak accepts around 320 new students. The large number of prospective new students makes the majoring process carried out by the school become less effective and takes a long time to determine majors for new students. With a system that can classify new student majors, it helps in the process of determining student majors. This study uses the Classification and Regression Trees (CART) algorithm for the classification process in determining majors for new students. The assessment indicators used for classification consist of interest, MTK (US) school exam scores, school exam IPA scores, school exam Indonesian language scores, math report cards, science report cards, social science report cards, Indonesian language report cards, and English report cards. Classification of majors at SMK 3 Pontianak consists of accounting, office, marketing, and hospitality majors. The amount of data used is 320 data which is divided into 224 training data and 96 test data. The CART algorithm generates decision trees, rules, and new student majors that have been classified. Based on the test results using the confusion matrix, the system accuracy results are 84.38%.
Strategi Maximum Profit Algoritma Greedy dalam Kecerdasan Buatan Penentu Aktivitas Fisik pada Mobile Exergame

The Covid-19 pandemic has started to subside. However, after the pandemic subsided the obesity rate in Indonesia continued to increase. The new habits formed after the pandemic are activities that can be done at home. This is an opportunity to be able to reduce obesity by doing physical activity at home. Physical activity undertaken specifically to lose weight. However, various problems arise when doing physical activity. Such as requiring expensive fees, monotony, and others. Mobile exergame is one solution to this problem. In previous research, exergames that use the brute force algorithm in determining physical activity take a long time. This problem will be solved using a greedy algorithm which also uses a maximum profit strategy. Descriptive statistics are used to compare the average time needed by the two algorithms to select the physical activity to be performed in an exergame. The results of this study indicate that in terms of time the greedy algorithm that uses the maximum profit strategy is proven to be faster than the brute force algorithm.
Developing Human Hand Gesture Data

Gesture is a movement originating from one of the body's limbs, generally using the hand. The movement is usually used to interact with each other among human beings. In this study, gestures are considered as a way for humans to be able to communicate with a system so that it can understand human body language. The first step to be able to create a system that can recognize body gestures is to create a dataset of the gestures to be used. The process for creating a dataset in this study includes taking pictures from a webcam, then pre-processing the images so that they can display the characteristics of each image. The dataset can then be used to model gesture classification using Convolutional Neural Network (CNN). The collection of gestures in this dataset is carried out on the entire upper body to create data variations of male and female also with various human body shapes. This study has collected 450 images of gesture data and has prepared these data with ration of 80% for training and 20% for testing.
Penerapan Algoritma C4.5 Untuk Analisa Tingkat Keberhasilan Mahasiswa Dalam Pembelajaran Praktikum di Masa Pandemi

Practical learning is a method of learning in which students conduct experiments directly by applying the theories they have learned and proving what they have learned in order to better understand what they have learned. The success rate of students towards practical learning is one of the factors that can improve the quality of higher education and can facilitate lecture activities. To analyze the success rate of students towards practicum learning, they can use the C4.5 algorithm by looking at the highest gain. The C4.5 algorithm is one of the algorithms in data mining whose results are identical to the decision tree. Several parameters used for this research are motivation factor, learning method factor, learning material factor, and facility factor. This research was conducted with the aim of helping universities determine the level of student success in practicum learning using the C4.5 algorithm as much as 200 data. The results of the study showed an accuracy of 82.00% with the facility factor being the highest factor. So that the results obtained can help the campus in improving the learning process.
Sistem Pendukung Keputusan Untuk Menentukan Kelayakan Pemberian Bantuan Usaha Mikro dengan Menggunakan Metode SAW dan SMART

The purpose of this business capital assistance program is to help people develop businesses that were previously affected by the Covid-19 pandemic which caused small businesses such as street vendors and home businesses to experience a decrease in income. This program aims to help small and medium enterprises (MSMEs). Potential beneficiaries are currently still selected through meetings held by village heads, which require a longer decision-making process.Therefore it is very important to create a new system that can be used to assist those who are eligible to receive targeted funds. In this study, a decision support system application was made to make it easier for village heads to determine candidates who are entitled to receive assistance according to the criteria. A decision support system is a solution that can be used to find out the results of the selection of MSME beneficiaries using the SAW and SMART methods. The SAW and SMART methods use 10 alternative data to provide an assessment of the final result by ranking from the highest to the lowest alternative value, showing that the two methods produce data that is accurate and suitable when applied as a ranking. The calculation results generated by the system that has been tested with the results of manual calculations, both show the same results.
Analisa Sentimen Informasi Hoaks Pasca Pandemi Covid-19 dengan Text Mining

In this era of globalization, the delivery of information can be conveyed by any information media, this can also cause the spread of hoax information. This is very detrimental for recipients of information, especially information that is very important to receive and information related to the Covid or Corona virus that has hit the world and Indonesia. Based on data from the Gugus Tugas Percepatan Penanganan Covid-19, in the post-pandemic era 227 hoax information were still found that were identified. Therefore, in this study the authors conducted hoax information research, especially regarding the Covid-19 virus in the post-pandemic era by using the Text Mining method to find out information patterns that often appear in the post-Covid-19 pandemic. This paper will also show some results regarding the association of several words with the main word in hoax information in the post-Covid-19 pandemic era in the form of Term of Frequency, Word Cloud, Fruchterman Reingold Layout, and Circle Layout. The results of this study indicate that in the post-Covid-19 pandemic era, hoax information is frequently associated with ‘vaccines’ and ‘covid’, with 113 and 111 occurrences respectively. This is due to public skepticism regarding the safety and effectiveness of the Covid-19 vaccine, leading to the dissemination of hoaxes aimed at discouraging vaccination. Other commonly mentioned words in post-pandemic hoax information include ‘omicron’, ‘vaccinated’, ‘variant’, ‘pfizer’, ‘mRNA’, ‘virus’, and ‘vaccination’, albeit with lower frequencies
Klasterisasi Peserta BPJS Berdasarkan Rekam Medis Menggunakan Algoritma K-Means

The history of the patient's medical examination with the BPJS (Social Security Administration Agency) at the Dumai City Hospital will be stored in the form of a medical record. The medical record is a file that contains patient identification information whether it contains the results of control, treatment and other services. The purpose of this research is to. to assist the Dumai Hospital in grouping BPJS participants based on medical records to find out the disease, gender and BPJS group that is most dominant in Dumai City BPJS participants. The results of grouping the disease will then be processed using data mining techniques. In this study, the focus was on BPJS PBI participants inpatient medical record data from January to December 2022, which were then processed using the K-Means Clustering algorithm with 3 clusters. Cluster 0 is dominated by disease types with code O342 (Maternal care due to uterine scar from previous surgery), female sex and age range 21-40 years. Cluster 1 is dominated by types of disease with code E119 (Diabetes mellitus without complications), female sex and age range 41-60 years and cluster 2 is dominated by disease types with code J180 (Bronchopneumonia, unspecified organism), female sex and age range 41-60 years.
Sistem Pendukung Keputusan Seleksi Pendidikan dan Pengembangan Pegawai Negeri dengan Metode AHP dan SAW

Education in Staff Operation of Indonesian National Police is an important element that aims to improve the quality of personnel. Staff Operation of Indonesian National Police (Sops Polri) is an unit in the National Police of Indonesian Headquarters. Education and development is carried out by the Staff Human Resources of Indonesian National Police (SSDM Polri) by gathering personnel data from various units. At first Sops Polri has to select its personnel so that the selected personnel is the best. But the process of selection in Sops Polri still done manually and the chances of errors are very large. So the development of decision support system using AHP and SAW methods can help the process of selection in Sops Polri. After decision support system was developed then it was tested by using the System Usability Scale (SUS) method with a result of 80.25 which can be interpreted as acceptable.
Sistem Pakar Dalam Mendiagnosa Penyakit Tubercolosis dengan mengimplementasikan Metode Case Based Reasoning

Health is one of the most valuable parts of human life. So healthy is the goal of every human being. Many things cause the decline in human health, such as hereditary genes, sensitive immunity and exposure to viruses or bacteria. One of the diseases caused by bacteria is tuberculosis (TB). Tuberculosis is a disease caused by exposure to a bacterium called Mycobacterium tuberculosis. There are two types of tuberculosis, namely pulmonary tuberculosis and extra pulmonary tuberculosis. Pulmonary tuberculosis can be defined as a disease that attacks the lungs and affects the lung parenchyma [4]. It's just that this type of disease does not attack other organs. While extrapulmonary tuberculosis is a tuberculosis disease in which this type of disease can attack other related organs such as the hilum, pleura and various other organs. the lack of funds for health checks makes it too late for many people to get treatment. Therefore, the development of technology should be utilized in handling this problem. One of the technologies that can be used in dealing with these problems is to use an expert system. An expert system is a system that is developed using the development of the knowledge that is owned by many experts and is used as a reference in developing the technology. In using an expert system, a method is needed that can help solve existing problems, therefore in this study the method used is the Case-Based Reasoning (CBR) method. The Case-Based Reasoning (CBR) method is the most suitable method for use in this study because the main function of this method is to diagnose disease. Based on the results of the calculation process using the Case-Based Reasoning method for each type of TB disease, the results obtained are for pulmonary tuberculosis to obtain a value of 85%, while for tuberculosis with extra pulmonary tuberculosis it is 62%. So based on the results obtained in this study it was determined that the sample was diagnosed with pulmonary tuberculosis. With a similarity of 85%.
Media industry in the information society: tracks of the professional self-realization

The article considers the features of the modern media industry development in the information society. The media industry is proposed to explicate in terms of its interaction with other economy segments, and also with regard to its influence on the social stratification due to the so called digital divide. The authors conclude that the digital opportunities make the media industry extremely advanced and efficient both for business and professional self-realization, upon that the authors emphasize that the learning ability, ability to the intellectual and creative development are becoming determinants of professional realization in the information society.
Resolving network clusters disparity based on dissimilarity measurements with nonmetric analysis of variance

Classic ANOVA (cA) tests the explanatory power of a partitioning on a set of objects. More fit for clusters proximity analysis, nonparametric ANOVA (npA) extends to a case where instead of the object values themselves, their mutual distances are available. However, extending the cA applicability, the metric conditions in npA are limiting. Based on the central limit theorem (CLT), here we introduce nonmetric ANOVA (nmA) that by relaxing the metric properties between objects, allows an ANOVA-like statistical testing of a network clusters disparity. We present a parametric test statistic which under the null hypothesis of no differences between the competing clusters means, follows an exact F-distribution. We apply our method on three diverse biological examples, discuss its parallel performance, and note the specific use of each method tailored by the inherent data properties. The R code is provided at github.com/AmiryousefiLab/nmANOVA.
Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer

Currently, over a thousand LLMs exist that are multi-purpose and are capable of performing real world tasks, including Q&A, text summarization, content generation, etc. However, accessibility, scale and reliability of free models prevents them from being widely deployed in everyday use cases. To address the first two issues of access and scale, organisations such as HuggingFace have created model repositories where users have uploaded model weights and quantized versions of models trained using different paradigms, as well as model cards describing their training process. While some models report performance on commonly used benchmarks, not all do, and interpreting the real world impact of trading off performance on a benchmark for model deployment cost, is unclear. Here, we show that a herd of open source models can match or exceed the performance of proprietary models via an intelligent router. We show that a Herd of open source models is able to match the accuracy of ChatGPT, despite being composed of models that are effectively 2.5x smaller. We show that in cases where GPT is not able to answer the query, Herd is able to identify a model that can, at least 40% of the time.
Interpretable Prototype-based Graph Information Bottleneck

The success of Graph Neural Networks (GNNs) has led to a need for understanding their decision-making process and providing explanations for their predictions, which has given rise to explainable AI (XAI) that offers transparent explanations for black-box models. Recently, the use of prototypes has successfully improved the explainability of models by learning prototypes to imply training graphs that affect the prediction. However, these approaches tend to provide prototypes with excessive information from the entire graph, leading to the exclusion of key substructures or the inclusion of irrelevant substructures, which can limit both the interpretability and the performance of the model in downstream tasks. In this work, we propose a novel framework of explainable GNNs, called interpretable Prototype-based Graph Information Bottleneck (PGIB) that incorporates prototype learning within the information bottleneck framework to provide prototypes with the key subgraph from the input graph that is important for the model prediction. This is the first work that incorporates prototype learning into the process of identifying the key subgraphs that have a critical impact on the prediction performance. Extensive experiments, including qualitative analysis, demonstrate that PGIB outperforms state-of-the-art methods in terms of both prediction performance and explainability.
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

Large Language Models (LLMs) have transformed the landscape of artificial intelligence, while their enormous size presents significant challenges in terms of computational costs. We introduce LoRAShear, a novel efficient approach to structurally prune LLMs and recover knowledge. Given general LLMs, LoRAShear at first creates the dependency graphs over LoRA modules to discover minimally removal structures and analyze the knowledge distribution. It then proceeds progressive structured pruning on LoRA adaptors and enables inherent knowledge transfer to better preserve the information in the redundant structures. To recover the lost knowledge during pruning, LoRAShear meticulously studies and proposes a dynamic fine-tuning schemes with dynamic data adaptors to effectively narrow down the performance gap to the full models. Numerical results demonstrate that by only using one GPU within a couple of GPU days, LoRAShear effectively reduced footprint of LLMs by 20% with only 1.0% performance degradation and significantly outperforms state-of-the-arts. The source code will be available at https://github.com/microsoft/lorashear.
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning

Federated learning (FL) is a paradigm that allows several client devices and a server to collaboratively train a global model, by exchanging only model updates, without the devices sharing their local training data. These devices are often constrained in terms of communication and computation resources, and can further benefit from model pruning -- a paradigm that is widely used to reduce the size and complexity of models. Intuitively, by making local models coarser, pruning is expected to also provide some protection against privacy attacks in the context of FL. However this protection has not been previously characterized, formally or experimentally, and it is unclear if it is sufficient against state-of-the-art attacks. In this paper, we perform the first investigation of privacy guarantees for model pruning in FL. We derive information-theoretic upper bounds on the amount of information leaked by pruned FL models. We complement and validate these theoretical findings, with comprehensive experiments that involve state-of-the-art privacy attacks, on several state-of-the-art FL pruning schemes, using benchmark datasets. This evaluation provides valuable insights into the choices and parameters that can affect the privacy protection provided by pruning. Based on these insights, we introduce PriPrune -- a privacy-aware algorithm for local model pruning, which uses a personalized per-client defense mask and adapts the defense pruning rate so as to jointly optimize privacy and model performance. PriPrune is universal in that can be applied after any pruned FL scheme on the client, without modification, and protects against any inversion attack by the server. Our empirical evaluation demonstrates that PriPrune significantly improves the privacy-accuracy tradeoff compared to state-of-the-art pruned FL schemes that do not take privacy into account.
Behavior Alignment via Reward Function Optimization

Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task. This is challenging since it requires the identification of reward structures that are not sparse and that avoid inadvertently inducing undesirable behaviors. Naively modifying the reward structure to offer denser and more frequent feedback can lead to unintended outcomes and promote behaviors that are not aligned with the designer's intended goal. Although potential-based reward shaping is often suggested as a remedy, we systematically investigate settings where deploying it often significantly impairs performance. To address these issues, we introduce a new framework that uses a bi-level objective to learn \emph{behavior alignment reward functions}. These functions integrate auxiliary rewards reflecting a designer's heuristics and domain knowledge with the environment's primary rewards. Our approach automatically determines the most effective way to blend these types of feedback, thereby enhancing robustness against heuristic reward misspecification. Remarkably, it can also adapt an agent's policy optimization process to mitigate suboptimalities resulting from limitations and biases inherent in the underlying RL algorithms. We evaluate our method's efficacy on a diverse set of tasks, from small-scale experiments to high-dimensional control challenges. We investigate heuristic auxiliary rewards of varying quality -- some of which are beneficial and others detrimental to the learning process. Our results show that our framework offers a robust and principled way to integrate designer-specified heuristics. It not only addresses key shortcomings of existing approaches but also consistently leads to high-performing solutions, even when given misaligned or poorly-specified auxiliary reward functions.
DynPoint: Dynamic Neural Point For View Synthesis

The introduction of neural radiance fields has greatly improved the effectiveness of view synthesis for monocular videos. However, existing algorithms face difficulties when dealing with uncontrolled or lengthy scenarios, and require extensive training time specific to each new scenario. To tackle these limitations, we propose DynPoint, an algorithm designed to facilitate the rapid synthesis of novel views for unconstrained monocular videos. Rather than encoding the entirety of the scenario information into a latent representation, DynPoint concentrates on predicting the explicit 3D correspondence between neighboring frames to realize information aggregation. Specifically, this correspondence prediction is achieved through the estimation of consistent depth and scene flow information across frames. Subsequently, the acquired correspondence is utilized to aggregate information from multiple reference frames to a target frame, by constructing hierarchical neural point clouds. The resulting framework enables swift and accurate view synthesis for desired views of target frames. The experimental results obtained demonstrate the considerable acceleration of training time achieved - typically an order of magnitude - by our proposed method while yielding comparable outcomes compared to prior approaches. Furthermore, our method exhibits strong robustness in handling long-duration videos without learning a canonical representation of video content.