Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of malicious sessions in IoT networks by using K-Nearest Neighbor and Moth Blade Optimization Algorithm.

-An IoT network's ease of use may enhance people's lives in several ways. Internet of Things (IoT) device security is a critical concern right now because of the proliferation of possible attack vectors. In this research, we provide a technique that can identify intrusions into IoT networks by dividing sessions into two types: attack and regular. Using a moth flame optimization genetic approach, work was done to choose attributes in order to determine the class representative sessions. To identify the specific class meeting, K-Nearest Neighbor was used. Using a real-world dataset, the experimental findings show that the proposed model, Moth Flame based IOT Network Security (MFIOTNS), may optimize various assessment parameters to provide higher productivity benefits.
Machine Learning-Based Rock Facies Prediction Using Geothermal Data: A Comparative Analysis of Algorithms

An approach to predicting rock facies has been generated. Machine learning techniques have generated a novel approach to predicting rock facies. The objective was to develop a reliable facies predictor capable of categorizing rocks into 5 distinct facies: Sand, sandy shale, shady sand, volcanic sand, and shale. To achieve this, electric logs, including gamma-ray, resistivity, and density, were utilized to generate a synthetic facies log that could accurately represent the true geological facies. The study evaluated the performance of four machine learning algorithms: k-nearest Neighbors (KNNs), Random Forest (RF), Decision Tree, and Stochastic gradient descent (SGD). These algorithms were employed to build predictive models using the Utah FORGE geothermal project data as input. The goal was to determine which algorithm performed better in accurately predicting rock facies based on the electric logs. The study results indicate that the developed facies prediction model successfully generated a synthetic facies log that closely matched human-defined facies logs. Moreover, the comparative analysis revealed insights into the strengths and weaknesses of the various machine learning algorithms in the context of rock facies prediction. This research has significant implications for geology and geothermal energy exploration, as it offers a data-driven approach to enhance our understanding of subsurface rock formations. By leveraging machine learning, we can improve the accuracy and efficiency of facies prediction, ultimately aiding geothermal resource assessment and development.
Analysis of the specific effort in performance judo

Judo, an Olympic sport, includes techniques performed with maximum efficiency and minimum effort. Specialists consider that the biomechanics of combat actions and the specificity of physical training and exercise capacity are increasingly important. The purpose of this paper is to highlight the characteristics of exercise capacity specific to the training and competitive activities for senior performance judo athletes. The characteristics of aerobic and anaerobic effort are determined in this study, as well as the relationship between them and the training methods that should be developed for performance judo athletes. The research subjects are male and female performance and high-performance athletes aged 19 to 21 years, participants in national and international competitions. The research used general and specific methods aimed at assessing and enhancing aerobic and anaerobic exercise capacity. For the assessment of specific anaerobic capacity, methods for measuring the level of blood lactic acid and laboratory spectrophotometry are used. Aerobic capacity is assessed by spirometry and digitised pulse oximetry methods. Data on exercise capacity were collected during training and the execution of combat actions specific to the competitive activity. Data on maximum oxygen uptake and calorie consumption specific to judo effort are also presented using the comparative calorimetric and thermographic system assessment. The research findings demonstrate the importance of assessing aerobic and anaerobic exercise capacity in the training of performance judo athletes in order to achieve the intended adaptive effects for increasing efficiency in the execution of combat actions during top-level competitions.
Poorly differentiated papillary thyroid carcinoma in a 5-year-old girl: a case report

Background The incidence of thyroid cancer has been increasing among the population less than 20 years of age, especially during the second decade of life. Differentiated papillary thyroid cancer is the most common pathology in children. It usually presents with thyroid nodules with or without cervical lymphadenopathy. Thyroid nodules carry a considerable risk of malignancy in children. Case presentation We herein report a 5-year-old girl with poorly differentiated papillary thyroid cancer which is an extremely rare finding at this age. The patient underwent right thyroid lobectomy followed by left hemi thyroid-ectomy and right central and lateral neck dissection. Pathologic examination revealed a poorly differentiated papillary thyroid carcinoma with nodal deposits. Conclusion It is essential to raise awareness of proper evaluation and management of thyroid nodules at such an age.
Neural Network Modeling for Development of High-Pressure Measurement of Carbon Dioxide Solubility in the Aqueous AEEA+Sulfolane

Function (RBF) network was developed. RBF network with a spread value of 2.2 and 138 neurons had an outstanding performance and achieved an MSE value of 2.53E-05 along with an R 2 value of 0.99993, 11 seconds, and a %AARD value of 0.1460. According to experimental and predicted data, the neural networks are well trained and are able to predict CO 2 loading precisely in an economic and optimized way.
Initial State Fluid Geochemistry of the Dieng Geothermal Field, Indonesia: New Constraints for Conceptual Model

The Dieng Geothermal Field, Indonesia has a 2-phase reservoir with a current installed capacity of 60 MW. The data used is fluid geochemical data from manifestations taken in the period 1977 - 1997 and well fluid data (brine, gas, and stable isotopes) during production tests in 1980 – 1999. The Dieng Geothermal System is divided into 2 reservoir areas, i.e., the Sileri and Sikidang areas. The Sileri area has a neutral fluid characteristic, even though it has magmatic fluid influx potential. The neutralization process occurs due to the intensive water-rock interaction process in the reservoir. The reservoir has a temperature of around 283-338 o C with a Cl content of 9,600 – 19,000 mg/kg. The source of the fluid is heated meteoric water, mixed with fluids resulting from magmatic degassing, boiling, and experiencing water-rock interaction with sedimentary or meta-sedimentary rocks. Besides being characterized by a high Cl content, interactions with sedimentary rock formations are also supported by a high boron content. The hydrothermal fluid in the Sileri reservoir flows vertically towards the upflow zone in two areas, i.e., Pagerkandang Crater (Well-10 and Well-1) and Merdada Crater (Well-4 and Well-9). There is also a steam cap zone in the Sileri area, which is characterized by excess enthalpy, high NCG, and a temperature profile that exceeds the boiling point depth. The Sikidang reservoir has a more acidic fluid character due to the more dominant influence of magmatic fluid. The Sikidang reservoir has a two-phase fluid, with most wells penetrating the steam cap zone and several wells penetrating the liquid zone, it is estimated that there is a boiling zone between liquid and steam reservoirs. The fluid temperature in the reservoir ranges from 260 to 324 o C with high NCG content (2 – 21 wt%). The fluid flow in the Sikidang Area comes from upflow around Pangonan Crater, Well-V, Sikidang, and Sibanteng manifestations. This upflow zone also correlates with the acid fluid zone, therefore there is the potential for acid fluid to be found deep in the reservoir.
WIP: Body Posture Analysis as an Objective Measurement for Human Trust Dynamics in AVs

—Human trust is critical for the adoption and continued use of autonomous vehicles (AVs). Experiencing vehicle failures that stem from security threats to underlying technologies that enable autonomous driving, can significantly degrade drivers’ trust in AVs. It is crucial to understand and measure how security threats to AVs impact human trust. To this end, we conducted a driving simulator study with forty participants who underwent three drives including one that had simulated cybersecurity attacks. We hypothesize drivers’ trust in the vehicle is reflected through drivers’ body posture, foot movement, and engagement with vehicle controls during the drive. To test this hypothesis, we extracted body posture features from each frame in the video recordings, computed skeletal angles, and performed k-means clustering on these values to classify drivers’ foot positions. In this paper, we present an algorithmic pipeline for automatic analysis of body posture and objective measurement of trust that could be used for building AVs capable of trust calibration after security attack events.
A simplified model for measuring longevity risk for life insurance products

In this paper, we propose a simple dynamic mortality model to fit and forecast mortality rates for measuring longevity and mortality risks. This proposal is based on a methodology for modelling interest rates, which assumes that changes in spot interest rates depend linearly on a small number of factors. These factors are identified as interest rates with a given maturity. Similarly, we assume that changes in mortality rates depend linearly on changes in a specific mortality rate, which we call the key mortality rate. One of the main advantages of this model is that it allows the development of an easy to implement methodology to measure longevity and mortality risks using simulation techniques. Particularly, we employ the model to calculate the Value-at-Risk and Conditional-Value-at-Risk of an insurance product testing the accuracy and robustness of our proposal using out-of-sample data from six different populations.
SOIL N-P-K PREDICTION USING LOCATION AND CROP SPECIFIC RANDOM FOREST CLASSIFICATION TECHNIQUE IN PRECISION AGRICULTURE

Agriculture is a basic and most important profession of any country as it balances the food requirement and also the essential raw materials of industry. In the similar sense, the adaptation of implementing smart technology in agriculture practices needs to be focused on better land productivity. The fertilizer and manure should be precisely applied during agriculture practice throughout the countryside with the use of digital technologies. This motivates us to develop a reactive web application which will accurately predict the required soil N-P-K (Nitrogen-Phosphorus-Potassium) content by utilizing one time soil testing results of available soil N-P-K contents as per the yield target. This predictive model is designed by considering standard experimental data sets from Indian Council for Agriculture Research (ICAR), India. The prediction model is designed using Random Forest (RF) algorithm which is capable of handling large dataset. The predicted N-P-K content are shown in a reactive R Shiny user interface to notify required N-P-K values for the necessary action by the farmer. The complete web based prediction model is efficiently conveying the required N-P-K content information of the particular farm location to the farmer as well as the agriculture specialists.
Observations from the Seafloor: Ultra-low-frequency Ambient 1 Ocean-Bottom Nodal Seismology at the Amendment Field

SUMMARY 5 Large-scale ocean-bottom node (OBN) arrays of 1000s of multi-component instruments de-6 ployed over 1000s of square kilometers have been used successfully for active-source seis-7 mic exploration activities including full waveform inversion (FWI) at exploration frequencies 8 above about 2.0 Hz. The analysis of concurrently recorded lower-frequency ambient wavefield 9 data, though, is only just beginning. A key long-term objective of such ambient wavefield anal-10 yses is to exploit the sensitivity of sub-2.0 Hz energy to build long-wavelength initial elastic 11 models, thus facilitating FWI applications. However, doing so requires a more detailed un-12 derstanding of ambient wavefield information recorded on the seafloor, the types, frequency 13 structure and effective source distribution of recorded surface-wave modes, the near-seafloor 14 elastic model structure, and the sensitivity of recorded wave modes to subsurface model struc-15 ture. To this end, we present a wavefield analysis of low-and ultra-low-frequency ambient 16 data (defined as < 1.0 Hz and < 0.1 Hz, respectively) acquired on 2712 OBN stations in the 17 Amendment Phase 1 survey covering 2750 km 2 of the Gulf of Mexico. After applying prestack 18 ambient data preprocessing and seismic cross-coherence interferometry workflows, we demon-19 strate that the resulting virtual shot gather (VSG) volumes contain evidence for surface-wave 20 and guided P-wave mode propagation between the 0.01-1.0 Hz that
High-permittivity ceramics enabled highly homogeneous zero-index metamaterials for high-directivity antennas and beyond

Zero-index metamaterials (ZIMs) can support uniform electromagnetic field distributions at any frequency, but their applications are hampered by the ZIM’s homogenization level—only 3 unit cells per free-space wavelength, which is fundamentally limited by the low-permittivity inclusions ( ε r ≈ 12) and background matrix ( ε r ≈ 1). Here, by filling high-permittivity SrTiO 3 ceramic ( ε r ≈ 294) pillars in BaTiO 3 ( ε r ≈ 25) background matrix, we demonstrate a highly homogeneous microwave ZIM with an over threefold increase in the homogenization level. Leveraging such a ZIM, we achieve not only an antenna, approaching the fundamental limit in the directivity with outstanding scalability, but also a concave lens with a focal length of as short as 1 λ 0 . Our highly homogeneous ZIM has profound implications in ceramics, ZIM-based waveguides and cavities, free-space wavefront manipulation, and microwave quantum optics, and opens up enormous possibilities in wireless communications, remote sensing, global positioning satellites, etc.
Vision: “AccessFormer”: Feedback-Driven Access Control Policy Generation Framework

Abstract —Access control failures can cause data breaches, putting entire organizations at risk of financial loss and reputation damage. One of the main reasons for such failures is the mistakes made by system administrators when they manually generate low-level access control policies directly from high-level requirement specifications. Therefore, to help administrators in that policy generation process, previous research proposed graphical policy authoring tools and automated policy generation frameworks. However, in reality, those tools and frameworks are neither usable nor reliable enough to help administrators generate access control policies accurately while avoiding access control failures. Therefore, as a solution, in this paper, we present “AccessFormer”, a novel policy generation framework that improves both the usability and reliability of access control policy generation. Through the proposed framework, on the one hand, we improve the reliability of policy generation by utilizing Language Models (LMs) to generate, verify, and refine access control policies by incorporating the system’s as well as administrator’s feedback. On the other hand, we also improve the usability of the policy generation by proposing a usable policy authoring interface designed to help administrators understand policy generation mistakes and accurately provide feedback.
Efficient Privacy-Preserving Medical Diagnosis on Edge Computing Platforms

Edge computing has emerged as a potential solution to some of the challenges in healthcare. It involves processing data closer to the source, such as medical devices and sensors, rather than relying solely on centralized data centers or cloud computing. This approach offers advantages like reduced latency, real-time decision-making, and enhanced privacy protection. Efficient privacy-preserving medical diagnosis on edge computing platforms is a concept deeply rooted in the intersection of healthcare and technology. Over the years, medical diagnosis has increasingly relied on machine learning algorithms to analyze vast amounts of patient data. These algorithms can predict diseases, identify anomalies in medical images, and offer personalized treatment recommendations. However, a significant challenge in this domain is the protection of patient data privacy. Healthcare data is incredibly sensitive, and data breaches or unauthorized access can result in severe consequences, including identity theft and discrimination. Therefore, this research work tackles several key issues. First and foremost, it aims to ensure the privacy of medical data throughout the diagnosis process. This means developing machine learning models and algorithms that can make accurate predictions while safeguarding the sensitive information contained in patient records. Additionally, these models must be highly efficient to operate effectively within the resource-constrained environment of edge computing platforms. Further, real-time processing is a critical requirement in healthcare, especially for conditions that demand immediate attention. Hence, this work strives to provide timely results to healthcare providers. Furthermore, the models must maintain a high level of accuracy and reliability to instil trust in the medical community and ensure that patients receive the best possible care. Ultimately, this research aligns with the goal of patient-centric care. It empowers patients to have more control over their data, share
The Effect of a Mainshock on the Size Distribution of the Aftershocks

A systematic decay of the aftershock rate over time is one of the most fundamental empirical laws in Earth science. However, the equally fundamental effect of a mainshock on the size distribution of subsequent earthquakes has still not been quantified today and is therefore not used in earthquake hazard assessment. We apply a stacking approach to well‐recorded earthquake sequences to extract this effect. Immediately after a mainshock, the mean size distribution of events, or b value, increases by 20–30%, considerably decreasing the chance of subsequent larger events. This increase is strongest in the immediate vicinity of the mainshock, decreasing rapidly with distance but only gradually over time. We present a model that explains these observations as a consequence of the stress changes in the surrounding area caused by the mainshocks slip. Our results have substantial implications for how seismic risk during earthquake sequences is assessed.
Metis: File System Model Checking via Versatile Input and State Exploration

We present Metis , a model-checking framework designed for versatile, thorough, yet configurable file system testing in the form of input and state exploration. It uses a nondeterministic loop and a weighting scheme to decide which system calls and their arguments to execute. Metis features a new abstract state representation for file-system states in support of efficient and effective state exploration. While exploring states, it compares the behavior of a file system under test against a reference file system and reports any discrepancies; it also provides support to investigate and reproduce any that are found. We also developed RefFS, a small, fast file system that serves as a reference, with special features designed to accelerate model checking and enhance bug reproducibility. Experimental results show that Metis can flexibly generate test inputs; also the rate at which it explores file-system states scales nearly linearly across multiple nodes. RefFS explores states 3–28 × faster than other, more mature file systems. Metis aided the development of RefFS, reporting 11 bugs that we subsequently fixed. Metis further identified 12 bugs from five other file systems, five of which were confirmed and with one fixed and integrated into Linux.
Practices in

Mutual learning and infrastructuring are two core concepts in Participatory Design (PD), but the relation between them has yet to be explored. In this article, we foreground learning in infrastructuring processes aimed at change in the public sector. Star and Ruhleder’s (1996) framework for first, second, and third level issues is applied as a fruitful way to stage and analyze learning in such processes. The argument is developed through the insights that arose from a 4-year-long infrastructuring process about future library practices. Framed as Co-Labs this process was organized by researchers and officers from the local regional office. This led to adjusted roles for both PD researchers and civil servants working with materials at the operational and strategic levels. The case shows how learning led to profound changes in the regional public sector in the form of less bureaucratic and more participatory experimental and learning-focused worldviews and practices.
TRADITIONS AND MYTHS OF KAJANG PEOPLE IN SOUTH SULAWESI

The research aimed to elaborate the myths following traditions in explicit connection with the Kajang indigenous people's dwellings and way of life. The existence of Kajang indigenous people was known for preserving their traditions with austerity. Clinging to their traditional belief system, Pasang ri Kajang was used to protect their sacred forest and natural environment. Some myths still seemed relevant and took part actively in their everyday lives. This was empirical qualitative research, with the researcher directly visiting the location to experience the cultural situation and the people of Kajang. First-hand experience, photographing, hand sketching, interviews, and focus group discussions were employed to collect data. Analyzing the characteristics of the Kajang area from the maps, narrative analysis for the interview, focus group discussion, sketches, manual notes, photographs, and cross-analysis from literature studies resulted in the difference between inner and outer Kajang. It is found that eight practical conducts closely related to the myths are elaborated, such as oral communication, no electricity, no clay roof tile, no footwear, no colorful clothes, no decoration or modern material, orientation of buildings, and odd numbering and time calculation. Some limitations and suggestions are also presented.
Measuring the Prevalence of Password Manager Issues Using In-Situ Experiments

—To protect their security, users are instructed to use unique passwords for all their accounts. Password managers make this possible, as they can generate, store, and autofill passwords within a user’s browser. Unfortunately, prior work has identified usability issues which may deter users from using password managers. In this paper, we measure the prevalence of usability issues affecting four popular password managers (Chrome, Safari, Bitwarden, and Keeper). We tested these password managers with their out-of-the-box settings on 60 randomly sampled websites. We show that users are likely to encounter issues using password managers during account registration and authentication. We found that usability issues were widespread, but varied by password manager. Common issues included password managers not prompting the user to generate passwords, autofilling web forms incorrectly or not at all, and generating passwords that were incompatible with websites’ password policies. We found that Chrome and Safari had fewer interaction issues than the other password managers we tested. We conclude by suggesting ways that websites and password managers can improve their compatibility with each other. For example, we recommend that password managers tailor their passwords to websites’ requirements (like Chrome and Safari), or adopt alphanumeric-only password generation by default (like Bitwarden).