Reviewerly/exHarmony huggingface dataset:
Constructing digital signature scheme based on the new hard problem on the elliptic curve

In this paper, the authors propose a solution to improve the security of the digital signature scheme, this solution is implemented on two levels of digital signature scheme construction. At the first level, the authors propose a new hard problem, different from the hard problems used before, and importantly, this hard problem belongs to the class of hard problems for which there is currently no solution (except by the “brute force attack” method). At the second level, the authors propose a method to construct new digital signature algorithms based on this hard problem.
Peningkatan Kualitas Statistik Resmi Produktivitas Padi melalui Imputasi Data Non-respons Menggunakan Model Aditif Geospasial

This study is motivated by the non-response problem in the Crop Cutting Survey conducted by the BPS-Statistics Indonesia as the official statistics provider. BPS has a vision of providing quality statistical data for advanced Indonesia. Handling non-response is essential to supporting this vision because non-response can potentially cause some sample characteristics to be unrepresented. This study proposed a non-response data imputation technique through statistical modeling. The proposed model was an additive model with the addition of geospatial smoothing functions of thin plate regression splines (TP) and Gaussian process (GP). Selection of the best model based on the smallest MSEP of 1000 iterations. Then we compared the average rice productivity between listwise deletion and imputation techniques through three scenarios of non-response data. The results showed that the model with the addition of the GP smoothing function gave the best performance with the smallest MSEP. The other results showed that the imputation method of non-response data is better than ignoring non-response. BPS can consider the imputation method to improve the quality of official statistics on rice productivity.
On Finite and Non-Finite Bayesian Mixture Models

In this paper, a Bayesian paradigm of a mixture model with finite and non-finite components is expounded for a generic prior and likelihood that can be of any distributional random noise. The mixture model consists of stylized properties-proportional allocation, sample size allocation, and latent (unobserved) variable for similar probabilistic generalization. The Expectation-Maximization (EM) algorithm technique of parameter estimation was adopted to estimate the stated stylized parameters. The Markov Chain Monte Carlo (MCMC) and Metropolis–Hastings sampler algorithms were adopted as an alternative to the EM algorithm when it is not analytically feasible, that is, when the unobserved variable cannot be replaced by imposed expectations (means) and when there is need for correction of exploration of posterior distribution by means of acceptance ratio quantity, respectively. Label switching for exchangeability of posterior distribution via truncated or alternating prior distributional form was imposed on the posterior distribution for robust tailoring inference through Maximum a Posterior (MAP) index. In conclusion, it was deduced via simulation study that the number of components grows large for all permutations to be considered for subsample permutations.
Macroeconomic Stability, Government Policies, and Financial Market Development: A Global Analysis of Transparent Governance, Anti-Monopoly Measures, and Legal Frameworks

This study explores the interplay between macroeconomic stability, government policies (transparency, anti-monopoly measures, legal frameworks), and financial market development. Drawing data from 125 countries over 11 years (2007-2017) with both parliamentary and presidential systems, various regression models are employed, including Ordinary Least Squares, Random and Fixed Effect Models. Prais-Winsten Regression addresses heteroskedasticity and autocorrelation. Results show a strong positive correlation between macroeconomic stability and financial market development. Transparent governance, anti-monopoly measures, and robust legal frameworks contribute significantly to both stability and financial growth. Transparent governance notably enhances the relationship between macroeconomic stability and financial market development globally and in presidential systems, but not in parliamentary systems. Effective anti-monopoly policies play a notable role in parliamentary systems, while legal frameworks maintain a positive influence across all scenarios. These findings are valuable for policymakers, investors, and researchers, informing their decision-making processes.
Programmable High-Precision Weight Bank Based on Integrated Semiconductor Optical Amplifier Array

The primary challenge in in-memory photonic computing lies in achieving high-precision weight control, which enables efficient parallel multiply accumulate (MAC) operations and enhances computing accuracy. Due to environmental factors like temperature fluctuations, achieving accurate weight control in photonic computing becomes challenging. To address the aforementioned issue, our solution involves an integrated array of semiconductor optical amplifiers (SOAs) as photonic synapses, forming a programmable weight bank. We have designed and fabricated a monolithically integrated 8-channel DFB-SOA chip, which demonstrates a remarkable record-high precision of 9.2 bits for weight control.
Type-2 Fuzzy LSTM for Nonlinear System Modeling

Type-2 fuzzy systems have a great adoption in different branches of engineering, due to the fact that this type of fuzzy systems are very well suited to tasks related to nonlinear systems. Data driven models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we show the advantages of type-2 fuzzy systems over type-1 fuzzy systems in modeling nonlinear systems. We combine Type-2 Takagi-Sugeno fuzzy model with the popular deep learning model, LSTM (long-short term memory), to overcome the disadvantages fuzzy model and neural network model. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in order to show the advantages of the type-2 fuzzy LSTM neural networks.
A Deep Learning Classifier Using Sliding Patches For Detection of Mammographical Findings

Mammography is known as one of the best forms to screen possible breast cancer in women, and recently deep learning models have been developed to assist the radiologist in the diagnosis. However, their lack of interpretability has become a significant drawback to their extended use in clinical practice. This paper introduces a novel approach for detecting and localising pathological findings in mammography exams through the use of a EfficientNet-based deep learning model. The model is trained using cropped segments of labelled pathological findings from Vindr Mammography Dataset. Achieving an average F1-score of 72.7 %, and reaching on mass and suspicious calcifications an F1-Score of 79.9 % and 84.5 % respectively. Using this classifier we propose a method to visualise from local information the regions of interest where pathological findings could be present on the complete image. Plus, we describe the limitations regarding area coverage of these patches on the model’s capability of generalization and certainty on its predictions, explaining its functionality.
Uncertainty Quantification for Efficient and Risk-Sensitive Reinforcement Learning

In complex real-world decision problems, ensuring safety and addressing uncertainties are crucial aspects. In this work, we present an uncertainty-aware Reinforcement Learning agent designed for risk-sensitive applications in continuous action spaces. Our method quantifies and leverages both epistemic and aleatoric uncertainties to enhance agent's learning and to incorporate risk assessment into decision-making processes. We conduct numerical experiments to evaluate our work on a modified version of Lunar Lander with variable and risky landing conditions. We show that our method outperforms both Deep Deterministic Policy Gradient (DDPG) and TD3 algorithms by reducing collisions and having significant faster training. In addition, it enables the trained agent to learn a risk-sensitive policy that balances performance and risk based on a specific level of sensitivity to risk required for the task.
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving

In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter difficulties when confronted with unexpected situations. At the same time, world models emerged in the field of model-based reinforcement learning as a way to enable agents to predict the future depending on potential actions. This led to outstanding results in sparse reward and complex control tasks. This work provides an overview of how world models can be leveraged to perform anomaly detection in the domain of autonomous driving. We provide a characterization of world models and relate individual components to previous works in anomaly detection to facilitate further research in the field.
An Imperial Analysis of Large Language Models for Automated Tweet Sentiment Prediction

Company' <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> brand perception majorly depends on customer experience and the reviews which follow that. A customer is capable of influencing many more people just on the basis of reviews given by him/her. Google released Pathways Language Model (PaLM) which is a major advancement in Artificial Intelligence (AI). It has been trained with the Pathways System, which allows it to generalize tasks in various domains. In this work, a trustworthy platform is provided for the examination of millions of people's continually moving and changing perspectives. Twitter data is captured, and effective sentiment and data analysis is used to generate trustworthy and helpful info graphics reflecting public opinion. Product sales, stock returns, election outcomes, and other commercial and social events may all be predicted and explained using the information found in tweets. Brands, product manufacturers, and other companies may utilise the information derived from data analysis to better understand their brand image, expand their market share by targeting the relevant demographics at the right moments, and enhance their offerings in terms of both quality and customer service.
Implementation of Extreme Learning Model for Cyber Security in IoT Networks

Cybersecurity in IoT (Internet of Things) networks faces several significant challenges and problems, primarily due to the unique characteristics of IoT devices and their widespread deployment. Cybersecurity is a major problem for IoT networks. Addressing these cybersecurity challenges in IoT networks requires a multi-pronged approach involving manufacturers, users, policymakers, and cybersecurity experts. This approach should include robust device authentication, encryption, regular updates, intrusion detection systems, and user education. Extreme Learning Machines (ELMs) have gained significant attention in the machine learning community due to their efficiency and effectiveness across diverse applications. In this paper, a new algorithm called Fusion Extreme Learning Machine with enhanced multi-feature integration (FELM-EMFI) is specifically tailored for enhancing the security of communication channels in Internet of Things (IoT) networks. By incorporating multiple features into the learning process, FELM-EMFI aim to bolster the reliability and robustness of communication channels within IoT systems. The suggested technique achieves the highest accuracy of 96.5%, outperforming the ELM method with 94.2%, the AVM strategy with 94.7%, and the BP approach with 91.2%. Consequently, the proposed approach demonstrates superior performance when compared to the current methodologies.
Synthetic Image Generator for Testing and Training Environments: A Deep Learning Survey

Image synthesis offers a way to quickly create huge amounts of datasets for regulating the manufacturing process to ensure optimal airflow as well as material while monitoring the generation of data sets variety for machine learning applications. Artificial information leads a vital role along with the training pipeline in the given requirements of deep learning applications. The present study offers an extensive collection of both the modern techniques and image synthesis for visual machine learning which employ for picture synthesizing. Image synthesis is described based on such a classification that takes modelling and rendering in the context of image production, and a categorization is also established regarding the computer vision applications which are utilized in. In order to encourage future picture production for machine learning, the designers concentrated on the method's computer graphics components. In the present process, each approach is evaluated for quality and reported performance, by suggesting as to what learning potential is anticipated. The present study acts as thorough reference and directs at both the application development and data production sides.
Fall Detection and Prevention using IoT in Senior Care Facilities

Using Internet of Things (IoT) devices, a novel method for enhancing the safety and comfort of senior care facilities is proposed, An aging population increases the risk of accidents in these facilities, underscoring the need for innovative solutions. This res earch introduces an innovative Internet of Things-based system for the detection and prevention of accidents. It employs cutting-edge machine learning algorithms with a network of strategically located Internet of Things (IoT) sensors to detect and respond to potential accidents. Comparing the system's sensitivity, specificity, and decreased false alarms to those of conventional methods, real-world testing and case studies demonstrate the system's efficacy in terms of its sensitivity, specificity, and reduced false alarms. The institution's staff can rest assured that the proposed Internet of Things solution will benefit the seniors under their care. The findings of this research are a significant contribution to the field of senior care and a guide for improving care for older people in institutional settings.
An Adaptive Local Context Extraction Method for Motion Prediction and Planning

Learning-based motion prediction and planning algorithms can efficiently extract and effectively represent the agent-map relationships in high-dimensional space. Generally, the performance is highly related to the neural network structure and quality of input data. However, existing works are more focused on exploring novel network structures while ignoring the form of data input. Typically, for a target agent to predict or plan, they simply input all road polylines within its R-meters radius as local context information and then use a network such as a cross-attention module to filter out the unimportant and irrelevant part of the local context. This simple strategy not only burdens the network in spatial and temporal computation consumption but also leads to much redundancy and overlapping when two target agents are very close. We propose our Adaptive Breadth-First Search (ABFS) local context extraction method to tackle these issues. ABFS inherits from breadth-first search (BFS), adding adaptive search depth based on the agent’s current state and changing the weights of the search direction (more focused on the roads forward). We compare our methods with previous radius search (RS) based and depth-first search (DFS) based context extraction methods. Systematic experiments are done based on the Differentiable Integrated Motion Prediction and Planning (DIPP) algorithm using Waymo motion dataset. We give both qualitative and quantitative results to show our method’s superiority.
Never Have I Ever Bingo: A Grammar-Learning Innovation for Acquiring the Present Perfect Tense in Writing

In order to guarantee that their pupils can learn the language effectively, English as a Second Language (ESL) educators have always equipped themselves with various educational approaches and strategies. In spite of this, ESL learners still find the present perfect tense one of the most challenging tenses to master as grammar structures and rules differ from their first language. Nevertheless, the implementation of language games has shown its effects on learners' knowledge, competency and motivation in learning a language, focusing on different targeted language areas and skills such as grammar. Accordingly, this paper aims to describe the design and development of ‘Never Have I Bingo’ through the ADDIE model as well as study its effectiveness on learners’ ability to use the present perfect tense in writing. This empirical study involved 31 Year 6 students in a primary school in Negeri Sembilan. The results of employed pre and post tests demonstrated a significant improvement in students’ ability to use the present perfect tense in writing sentences. Interviews and document analysis were also conducted to demonstrate students’ perceptions and effectiveness of the game. The study could provide insight to educators in developing and conducting games in grammar learning. Future studies might concentrate on creating games that improve the many grammar structures and rules used in other skills.
Anomaly Behavior Analysis for Sensors Fault Detection

In today's world, sensors play a crucial role, as they feed information to make accurate decisions and take actions; therefore, making sure that sensors behave correctly is critical. In this work, we focus on inspecting the data provided by sensors, aiming at discovering any issue due to malfunction, misuse, or any other source of error before the issue is propagated through the system. To achieve that, we propose a novel approach based on wavelets embedded in a microcontroller to analyze data from sensors. The objective is to determine whether the sensor is issuing correct data (normal behavior) or not (abnormal behavior), to prevent the error from reaching other parts of the system.
Complete photonic tensor convolution driven by single dataflow

Current photonic convolutional processors transform tensor convolutions into multi-channel general matrix multiplication (GeMM), leading to data replication and hardware complexity. In this study, we propose and experimentally demonstrate a photonic tensor processing unit (PTPU) with single dataflow, which offers a more concise approach to multi-channel standard tensor convolution processing. In experiment, we extracted features from a 3-channel (RGB) image in horizontal and vertical directions using an integrated multi-wavelength source. We then built a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{D}$</tex> -convolutional neural network to predict the presence of COVID-19 based on computer tomography (CT) scan data consisting of 64-channel tensors.
Perbandingan Kinerja Algoritma K-Means dan K-Medoids Dalam Klasterisasi Jumlah Tindak Pidana Kejahatan Berbasis Wilayah Kepolisian Daerah

Criminal acts are often a problem that occurs in Indonesia. Where currently the number of reports handled by the police regarding criminal acts is always there every day. Indonesia's population is increasing and the background of perpetrators who are unemployed is often one of the reasons why the police find it difficult to resolve criminal acts that occur due to limited human resources. To overcome this problem, information is needed that provides areas in Indonesia where criminal acts frequently occur so that the police can make decisions to allocate human resources to protect those jurisdictions from criminal acts that occur. Using data on criminal offenses and the employment of criminal offenders, namely not working from 2021, data was taken from the National Police Criminal Investigation Unit's Pusiknas Annual Journal. The data will be clustered using data mining techniques using the K-Means and K-Medoids algorithms. These 2 algorithms produced 2 clusters with the smallest Davies Bouldin index value found in the K-Means algorithm with a value of 0.272. With the research results which produced 2 clusters, it can be concluded that there are categories of high crime and low crime.
Journey of ChatGPT from Prompts to Stories in Games: the Positive, the Negative, and the Neutral

This paper presents a study on biases present in the story endings for story-driven games generated by ChatGPT. The study uses various prompts to assess the biases in ChatGPT's output. The results emphasize a consistent inclination towards positive endings in the stories generated by ChatGPT. Even when explicitly instructed to generate neutral endings, ChatGPT exhibited a bias towards positive outcomes. These biases raise concerns regarding the training data and alignment processes used by OpenAI to train ChatGPT, as they may reflect societal biases or the preferences of the majority of the data. Addressing these biases is crucial to ensure that these models align with societal norms and avoid reinforcing existing biases. Future studies should concentrate on developing methods to reduce biases in AI language models and enhance the ethical perspective of these technologies. Our source code and data are made publicly available at https://bit.ly/chatgpt-game-story-gen.
N<sup>2</sup>V<sup>2</sup>PRO: Neural Network Mapping Framework for a Custom Vector Processor Architecture

Convolutional neural networks (CNNs) have been demonstrated to be a successful approach in the field of artificial intelligence (AI). Deploying CNNs on embedded devices at a large scale would contribute significantly to the advancement and practical implementation of AI in various industries. However, the complexity of CNNs in terms of memory and operation requirements poses challenges in terms of computing performance, memory bandwidth, and flexibility of the executing hardware. This paper introduces a framework that addresses these issues through model quantization and hardware acceleration on a scalable vertical vector processor architecture. Firstly, the framework includes a method for layer fusion, which is designed to optimize the hardware utilization. Secondly, data storage is optimized to enhance memory efficiency. Lastly, CNNs are mapped onto the vertical vector processing concept of the hardware accelerator. The effectiveness of the proposed framework is evaluated by analyzing the accelerator efficiency based on a field-programmable gate array (FPGA). The results demonstrate that the framework offers flexibility, configurability, and efficient mapping for typical CNN implementations. The framework achieves up to 84% of the peak performance of the vector processor for the VGG net.