Reviewerly/exHarmony huggingface dataset:
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning

Transfer learning in Reinforcement Learning (RL) has been widely studied to overcome training challenges in Deep-RL, i.e., exploration cost, data availability and convergence time, by bootstrapping external knowledge to enhance learning phase. While this overcomes the training issues on a novice agent, a good understanding of the task by the expert agent is required for such a transfer to be effective. As an alternative, in this paper we propose Expert-Free Online Transfer Learning (EF-OnTL), an algorithm that enables expert-free real-time dynamic transfer learning in multi-agent system. No dedicated expert agent exists, and transfer source agent and knowledge to be transferred are dynamically selected at each transfer step based on agents’ performance and level of uncertainty. To improve uncertainty estimation, we also propose State Action Reward Next-State Random Network Distillation (sars-RND), an extension of RND that estimates uncertainty from RL agent-environment interaction. We demonstrate EF-OnTL effectiveness against a no-transfer scenario and state-of-the-art advice-based baselines, with and without expert agents, in three benchmark tasks: Cart-Pole, a grid-based Multi-Team Predator-Prey (MT-PP) and Half Field Offense (HFO). Our results show that EF-OnTL achieves overall comparable performance to that of advice-based approaches, while not requiring expert agents, external input, nor threshold tuning. EF-OnTL outperforms no-transfer with an improvement related to the complexity of the task addressed.
Transparency in Sum-Product Network Decompilation

Sum-product networks guarantee that conditionals and marginals can be computed efficiently, for a wide range of models, bypassing the hardness of inference. However, this advantage comes at the expense of transparency, since it is unclear how variables interact in sum-product networks. Due to this, a series of decompilation algorithms transform sum-product networks back to Bayesian networks. In this work, we first study the transparency and causal utility of the resulting Bayesian networks. We then propose a novel decompilation algorithm to address the identified limitations.
Blame Attribution for Multi-Agent Path Finding Execution Failures

In Multi-Agent Systems (MAS), Multi-Agent Path Finding (MAPF) is the problem of finding a conflict-free plan for a group of agents from a set of starting points to a set of target points. Deviations from this plan are standard in real-world applications and may decrease overall system efficiency and even lead to accidents and deadlocks. In large MAS scenarios with physical robots, multiple faulty events occur over time, contributing to the overall degraded system performance. This raises the main problem we address in this work: how to attribute blame for a degraded MAS performance over a set of faulty events. We formally define this problem and propose using the Shapley values to solve it. Then, we propose an algorithm that efficiently approximates Shapley values by considering only some subsets of faulty events set. We analyze this algorithm theoretically and experimentally and demonstrate that it enables effectively trading off runtime for error.
Long-Tail Theory Under Gaussian Mixtures

We suggest a simple Gaussian mixture model for data generation that complies with Feldman’s long tail theory (2020). We demonstrate that a linear classifier cannot decrease the generalization error below a certain level in the proposed model, whereas a nonlinear classifier with a memorization capacity can. This confirms that for long-tailed distributions, rare training examples must be considered for optimal generalization to new data. Finally, we show that the performance gap between linear and nonlinear models can be lessened as the tail becomes shorter in the subpopulation frequency distribution, as confirmed by experiments on synthetic and real data.
Kebijakan Keamanan Teknologi Informasi Pada Perangkat Keras Di Perusahaan Distributor Sepatu

&lt;p&gt;&lt;em&gt;A shoe distributor company is a company engaged in the distribution of shoes to consumers. In its daily practice, shoe distributor companies have not implemented the IT Security Policy properly. Then the Community Service program is carried out with the aim of enforcing the established information security policy. The method is in the form of exposing material to shoe distributor companies regarding the importance of having and implementing an IT Security Policy, especially an IT Security Policy on Hardware. The implementation of the program was carried out in two major stages, namely the pre-implementation stage where the research team received data on company problems, conducted an analysis of these problems, prepared presentation materials/materials and the implementation stage where the PKM implementing team coordinated and executed the PKM material presentation activities. The presentation of the material was carried out face to face at the Head Office of the shoe distributor company. The activity began with an opening &amp;amp; introductory session, followed by presentation of material, which was divided into five major topics, namely explanation of system security, explanation of Policy, Standards, Procedures, Guidelines, explanation of Sample Template Policy, Standards, Procedures, Guidelines, explanation of Hardware security, and explanation of company Case Study. Then, the activity continued in the question-and-answer session and after that continued with the evaluation activities carried out by filling out the Google feedback link in the program implementation form for the participants. The results obtained in the PKM activities were that the implementation was carried out well and was beneficial for the shoe distributor company with several suggestions for improvement, namely the delivery of material that could be slower, examples that were more specific and appropriate.&lt;/em&gt;&lt;/p&gt;
Organizational Prowess of School Principals and Institutional Excellence

This quantitative study unveils the organizational prowess exhibited by public school principals in Mafraq city and its correlation with institutional excellence. The study's sample included 350 teachers from schools in Mafraq city, Jordan, during the first academic semester of 2022-2023. The study employed a questionnaire to collect data and the Social Statistical Package for the Social Sciences (SPSS).The findings of the study are as follows: (i) Institutional excellence is a fundamental requirement for administrative development aimed at elevating performance levels and accomplishing goals through the skills and abilities of teachers. (ii) The positive correlation between organizational prowess and institutional excellence is attributed to the presence of managers who can adapt to change and transition their administrations from traditional approaches to participatory methods. (iii) School principals play the most prominent role in achieving organizational prowess when they possess awareness and ingenuity in investing intellectual capital and providing an organizational climate that encourages creativity and innovation.
Integrating Estimation of Inflation Threshold and Nowcasting Regional Economic Growth Using Classical, Machine Learning, and Ensemble Method

In line with the global economic recovery, Indonesia's post-pandemic economic recovery is experiencing major challenge caused by uncertainty due to increasing inflationary pressure. Efforts to reduce inflationary fluctuations were carried out by the government in order to maintain optimal economic growth under the inflation targeting policy framework. Hence it is necessary to inspect the inflation threshold for economic growth in determining the inflation target. This study attempts to integrate inflation threshold estimation and nowcasting modelling of regional economic growth as an early warning system for achieving the government's economic growth target by utilizing several classical, machine learning, and ensemble models. Threshold estimation for the National level and East Java is applied to provincial groups obtained from clustering time series with dynamic time wrapping (DTW), author's justification, and the west-east region. In addition, estimates were also carried out in the period before Covid-19 as well as the entire period and the addition of predictor variables to test the sensitivity of the inflation threshold estimate. The results show that there was a non-linear relationship between inflation and economic growth with an inverted U shape. There are differences in the inflation threshold range due to provincial groupings, addition of control variables, both for the National level and for East Java. Evaluation of the inflation level in East Java and eight cities shows that in several periods, East Java inflation has exceeded Bank Indonesia's ITF target. Taking into account the estimated threshold range, it is recommended to set a more flexible inflation target at the regional economic level. Finally, the results of nowcasting with several specifications, such as the use of machine learning models and weighted ensemble models based on accuracy, as well as out-sample predictions with one-step ahead can improve the accuracy and consistency of the model for both short-term and long-term.
Designing a Building for Shadow Puppet Shows in Semarang City

The study aim is how to create a different building design that is able to attract the interest of young people who are able to accommodate and also preserve the art of Shadow Puppet (shadow puppet) that it is known by the wider community. Based on the shortage of shadow puppet show facilities in Semarang as the capital of Central Java, the procurement of a shadow puppet performance facility which has educational facilities for the Dalang or puppeteer profession is felt to be the right solution to preserve and revive this art of Shadow Puppet. The design of the shadow puppet show building in Semarang City raises the philosophy of the mountains of Shadow Puppet called “Kayon". Harmony with Nature as the architectural theme of the shadow puppet performance building was chosen so that the architecture of the building can live in harmony with nature by understanding and accepting the inanimate reality of natural forces.
EmoEye: Eye-Tracking and Biometrics Database for Emotion Recognition

Emotion recognition using Machine Learning algorithms is often used both in science and commerce. Responding to the demand for deep learning techniques of automatic emotion detection using biological signals and our own business needs as a neuromarketing laboratory, we created a large dataset of eye tracking and biometrics data suitable for emotion recognition tasks. The EmoEye database sample consisted of 200 people (147 women, 49 men, 4 non-binary individuals; 27.46 ± 11.45 years old). Each respondent was asked to view 316 images from the Open Affective Standardized Image Set (OASIS) and rate them on arousal and valence scales from the Self-Assessment Manikin questionnaire. Eye tracking, galvanic skin response (GSR), and photoplethysmogram were recorded throughout the experiment. Demographic data was also collected for each respondent. The image ratings on the valence scale did not differ statistically from the standard ratings of the corresponding images for the original stimulus base. The overall distribution trends of ratings on both scales for different categories of images were similar for standard ratings and ratings obtained from our respondents. As a result of this study, a corpus of GSR, heart rate variability and eye movement reactions data (fixation coordinates; fixation duration; average pupil size for the right and left eye) was compiled and successfully trained on a multimodal neural network algorithm within our laboratory and is ready for further implementation.
Flood Hazard Maping Using QGIS Spatial Analysis in Bangko and Masjid Watershed at Riau, Indonesia

Flooding is a natural occurrence hazardous to people and properties and produces environmental and economic losses, especially in flood-prone areas. This study will be used to assess flood risk in the Bangko and Masjid watersheds. To analyze it, flood hazard mapping is arranged by slope, land cover, elevation, rainfall, buffer zone, and soil type, which is done with the help of a Geographic Information System (GIS), specifically QuantumGIS (QGIS). This system tool assesses each variable utilizing satellite spatial data and categorizes the vulnerability being very high, high, medium, low, and very low. Each map arrangement value is combined using an overlay value to determine the flood susceptibility. According to the mapping results, the Bangko and Masjid watersheds are highly vulnerable to flooding. Based on the results of the mapping, in the Bangko watershed 61% of the total area is vulnerable to flooding, and 20% has high to very high vulnerability, while in the Mosque watershed 84% of the area is vulnerable to flooding and 11% of the total area has high vulnerability. to very high. After validation of areas that are often affected by floods, it is known that these areas are in vulnerable to very vulnerable areas. This is due to the low watershed elevation and flat slope and high rainfall, as well as residential areas close to the river in the watershed. This is due to the watershed's low elevation and flat slope and the high rainfall, and the residential area close to the river in the watershed area. Hence, it is vital to establish a convenient and straight-off understandable flood hazard map that emphasizes the management mitigation effects.
The Impact of Indonesian Army Information and Data Processing Service Department Programming Training for Indonesian Army

&lt;p&gt;This research discusses the importance of programming training for soldiers of the Indonesian Army in facing the era of globalization and digital transformation. In this context, programming is recognized as a tool for developing creative and logical thinking skills, which are key in overcoming complex challenges both in the military and society. The purpose of this study was to determine the impact of programming training for Indonesian Army soldiers. The methodological approach used combines quantitative and qualitative methods, through an initial survey to identify the number of soldiers taking programming training as well as an in-depth interview approach to explore the impact of programming on military assignments. The results showed that programming training had a positive impact, not only on improving problem-solving skills but also on the development of logical, systematic thinking and analytical skills. From a military perspective, programming gives The Indonesian Army soldiers a competitive edge in addressing modern challenges, such as technology development, intelligence analysis, and data management. Programming training also forms habits of logical thinking that are useful in making everyday decisions. Therefore, programming is not only a technical skill but also a tool to deal with the changing dynamics of future battles and adapt to technological developments.&lt;/p&gt;
A novel package of key generation and integrity validation in symmetric key cryptography

In this paper we presents a new cryptography method on symmetric key Cryptography along with that it will ensure the integrity of the data during the transmission. Here encryption and decryption operation is performed on bit stream of data. It is suitable for any type of text files. This method of encryption is simple and powerful to secure data in network against passive attack like traffic analysis to provide data confidentiality. The Cryptography package deals with generating a simple encryption key by choosing a byte of data in the plaintext. By using that secure encryption key Plain text is converted to cipher text and in the destination Cipher text is converted to plain text by performing basic XOR and other operation. To ensure integrity of data available parity bit methods are applied.
Comparison of Error Rate Prediction Methods in Classification Modeling with Classification and Regression Tree (CART) Methods for Balanced Data

CART (Classification and Regression Tree) is one of the classification algorithms in the decision tree method. The model formed in CART is a tree consisting of root nodes, internal nodes, and terminal nodes. After the model is formed, it is necessary to calculate the accuracy of the model. The aims is to see the performance of the model. The accuracy of this model can be done by calculating the predicted error rate in the model. The error rate prediction method works by dividing the data into training data and testing data. There are three methods in the error rate prediction method, such as Leave One Out Cross Validation (LOOCV), Hold Out (HO), and K-Fold Cross Validation. These methods have different performance in dividing data into training data and testing data, so there are advantages and disadvantages to each method. Therefore, a comparison was made for the three error rate prediction methods with the aim of determining the appropriate method for the CART algorithm. This comparison was made by considering several factors, for instance variations in the mean, number of variables, and correlations in normal distributed random data. The results of the comparison will be observed using a boxplot by looking at the median error rate and the lowest variance. The results of this study indicate that the K-Fold Cross Validation has the median error rate and the lowest variance, so the most suitable error prediction method used for the CART method is the K-Fold Cross Validation method.
Pemodelan Waktu Survival Pasien Tuberkulosis menggunakan Regresi Cox Proportional Hazard dengan Data Tersensor

Cox proportional hazard regression is a type of survival analysis that can be applied to tuberculosis cases. This study aims to determine the Cox proportional hazard regression model and the factors that influence the survival time of tuberculosis patients at RSUP Dr. M. Djamil Padang. The survival period used is the time when the patient is taking TB treatment at RSUP Dr. M. Djamil Padang in 2021 until the patient is declared dead. The method used in the Cox Proportional Hazard Regression analysis is the Maximum Partial Likelihood Estimation Method. By using the cox proportional hazard regression model, the factors that influence the survival time of tuberculosis patients at RSUP Dr. M. Djamil's is BMI (X3) , leukocytes (X5) , fever (X9) , shortness of breath (X11) , and decreased appetite (X12) . The Cox Proportional Hazard Regression Model obtained is hi(t) = h0(t) exp(1,315X3 + 1,224X5 + 1,138X9 +1,623X11 + 1,251X12).
Comparison of the Chen and Sinsgh’s Fuzzy Time Series Methods in Forecasting Farmer Exchange Rates in Indonesia

Chen and Singh's Fuzzy Time Series Model is a forecasting method that uses the basi fuzzy logic in the process. The differences in the models are in the fuzzy logic relations. Chen's model uses Fuzzy Logical Relationship Groups. Meanwhile, the Singh model uses only Fuzzy Logical Relationships in the forecasting process. To find out the best model between the two models, forecasting the Farmer's Exchange Rate is carried out. Farmers' exchange rates are the option for observers of agricultural development in assessing the level of welfare of farmers in Indonesia. With changes in farmer exchange rates every month, it is necessary to forecast data in order to obtain an overview for the following month. Research used is applied research where the initial step is to study and analyze the theories related to our research, then colect the necessary data. The data used is data secondary data obtained online from the official website of the Badan Pusat Statistika (BPS). the forecasting results of the two models were compared using MAPE. The results of the comparison of the accuracy of the prediction accuracy of Chen and Singh's fuzzy time series models on farmers' exchange rates obtained Chen's MAPE fuzzy time series values ​​of 0.679% and Singh's fuzzy time series models of 0.354%. This means that the best forecasting model for farmer exchange rates in Indonesia is the Singh model.
THE UTILIZATION OF AUGMENTED REALITY TECHNOLOGY IN DETERMINING QIBLA DIRECTION (ANALYSIS OF MIQAT APPLICATIONS BY SAMER JOUDI)

Nowadays, around the world, there are numerous Qibla direction applications available on various websites and digital content providers. One of these is the Miqat application: Prayer Times, Qiblah, and Hilal Visibility, an augmented reality-based Android app for determining the direction of the Qibla. Samer Joudi, a geospatial technology specialist from the United Arab Emirates, released the Miqat app in 2015. As of November 20, 2022, more than 5,000,000 (five million) Android users had downloaded the Miqat app, giving it a rating of 4.6 out of 5. A 3D Qibla feature in the Miqat application uses augmented reality technology on the display to point users in the direction of the Kaaba. actual life. The results of this study are that the calculation method used by the Miqat application is the Vincenty formula, which is quite accurate (7 minutes difference from the calculation of the Istiwa'aini Qibla direction) because the Vincenty formula has high accuracy in determining the Qibla direction based on the ellipsoid shape of the earth. Then, determining the Qibla direction using the 3D Qibla feature with augmented reality technology is categorized as less accurate according to Slamet Hambali's level of accuracy, but still within tolerance limits according to Thomas Djamaluddin's level of accuracy. The accuracy of the Qibla direction of the Miqat application differs from 0o to 1o from the Qibla direction measured by Istiwa'aini.
Multi Aspect Sentiment Analysis of Mutual Funds Investment App Bibit Using BERT Method

With the rapid development of technology, an investor no longer needs to visit investment companies to make investments. Investors can conduct all investment transactions through their smartphone screens. Bibit is one investment application that can help investors invest in mutual funds. There are many reviews given by users every day, therefore, aspect-based sentiment analysis is needed to identify the aspects and sentiments of users from each review. BERT is one popular text classification method that currently has good performance. Therefore, aspect-based sentiment analysis will be carried out in this study using the BERT method with pre-trained IndoBERT on Bibit application reviews. From the multi-aspect sentiment analysis classification results, the service aspect had the highest average accuracy score of 0.92, the user satisfaction aspect had an average accuracy score of 0.87, and the system aspect had the lowest average accuracy score of 0.75. From the sentiment analysis results, the company can improve the system and service aspects of the Bibit application to provide better service &amp; functionality.
A Secured Audio Encryption Algorithm Based On Gauss and Henon Chaotic Maps

The complex and erratic dynamics of chaotic maps can be used for secure communication because they generate a key stream of data that appears random and difficult to predict. This paper proposes a new audio encryption algorithm based on 1D Gauss and 2D Henon chaotic maps. We employed two chaotic maps of various dimensions since a single chaotic map has a smaller key space and less security. The two chaotic maps are used to produce keys and these keys would be employed to carry out the operation of permutation and logical XOR operation upon the samples of the given input audio signal. The key generated by a 1D Gauss map is used to carry out the permutation operation. Moreover, the key generated by a 2D Henon chaotic map is used to carry out the logical operation. Several audio security tests and statistical analyses show that the suggested algorithm has good attack and security performance. The performance results of the offered algorithm reveal that it can be competitive with certain other previously proposed audio encryption techniques.