Reviewerly/exHarmony huggingface dataset:
Exploring Eye Expressions for Enhancing EOG-Based Interaction

This paper explores the classification of eye expressions for EOG-based interaction using JINS MEME, an off-the-shelf eye-tracking device. Previous studies have demonstrated the potential for using electrooculography (EOG) for hands-free human-computer interaction using eye movements (directional, smooth pursuit) and eye expressions (blinking, winking). We collected a comprehensive set of 14 eye gestures to explore how well both types of eye gestures be classified together in a machine learning model. Using a Random Forest classifier trained on our collected data using 15 engineered features, we obtained an overall classification performance of 0.77 (AUC). Our results show that we can reliably classify eye expressions, enhancing the range of available eye gestures for hands-free interaction. With continued development and refinement in EOG-based technology, our findings have long-term implications for improving the usability of the technology in general and for individuals who require a richer vocabulary of eye gestures to interact hands-free.
ABPET: An Automatic Software Bug Prediction Using Ensemble Learning Technique

Software Bug prediction (SBP) helps in improving the quality of software to be developed.Bugs can be of different kinds such as functional, usability, security, etc, which leads to irrelevant features, data redundancy and missing samples.SBP is a very popular area in the IT world as this helps in improving the software product.SBP is a complicated task that can be performed both manually (semi-automated) or automatically.The semiautomated task requires extensive knowledge about the product to be developed and manpower.On the other hand, automatic SBP eliminated such issues and improves the bug prediction cost significantly.In this paper, a multistage automatic SBP (ABPET) model using ensemble machine learning techniques is proposed.The proposed ABPET architecture is divided into three phases: in preprocessing stage the skewed and asymmetric data is preprocessed performed, in the second stage, ABPET classification model using ensemble ML algorithms RF and AdaBoost designed.Finally, a variety of estimated metrics such as precision, recall, accuracy F-measure, and Mathews co-relation coefficient are used to evaluate the constructed framework.The proposed multi-stage framework is tested on a publicly available dataset of the Promise repository.The experimental results indicated that AdaBoost outperforms the random forest by a 2% on accuracy and F-measure parameter.
Implementasi Metode K-Nearest Neighbor dalam Mengklasifikasikan Kesegaran Ikan Kuro Menggunakan Citra

K uro fish (Eleutheronematetradactylum) is a type of fish that spreads throughout Indonesian waters, with different local names in each region. On the east coast of Sumatra it is known by the name of sukain fish while on the north coast of Java it is known as kuro fish. Freshness of fish is one of the benchmarks for consumers in choosing quality or good fish for consumption, because fresh fish is rich in protein and nutrients. Fish is also known to contain omega 3 fatty acids which are beneficial for brain growth, as well as calcium, vitamin D and phosphorus which are good for bones. However, the nutritional content contained in the fish may not be optimal anymore if it is consumed in a condition that is not fresh. Not only that, consumption of fish that is not fresh which leads to rotten conditions can make someone poisoned. Fish freshness checks can be done through microbiological and chemical analysis, but this method is less effective because it requires a lot of manpower, is quite expensive, and takes longer. For traders, the level of freshness of fish is determined in the traditional way, namely by observing, holding and smelling the smell of fish, sometimes there is also something that escapes observation so that there are still fish that are not fresh. To reduce these problems, the authors apply the K-Nearest Neighbor method in classifying the freshness of fish using images based on the color of the fish. By using the Kuro Fish type, using Matlab tools and in the results of the study using the K-Nearest Neighbor method with 40 training data and producing an accuracy of 100% and 16 test data with 7 correct data resulting in poor accuracy, which is 43 ,75%
Earthquake Monitoring and Detection Using the Internet of Things as Communication Protocol

Earthquake monitoring and notification by using smartphones or the Internet of things (IoT) expedients in real time is a difficult and inspiring project, not only because it is forced with the hard real-time issue but also due to the parallel task of earthquake and the non-earthquake notification. Earthquake event monitoring, nevertheless, is a challenge due to a deficiency of high-loyalty sense and sort proficiency. This project plans to use a NodeMCU with a SW-420 vibration sensor, an IoT sensor for smart earthquake monitoring. Earthquake monitoring includes some main measures, detected by using the SW-420 vibration sensor connected with NodeMCU and gets notification through the Blynk app to get data from ThingSpeak. The developed vibration is then detected by earthquake monitoring system from vibration sensor using time and regularity province inquiry. About monitoring of an earthquake, the earthquake inspiration transmissions are using the projected data into the Blynk from the smartphone for well-organized notification and conception. The recital of the projected system is assessed through mathematical simulations and a set of tests using trembling table tests.
Deontic Paradoxes in ASP with Weak Constraints

The rise of powerful AI technology for a range of applications that are sensitive to legal, social, and ethical norms demands decision-making support in presence of norms and regulations.Normative reasoning is the realm of deontic logics, that are challenged by well-known benchmark problems (deontic paradoxes), and lack efficient computational tools.In this paper, we use Answer Set Programming (ASP) for addressing these shortcomings and showcase how to encode and resolve several well-known deontic paradoxes utilizing weak constraints.By abstracting and generalizing this encoding, we present a methodology for translating normative systems in ASP with weak constraints.This methodology is applied to "ethical" versions of Pac-man, where we obtain a comparable performance with related works, but ethically preferable results.
Generalizing Level Ranking Constraints for Monotone and Convex Aggregates

In answer set programming (ASP), answer sets capture solutions to search problems of interest and thus the efficient computation of answer sets is of utmost importance.One viable implementation strategy is provided by translation-based ASP where logic programs are translated into other KR formalisms such as Boolean satisfiability (SAT), SAT modulo theories (SMT), and mixed-integer programming (MIP).Consequently, existing solvers can be harnessed for the computation of answer sets.Many of the existing translations rely on program completion and level rankings to capture the minimality of answer sets and default negation properly.In this work, we take level ranking constraints into reconsideration, aiming at their generalizations to cover aggregate-based extensions of ASP in more systematic way.By applying a number of program transformations, ranking constraints can be rewritten in a general form that preserves the structure of monotone and convex aggregates and thus offers a uniform basis for their incorporation into translation-based ASP.The results open up new possibilities for the implementation of translators and solver pipelines in practice.
Penalization Framework For Autonomous Agents Using Answer Set Programming

This paper presents a framework for enforcing penalties on intelligent agents that do not comply with authorization or obligation policies in a changing environment.A framework is proposed to represent and reason about penalties in plans, and an algorithm is proposed to penalize an agent's actions based on their level of compliance with respect to authorization and obligation policies.Being aware of penalties an agent can choose a plan with a minimal total penalty, unless there is an emergency goal like saving a human's life.The paper concludes that this framework can reprimand insubordinate agents.
A Logic Programming Approach to Global Logistics in a Co-Design Environment

In a co-design environment changes need to be integrated quickly and in an automated manner.This paper considers the challenge of creating and optimizing a global logistics system for the construction of a passenger aircraft within a co-design approach with respect to key performance indicators (like cost, time or resilience).The product in question is an aircraft, comprised of multiple components, manufactured at multiple sites worldwide.The goal is to find an optimal way to build the aircraft taking into consideration the requirements for its industrial system.The main motivation for approaching this challenge is to develop the industrial system in tandem with the product and making it more resilient against unforeseen events, reducing the risks of bottlenecks in the supply chain.This risk reduction ensures continued efficiency and operational success.To address this challenging and complex task we have chosen Answer Set Programming (ASP) as the modeling language, formalizing the relevant requirements of the investigated industrial system.The approach presented in this paper covers three main aspects: the extraction of the relevant information from a knowledge graph, the translation into logic programs and the computation of existing configurations guided by optimization criteria.Finally we visualize the results for an effortless evaluation of these models.Internal results seem promising and yielded several new research questions for future improvements of the discussed use case.
How to Study Infrastructure: Methodological Remarks in the Context of the Pandemic and its Impact on City Design

The paper is an introduction to the anthropology of infrastructures. We define how infrastructure is understood on the grounds of anthropology and science and technology studies. We show what is the significance of various infrastructures for the functioning of modern and late societies. The text discusses extensively the methodological challenges of studying infrastructures. We not only explain why analyzing infrastructures is difficult but also discuss several methodological tricks we can resort to when trying to uncover infrastructures. We elaborate the methodological guidelines on the margins of two research projects. The first dealt with social aspects of epidemics, and the second with urban clusters of innovative companies.
Contemporary Issues in Collection Management

This edited, openly licensed, textbook examines several different issues in collection management. Topics covered include physical vs. digital collections; the impact of BookTok on collections; challenges to 2SLGBTQ+ collections ; ebook licensing; ebook pricing; accessible collections for users with physical disabilities; accessible collections for users with invisible disabilities; "just in time"/demand-driven acquisitions; climate change and collections and research data collections.
Deep Reinforcement Learning-based Auto-tuning Algorithm for Cavity Filters

Over the past few decades, the tuning of cavity filters has often been done by trial and error, using human experience and intuition, due to the imprecision of the design and manufacturing tolerances, which often results in detuning the filters and requiring costly post-production fine-tuning. Various techniques using optimization and machine learning have been investigated to automate the process. The superiority of a deep reinforcement learning approach, which can properly explore various possibilities and operate them in the desired way according to the well-defined reward, has motivated us to apply it to our problem. To meet the demand for an automatic tuning algorithm for cavity filters with high accuracy and efficiency, this study proposes an automatic tuning algorithm for cavity filters based on the deep reinforcement learning. For the efficiency of the tuning process, we limit the order of the elements to be tuned, inspired by the experience of experts based on domain knowledge. In addition, the coarse tuning process is performed first, followed by the fine tuning process to improve the tuning accuracy. The proposed method has demonstrated the ability of the deep reinforcement learning to learn the complex relationship between impedance values of equivalent circuit elements and S-parameters to effectively satisfy filter design requirements within an acceptable time range. The performance of the proposed automatic tuning algorithm has been evaluated through simulation experiments. The effectiveness of the proposed algorithm is demonstrated by the fact that it is able to tune a detuned filter from random starting point to meet its design requirements.
Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks

Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients, for pediatric inpatient clinical prediction tasks.This retrospective cohort study used EHR data and included patients with at least one admission to an inpatient unit. One admission per patient was randomly selected. Adult inpatients were 18 years or older while pediatric inpatients were more than 28 days and less than 18 years. Admissions were temporally split into training (January 1, 2008 to December 31, 2019), validation (January 1, 2020 to December 31, 2020), and test (January 1, 2021 to August 1, 2022) sets. Primary comparison was a self-supervised model trained in adult inpatients versus count-based logistic regression models trained in pediatric inpatients. Primary outcome was mean area-under-the-receiver-operating-characteristic-curve (AUROC) for 11 distinct clinical outcomes. Models were evaluated in pediatric inpatients.When evaluated in pediatric inpatients, mean AUROC of self-supervised model trained in adult inpatients (0.902) was noninferior to count-based logistic regression models trained in pediatric inpatients (0.868) (mean difference = 0.034, 95% CI=0.014-0.057; P < .001 for noninferiority and P = .006 for superiority).Self-supervised learning in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients. This finding suggests transferability of self-supervised models trained in adult patients to pediatric patients, without requiring costly model retraining.
NNTBFV: Simplifying and Verifying Neural Networks Using Testing-Based Formal Verification

Neural networks are extensively employed in safety-critical systems. However, these critical systems incorporating neural networks continue to pose risks due to the presence of adversarial examples. Although the security of neural networks can be enhanced by verification, verifying neural networks is an NP-hard problem, making the application of verification algorithms to large-scale neural networks a challenging task. For this reason, we propose NNTBFV, a framework that utilizes the principles of Testing-Based Formal Verification (TBFV) to simplify neural networks and verify the simplified networks. Unlike conventional neural network pruning techniques, this approach is based on specifications, with the goal of deriving approximate execution paths under given preconditions. To mitigate the potential issue of unverifiable conditions due to overly broad preconditions, we also propose a precondition partition method. Empirical evidence shows that as the range of preconditions narrows, the size of the execution paths also reduces accordingly. The execution path generated by NNTBFV is still a neural network, so it can be verified by verification tools. In response to the results from the verification tool, we provide a theoretical method for analysis. We evaluate the effectiveness of NNTBFV on the ACAS Xu model project, choosing Verification-based and Random-based neural network simplification algorithms as the baselines for NNTBFV. Experiment results show that NNTBFV can effectively approximate the baseline in terms of simplification capability, and it surpasses the efficiency of the random-based method.
Price Prediction Using Web Scraping and Machine Learning Algorithms in the Used Car Market

The development of technology increases data traffic and data size day by day. Therefore, it has become very important to collect and interpret data. This study, it is aimed to analyze the car sales data collected using web scraping techniques by using machine learning algorithms and to create a price estimation model. The data needed for analysis was collected using Selenium and BeautifulSoup and prepared for analysis by applying various data preprocessing steps. Lasso regression and PCA analysis were used for feature selection and size reduction, and the GridSearchCV method was used for hyperparameter tuning. The results were evaluated with machine learning algorithms. &#x0D; Random Forest, K-Nearest Neighbor, Gradient Boost, AdaBoost, Support Vector and XGBoost regression algorithms were used in the analysis. The obtained analysis results were evaluated together with Mean Square Error (MSE), Root Mean Square Error (RMSE) and Coefficient of Determination (R-square). When the results for data set 1 were examined, the model that gave the best results was XGBoost Regression with 0.973 R2, 0.026 MSE and 0.161 RMSE values. When the results for data set 2 were examined, the model that gave the best results was K-Nearest Neighbor Regression with 0.978 R2, 0.021 MSE and 0.145 RMSE values.
Feature Ranking for Feature Sorting and Feature Selection with Optimisation

This paper introduces a framework called FR4(FS) $$^2$$ + which stands for Feature Ranking for Feature Sorting and Feature Selection with Optimisation. It is a tandem combining feature sorting which obtains a feature ranking and then only a certain percentage of the promising features are kept in order to be submitted to a further feature subset selection. The test-bed comprises three NIPS 2003 challenges which are still open in the sense that there is not currently a final approach to face these challenges successfully. Experimental results show that the approach is competitive and can be further exploited.
Semi-supervised hyperspectral unmixing dataset creation methods for unmixing algorithm analysis

Hyperspectral imaging is part of a growing remote sensing industry used in various applications like food or agriculture industries. Labeling hyperspectral data cubes is a resource and time intensive task. In order to try and speed up the labeling procedure, we propose a semi-supervised machine learning methodology to improve labeling speed at a cost of computational resources. An experiment was created to test the viability of this methodology. Gathered results show low hyperspectral label prediction (classification) accuracy using simple and fast neural networks.
Pengembangan Sistem Prediksi Bantuan Program Keluarga Harapan (PKH) Berbasis Machine Learning

The Family Hope Program (PKH) is a poverty alleviation program which is one of the government's strategies in reducing the poverty line. This program provides cash social assistance to poor families who are included in the list of beneficiary families with a focus on education and health. The purpose of implementing the PKH program is not only to reduce poverty and increase human resources but to break the poverty chain. The implementation of PKH in its realization experienced many obstacles that caused the program not to be on target, this was because the data verification process was not yet effective and was still carried out manually. A process is needed to digitize the distribution and realization of the family of hope program. Through this research, a system was developed that can predict the value of PKH beneficiary assistance. The system developed is based on machine learning with a prediction model using Artificial Neural Network (ANN) and Backpropagation learning algorithm. Parameters in the learning system using PKH assessment as many as 8 indicators from the data of PKH beneficiaries in Tabanan Regency. Based on the prediction model testing using two data treatments, namely with and without preprocessing data. Parameters treated with data on numeric attributes and categories provide optimal values with an R2 Score of 0.695824 with a number of hidden layers of 500 and a max epoch of 375
DEVELOPMENT OF SODA API LEARNING MEDIA BY APPLYING JIGMENT LEARNING METHODS FOR CITIZEN STUDENTS EARNING

In every learning carried out in an educational institution, the best thing is always done to improve the results of the learning. In this case learning can be assisted by learning media. The learning media can increase students' absorption in receiving each subject matter. For its use, the media used is SODA API media. This SODA API media has characteristics that can improve teamwork. Referring to the application of this learning media, it is applied to SMAN 4 Blitar. It is hoped that the results of its implementation can be; (1) developing the effectiveness and efficiency of learning, (2) developing students' enthusiasm for learning, (3) developing students' interest and motivation in learning, (4) developing students interact directly with reality, (5) overcoming the diverse learning modalities of students, (6) streamline the communication process in learning, (7) improve the quality of learning. This learning media will be optimized for the JIGMEN learning method which will certainly help students absorb the material presented.
The statistics of visual input change systematically with development

What a perceiver sees and learns about the visual world depends intimately on their own moment-to-moment behaviors. Head, eye and postural movements – even small ones – significantly alter the images projected to the eye. These images jointly carry both the low-level visual information fundamental to cortical visual processing and high-level semantic information. Infant visual experiences are crucial to the development of all levels of the cortical hierarchy and their feed-forward and feed-back connections. Visual experiences have been assumed to be more or less uniform in terms of their low-level statistics. However, the dramatic changes in infant motor control and behavior that occur over the first year have to been shown to yield marked changes in the higher level content (e.g., object categories, faces and their views) of visual experience (Adolph & Hoch, 2019; Smith et al., 2018). Here we present evidence on developmental changes in low-level visual statistics and their correlations with higher-level visual content. Infants at three different stages of motor development (1-3 months, 6-8 months, & 10-12 months; total n = 36) wore head-mounted cameras as they went about their daily lives at home, without experimenters present. We analyzed the images for low-level statistics of edges, the key units of primary visual cortex development (amount of edges in the image, degree to which these represented a single orientation, and amount of energy and contrast at low spatial frequencies), as well as high level content (e.g., faces and foregrounded individual objects). The evidence points to behaviorally-constrained niches of development that change systematically with development, with changes in both the low-level and high-level properties across age groups. The findings have new implications for a training curriculum for vision that is constrained by infant development.