Reviewerly/exHarmony huggingface dataset:
Max-Sliced Mutual Information

Quantifying the dependence between high-dimensional random variables is central to statistical learning and inference. Two classical methods are canonical correlation analysis (CCA), which identifies maximally correlated projected versions of the original variables, and Shannon's mutual information, which is a universal dependence measure that also captures high-order dependencies. However, CCA only accounts for linear dependence, which may be insufficient for certain applications, while mutual information is often infeasible to compute/estimate in high dimensions. This work proposes a middle ground in the form of a scalable information-theoretic generalization of CCA, termed max-sliced mutual information (mSMI). mSMI equals the maximal mutual information between low-dimensional projections of the high-dimensional variables, which reduces back to CCA in the Gaussian case. It enjoys the best of both worlds: capturing intricate dependencies in the data while being amenable to fast computation and scalable estimation from samples. We show that mSMI retains favorable structural properties of Shannon's mutual information, like variational forms and identification of independence. We then study statistical estimation of mSMI, propose an efficiently computable neural estimator, and couple it with formal non-asymptotic error bounds. We present experiments that demonstrate the utility of mSMI for several tasks, encompassing independence testing, multi-view representation learning, algorithmic fairness, and generative modeling. We observe that mSMI consistently outperforms competing methods with little-to-no computational overhead.
Comparative Study of Cloud Forensic Investigation Using ADAM And NIST 800-86 Methods in Private Cloud Computing

As information technology advances, associated risks also increase, particularly in the field of private cloud computing services. These services are subject to potential internal abuse risks, either due to system vulnerabilities or other factors. However, the investigation of these incidents in private cloud computing varies greatly due to the different frameworks and unique characteristics of each cloud service. The lack of a standardized approach to analyzing and assessing investigative processes in cloud computing services has been a persistent problem. This lack of consensus affects the accuracy, efficiency, and data acquisition process when dealing with digital evidence in each method, causing concern among researchers. To overcome this, a comparative study was carried out with a focus on the ADAM (The Advanced Data Acquisition Model) method and the NIST (National Institute of Standards and Technology) method. The goal is to identify the most effective investigative process to deal with cyber attack incidents on both the server and client side of cloud computing services. By testing these methods in a network that is built on private cloud computing services, then the results from this research include the weaknesses and strengths of the ADAM and NIST methods are found when applied to cloud computing case studies and these have not been identified in previous research, then produce recommendations for investigators when conducting investigations on case studies on cloud computing, and in this study managed to find a bug in the ownCloud application version 10.9.1. Then this study also aims to provide researchers with valuable references to carry out analysis and assessment in the investigative process, where standardization is still an unresolved issue.
Deep Learning for Tumor-Associated Stroma Identification in Prostate Histopathology Slides

The diagnosis of prostate cancer is driven by the histopathological appearance of epithelial cells and epithelial tissue architecture. Despite the fact that the appearance of the tumor-associated stroma contributes to diagnostic impressions, its assessment has not been standardized. Given the crucial role of the tumor microenvironment in tumor progression, it is hypothesized that the morphological analysis of stroma could have diagnostic and prognostic value. However, stromal alterations are often subtle and challenging to characterize through light microscopy alone. Emerging evidence suggests that computerized algorithms can be used to identify and characterize these changes. This paper presents a deep-learning approach to identify and characterize tumor-associated stroma in multi-modal prostate histopathology slides. The model achieved an average testing AUROC of $$86.53\%$$ on a large curated dataset with over 1.1 million stroma patches. Our experimental results indicate that stromal alterations are detectable in the presence of prostate cancer and highlight the potential for tumor-associated stroma to serve as a diagnostic biomarker in prostate cancer. Furthermore, our research offers a promising computational framework for in-depth exploration of the field effect and tumor progression in prostate cancer.
On the Relevance of Temporal Features for Medical Ultrasound Video Recognition

Many medical ultrasound video recognition tasks involve identifying key anatomical features regardless of when they appear in the video suggesting that modeling such tasks may not benefit from temporal features. Correspondingly, model architectures that exclude temporal features may have better sample efficiency. We propose a novel multi-head attention architecture that incorporates these hypotheses as inductive priors to achieve better sample efficiency on common ultrasound tasks. We compare the performance of our architecture to an efficient 3D CNN video recognition model in two settings: one where we expect not to require temporal features and one where we do. In the former setting, our model outperforms the 3D CNN - especially when we artificially limit the training data. In the latter, the outcome reverses. These results suggest that expressive time-independent models may be more effective than state-of-the-art video recognition models for some common ultrasound tasks in the low-data regime. Code is available at https://github.com/MedAI-Clemson/pda_detection .
COUPLE: Accelerating Video Analytics on Heterogeneous Mobile Processors

Deep learning has achieved tremendous success in various fields, but its significant computational demands make inference on mobile devices extremely challenging. To address this issue, we propose the COUPLE system, which enables heterogeneous processors to collaborate on mobile devices for accelerating video analytics. Additionally, we design the Co-Optimize strategy which utilizes the inference results of GPU to mitigate the accuracy loss caused by DSP. Experimental results demonstrate that COUPLE can improve the inference Average Precision by up to 5% compared to existing solutions.
KLASIFIKASI KETEPATAN KELULUSAN SISWA PADA SMK YADIKA 9 BINTARA JAYA KOTA BEKASI MENGGUNAKAN ALGORITMA C4.5

Education is the learning of knowledge, skills, and habits of a group of people passed from one generation to the next through teaching, training, or research. SMK Yadika 9 Bintara Jaya Bekasi City is one of the private vocational schools, However, along the way, there are still problems that occur every year, namely the problem of the right number of student graduations and late. The number of students who graduate on time is an indicator of the effectiveness of a vocational school both public and private. SMK needs to detect the behavior of active students so that factors that cause students not to graduate on time can be seen. Data collection using techniques in the form of interviews and observations, in this study is related to systematic literature review to find predictive modeling using data mining classification methods. Data mining classification in determining the accuracy of student graduation with the CRISP-DM testing method. The results of this study resulted that the C4.5 algorithm is the most optimal method of performance in determining the graduation accuracy classification of students of SMK Yadika 9 Bintara Jaya Bekasi City.
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations

Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions. However, it is not clear yet how to evaluate the correctness of those explanations, whether it is from a human or a model perspective. One unaddressed bottleneck in the current evaluation procedure is the problem of out-of-distribution explanations, whose distribution differs from those of the training data. This important issue affects existing evaluation metrics such as the popular faithfulness or fidelity score. In this paper, we show the limitations of faithfulness metrics. We propose GInX-Eval (Graph In-distribution eXplanation Evaluation), an evaluation procedure of graph explanations that overcomes the pitfalls of faithfulness and offers new insights on explainability methods. Using a fine-tuning strategy, the GInX score measures how informative removed edges are for the model and the EdgeRank score evaluates if explanatory edges are correctly ordered by their importance. GInX-Eval verifies if ground-truth explanations are instructive to the GNN model. In addition, it shows that many popular methods, including gradient-based methods, produce explanations that are not better than a random designation of edges as important subgraphs, challenging the findings of current works in the area. Results with GInX-Eval are consistent across multiple datasets and align with human evaluation.
Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems

With the recent spike in the number and availability of Large Language Models (LLMs), it has become increasingly important to provide large and realistic benchmarks for evaluating Knowledge Graph Question Answering (KGQA) systems. So far the majority of benchmarks rely on pattern-based SPARQL query generation approaches. The subsequent natural language (NL) question generation is conducted through crowdsourcing or other automated methods, such as rule-based paraphrasing or NL question templates. Although some of these datasets are of considerable size, their pitfall lies in their pattern-based generation approaches, which do not always generalize well to the vague and linguistically diverse questions asked by humans in real-world contexts. In this paper, we introduce Spider4SPARQL - a new SPARQL benchmark dataset featuring 9,693 previously existing manually generated NL questions and 4,721 unique, novel, and complex SPARQL queries of varying complexity. In addition to the NL/SPARQL pairs, we also provide their corresponding 166 knowledge graphs and ontologies, which cover 138 different domains. Our complex benchmark enables novel ways of evaluating the strengths and weaknesses of modern KGQA systems. We evaluate the system with state-of-the-art KGQA systems as well as LLMs, which achieve only up to 45\% execution accuracy, demonstrating that Spider4SPARQL is a challenging benchmark for future research.
FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding

Image retrieval is a fundamental task in computer vision. Despite recent advances in this field, many techniques have been evaluated on a limited number of domains, with a small number of instance categories. Notably, most existing works only consider domains like 3D landmarks, making it difficult to generalize the conclusions made by these works to other domains, e.g., logo and other 2D flat objects. To bridge this gap, we introduce a new dataset for benchmarking visual search methods on flat images with diverse patterns. Our flat object retrieval benchmark (FORB) supplements the commonly adopted 3D object domain, and more importantly, it serves as a testbed for assessing the image embedding quality on out-of-distribution domains. In this benchmark we investigate the retrieval accuracy of representative methods in terms of candidate ranks, as well as matching score margin, a viewpoint which is largely ignored by many works. Our experiments not only highlight the challenges and rich heterogeneity of FORB, but also reveal the hidden properties of different retrieval strategies. The proposed benchmark is a growing project and we expect to expand in both quantity and variety of objects. The dataset and supporting codes are available at https://github.com/pxiangwu/FORB/.
The effect of activation function choice on the performance of convolutional neural networks

With the advance of technology, artificial intelligence (AI) is now applied widely in society. For example, natural language processing, speech recognition, and autopilot are all famous examples of how AI is changing our world. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes. Choosing the right neural activation function is crucial to prevent the loss of important trends and increase the efficiency of training time. Among all the different widely used activation functions, we hypothesized that a rectified linear unit (ReLU) would be the most efficient in training time and attain the highest accuracy on the image recognition task because ReLU has the advantage of avoiding the vanishing gradient problem and only requires light mathematical calculations. Having high accuracy and efficiency in the task of image classification is beneficial as this technique can be employed in many different real-world applications, such as diagnosing in healthcare, identifying potential threats in security, or developing an autonomous vehicle. Our results indicate that when the number of hidden layers is small, networks employing the ReLU performed similarly to networks using hyperbolic tangent, and both networks with ReLU and hyperbolic tangent outperformed networks using the sigmoid function.
Analisis Sentimen Pengguna Aplikasi SIMANTAP Pondok Pesantren Salafiyah Syafi’iyah Situbondo

Pondok Pesantren Salafiyah Syafi'iyah Sukorejo Situbondo provides a payment application called SIMANTAP. This application aims to make it easier for guardians of students to make payments for bills given by Islamic boarding schools. Along the way, some obstacles arise; users tend to give their views in a comment. These comments will later provide an overview of the user's response to the SIMANTAP application. This study will analyze comment data using the Naïve Bayes Classifier. The data was taken from September 2021 to October 2021. As a result, users tend to give a positive response of 89%. Based on the test data results using the Naïve Bayes classifier algorithm, the classification can predict quite well; this is shown by the precision results of 82%, 97% recall, and 89% fi-score.
The Influence of The Use of Learning Media, Interpersonal Communication, Instructional Communication on The Level of Emotional Intelligence

This research aims to determine the magnitude of the influence of learning media, interpersonal communication, and instructional communication on student absorption levels. This research is quantitative research with data distribution through surveys. People with autism need proper education with appropriate learning methods, therefore inclusive schools are formed which are able to support education for children with special needs. The results of this research are that there is a significant influence between the variables of instructional communication, interpersonal communication, and learning media with emotional intelligence.
NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA

The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO &gt;50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.
A Framework to Assess Knowledge Graphs Accountability

Knowledge Graphs (KGs), and Linked Open Data in particular, enable the generation and exchange of more and more information on the Web. In order to use and reuse these data properly, the presence of accountability information is essential. Accountability requires specific and accurate information about people's responsibilities and actions. In this article, we define KGAcc, a framework dedicated to the assessment of RDF graphs accountability. It consists of accountability requirements and a measure of accountability for KGs. Then, we evaluate KGs from the LOD cloud and describe the results obtained. Finally, we compare our approach with data quality and FAIR assessment frameworks to highlight the differences.
PERANCANGAN VPS DAN MULTI-REGION LOAD BALANCER UNTUK WEBSITE PT SCHLEMMER AUTOMOTIVE INDONESIA MENGGUNAKAN LAYANAN GOOGLE CLOUD PLATFORM

PT Schlemmer Automotive Indonesia as a multinational company under the Schlemmer Group requires a website that can be accessed smoothly by users from various countries, such as Indonesia, Germany and China. In order for the website to be accessed quickly and stably in each of these countries, a reliable and efficient infrastructure is required. This study aims to design a VPS system and load balancer for the PT Schlemmer Automotive Indonesia website that can divide the traffic load between several servers or VPS instances and direct traffic to access servers that are in the closest location to the user. The method used in this study is the prototyping method with the stages of system requirements analysis, system design, system testing, and implementation. The test results show that the designed system can run well and proves that the use of VPS and load balancers can improve website performance and stability when traffic spikes occur. In conclusion, this study shows that the use of VPS and load balancers on the PT Schlemmer Automotive Indonesia website using the Google Cloud Platform service results in good website performance when accessed in each country.
TATA KELOLA REKAM MEDIS BERBASIS TEKNOLOGI INFORMASI UNTUK LAPORAN INDEKS PENYAKIT POLI JANTUNG DI RSUD AL - IHSAN

At this time, technology is generally used to help the completion of work to be easier, faster and more accurate. One of the uses of technology in medical record management is an important part of success in implementing good corporate governance which aims to ensure that health service organizations are responsible for continuously improving the quality of their services and guaranteeing the provision of services with high standards by creating an environment where excellent service. Utilization of visual basic information technology in medical management which is intended to facilitate officers in the process of collecting data related to heart disease up to the process of analyzing and reporting. The research method used is a quantitative method with a descriptive approach. With data collection techniques in the form of observation, interviews and literature and using the waterfall system development method. The purpose of this study is that with this disease index data system, it is hoped that it will make it easier to make reports and can speed up the process of making disease index reports every month for reporting to the hospital management department.
TATA KELOLA REKAM MEDIS ELEKTRONIK BERBASIS TEKNOLOGI INFORMASI PADA PENANGANAN RESUME MEDIS PASIEN DENGAN METODE AGILE

This study contains a medical resume management system using a programming language using Microsoft Visual Studio 2012 at Pusat Mata Nasional Rumah Sakit Mata Cicendo. The research method used by the author is a qualitative research method which is based on the philosophy of postpositivism, observation, and software development methods using the Agile Method, a software methodology based on the work process with stages starting from requirements, design, development, testing, deployment. The problem found in this research is to first summarize and write the manual of the existing application onto paper after that it is typed in Microsoft Word, then printed, so it is less efficient. Therefore, to overcome these problems, the authors created an integrated medical resume governance system to facilitate the process of creating an effective and efficient medical resume, including the registration process, patient data, doctor data, polyclinic data, medical information document retrieval data, reports. retrieval, and medical resume reports. With a medical resume management system, the process of making medical resume submissions will be faster and on time.
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments

Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks. Recent successes of Transformers inspire casting offline RL as sequence modeling, which performs well in long-horizon tasks. However, they are overly optimistic in stochastic environments with incorrect assumptions that the same goal can be consistently achieved by identical actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer (UNREST) for planning in stochastic driving environments without introducing additional transition or complex generative models. Specifically, UNREST estimates state uncertainties by the conditional mutual information between transitions and returns, and segments sequences accordingly. Discovering the `uncertainty accumulation' and `temporal locality' properties of driving environments, UNREST replaces the global returns in decision transformers with less uncertain truncated returns, to learn from true outcomes of agent actions rather than environment transitions. We also dynamically evaluate environmental uncertainty during inference for cautious planning. Extensive experimental results demonstrate UNREST's superior performance in various driving scenarios and the power of our uncertainty estimation strategy.