Reviewerly/exHarmony huggingface dataset:
Mitigating Digital Asset Risks

The rapid emergence of digital assets, underpinned by technological advancements such as blockchain, distributed ledger technology (DLT), and smart contracts, has triggered a paradigm shift in the global financial ecosystem. These digital assets, which encompass cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies (CBDCs), hold the potential to transform financial markets by enabling new business models, investment opportunities, and efficient transaction mechanisms. However, their accelerated growth also introduces a unique set of challenges and risks, such as fraud, market manipulation, cybersecurity threats, and regulatory uncertainties.This position paper presents an interdisciplinary, empirical analysis of the digital asset landscape, focusing on the definition and classification of digital assets, their evolution from novelty to necessity, and the current state of adoption and regulation. We explore the various types of digital assets, their unique characteristics and use cases, and the technological innovations that have shaped their development, such as the advent of blockchain technology and the rise of decentralized finance (DeFi) and NFTs. Moreover, we examine the regulatory landscape surrounding digital assets, highlighting jurisdictional approaches, regulatory classifications, and key developments in the space, as well as the challenges and opportunities that regulators face in devising effective regulatory frameworks.To address the risks associated with the proliferation of digital assets, we outline several mitigation strategies and recommendations for regulators, market participants, and stakeholders based on quantitative analysis and empirical findings. These include balancing innovation and risk, by formulating regulations that safeguard the interests of consumers and investors while fostering an environment conducive to innovation; promoting global regulatory coordination and harmonization, to reduce the potential for regulatory arbitrage and enhance cross-border cooperation; and leveraging regulatory sandboxes and innovation hubs, to support the growth of digital asset businesses and facilitate continuous learning and adaptation.By adopting a forward-looking and flexible approach to regulation and engaging in ongoing dialogue with market participants and stakeholders, regulators can ensure that the benefits of digital assets are realized while mitigating the associated risks.
Improving the Estimation of the Diffuse Component of Photosynthetically Active Radiation (PAR)

Abstract Most weather forecasting models are not able to accurately reproduce the great variability existing in the measurements of the diffuse component of photosynthetically active radiation (PAR; 400–700 nm) under all sky conditions. Based on the well‐known relationship between the diffuse fraction ( k ) and the clearness index ( k t ), this study addresses improvements in estimations by proposing adaptations of previous models, which were previously applied only to the total solar irradiance (TSI; 280–3,000 nm). In order to reproduce this variability, additional parameters were introduced. The models were tested employing a multisite database gathered at the Mediterranean basin. Since Artificial Neural Network (ANN) models are not limited to fixed coefficients to predict the diffuse fraction of PAR ( k PAR ), these types of models are more accurate than empirical ones, reaching determination coefficients ( r 2 ) up to 0.998. However, the simpler linear model proposed by Foyo‐Moreno et al. (2018), https://doi.org/10.1016/j.atmosres.2017.12.012 shows a similar performance to the ANN models, directly predicting the diffuse component of PAR (PAR Diffuse ) from TSI Diffuse , with a r 2 up to 0.997. Results obtained here also determine that the most important variables for estimating PAR Diffuse are k t or k t,PAR , and the apparent solar time (AST). Therefore, PAR Diffuse can be modeled using TSI measured in most radiometric stations, reaching r 2 up to 0.858 for empirical models and 0.970 for ANN models. This modified approach will allow for the very accurate construction of long‐term data series of PAR Diffuse in regions where continuous measurements of PAR are not available.
Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer

Objective To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches. Methods Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality. Results Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts. Conclusions We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.
RICO-MR: An Open-Source Architecture for Robot Intent Communication through Mixed Reality

This article presents an open-source architecture for conveying robots’ intentions to human teammates using Mixed Reality and Head-Mounted Displays. The architecture has been developed focusing on its modularity and re-usability aspects. Both binaries and source code are available, enabling researchers and companies to adopt the proposed architecture as a standalone solution or to integrate it in more comprehensive implementations. Due to its scalability, the proposed architecture can be easily employed to develop shared Mixed Reality experiences involving multiple robots and human teammates in complex collaborative scenarios.
Improving forecasting accuracy using quantile regression neural network combined with unrestricted mixed data sampling

A traditional regression method involving time series variables is often observed at the same frequencies. In a situation where the frequencies differ, the higher ones are averaged or aggregated to the lower frequency. A Mixed Data Sampling (MIDAS) regression model was introduced to address such problems. In any country, stakeholders are interested in monitoring and forecasting accurately the Gross Domestic Product (GDP) using the dynamics of macroeconomic variables. We applied the hybrid QRNN-U-MIDAS model to forecast quarterly GDP using monthly and weekly data. The Quantile Regression Neural Network (QRNN) is designed to model nonlinear relationships amongst data sampled at the same frequency. Therefore, we take advantage of QRNN skills using the optimization techniques of gradient descent-based algorithms to optimise the estimated loss function Ea (\tau), and introduce them into the U-MIDAS framework, which can handle mixed data frequencies, and construct a QRNN-U-MIDAS model. The suggested hybrid QRNN-U-MIDAS model was implemented in an R-package that we created to perform both simulation and real-time data applications. The findings indicate that the QRNN-U-MIDAS regression model outperforms competing models in terms of its capacity for prediction across the conditional distribution of a response variable with a comprehensive view of the information contained in the variables, which is lacking in other competing models like U-MIDAS, ANN-U-MIDAS etc. More so, this novel model will add to the existing works of literature on robust forecasting models.
Using Less but Important Information for Feature Distillation

The purpose of feature distillation is that using the teacher network to supervise student network so that the student network can mimic the intermediate layer representation of the teacher network. The most intuitive way of feature distillation is to use the Mean-Square Error (MSE) to optimize the distance of feature representation at the same level for both networks. However, one problem in feature distillation is that the dimension of the intermediate layer feature maps of the student network may be different from that of the teacher network. Previous work mostly elaborated a projector to transform feature maps to the same dimension. In this paper, we proposed a simple and straightforward feature distillation method without additional projector to adapt the feature dimension inconsistency between the teacher and the student networks. We consider the redundancy of the data and show that it is not necessary to use all the information when performing feature distillation. In detail, we propose a cut-off operation for channel alignment and use singular value decomposition (SVD) for knowledge alignment so that only important information is transferred to the student network to solve the dimension inconsistency problem. Extensive experiments on several different models show that our method can improve the performance of student networks.
Amortized Variational Inference via Nosé-Hoover Thermostat Hamiltonian Monte Carlo

Sampling latents from the posterior distribution efficiently and accurately is a fundamental problem for posterior inference. Markov chain Monte Carlo (MCMC) is such a useful tool to do that but at the cost of computational burden since it needs many transition steps to converge to the stationary distribution for each datapoint. Amortized variational inference within the framework of MCMC is thus proposed where the learned parameters of the model are shared by all observations. Langevin autoencoder is a newly proposed method that amortizes inference in parameter space. This paper generalizes the Langevin autoencoder by utilizing the stochastic gradient Nosé-Hoover Thermostat Hamiltonian Monte Carlo to conduct amortized updating of the parameters of the inference distribution. The proposed method improves variational inference accuracy for the latent by subtly dealing with the noise introduced by stochastic gradient without estimating that noise explicitly. Experiments benchmarking our method against baseline generative methods highlight the effectiveness of our proposed method.
Learning Adaptable Risk-Sensitive Policies to Coordinate in Multi-agent General-Sum Games

In general-sum games, the interaction of self-interested learning agents commonly leads to socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH). Previous works address this challenge by sharing rewards or shaping their opponents' learning process, which require too strong assumptions. In this paper, we observe that agents trained to optimize expected returns are more likely to choose a safe action that leads to guaranteed but lower rewards. To overcome this, we present Adaptable Risk-Sensitive Policy (ARSP). ARSP learns the distributions over agent's return and estimates a dynamic risk-seeking bonus to discover risky coordination strategies. Furthermore, to avoid overfitting training opponents, ARSP learns an auxiliary opponent modeling task to infer opponents' types and dynamically alter corresponding strategies during execution. Extensive experiments show that ARSP agents can achieve stable coordination during training and adapt to non-cooperative opponents during execution, outperforming a set of baselines by a large margin.
Evaluation of Chatbot Technology: The Case of Greece

In recent years, the field of Artificial Intelligence has made significant strides, particularly in advancing chatbots through Natural Language Processing (NLP) technology. Recently, however, there has been intense competition in this industry, with major tech companies consistently introducing new and improved solutions. Nevertheless, the Greek context introduces several unique challenges and obstacles to the adoption of modern solutions, owing to the distinctiveness and relative rarity of the Greek language, as well as the limited financial resources of the Greek economy. The objective of this study is to assess the performance of chatbots in terms of the quality of their responses, including relevance, naturalness, coherence, accuracy, and vocabulary, and to gauge user experience and satisfaction. An additional objective is to acquire a comprehensive comparative overview of chatbot performance in Greece, both on a per-question basis and when comparing related questions. The chosen method for evaluation is a guided interview employing closed-type questions. The intention is to gather structured and quantified data in an area where the typical internet user may not possess extensive familiarity or prior experience with such evaluations. Conclusions were drawn for each question, enabling a focused and comparative assessment of solution quality, identification of potential trends, and verification of response consistency.
Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm

Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals are permeable in a certain frequency range. Attenuation occurs in signals outside this frequency range. There are two types of filters: passive and active. Active filters consist of passive and active components, including transistors and operational amplifiers, but also require a power supply. In contrast, passive filters only consist of resistors and capacitors. Therefore, active filters are capable of generating signal gain and possess the benefit of high-input and low-output impedance. In order for active filters to be more functional, the parameters of the resistors and capacitors in the circuit must be at optimum values. Therefore, the active filter is discussed in this study. In this study, the tree seed algorithm (TSA), a plant-based optimization algorithm, is used to optimize the parameters of filters with tenth-order Butterworth and Bessel topology. In order to improve the performance of the TSA for filter parameter optimization, opposition-based learning (OBL) is added to TSA to form an improved TSA (I-TSA). The results obtained are compared with both basic TSA and some algorithms. The experimental results show that the I-TSA method is applicable to this problem by performing a successful prediction process.
Peningkatan Keterampilan Membaca Puisi dengan Menggunakan Media Video pada Peserta Didik Kelas X UPT SMA Negeri 4 Sidrap

This research describes improving poetry reading skills using video media for UPT students at SMA Negeri 4 Sidrap. This research aims to: (1) determine the increase in teacher activity in learning to read poetry using video media, (2) determine the increase in student activity in learning to read poetry using video media, and (3) to determine the increase in poetry reading skills using video media for students. The subject of this study was class X IPA 1 UPT SMA Negeri 4 Sidrap, which consisted of 28 students. This type of research is classroom action research (CAR) using the Kurt Lewin model which consists of three action cycles. Data collection techniques are obtained from observation, interviews, tests and documentation. The results of this classroom action research were obtained by showing: (1) teacher activities in using video media in improving students' poetry reading skills in cycle I obtained a score of 66.66% in the good category, cycle II experienced an increase with a score of 72.91% in the good category, and in cycle III they got a score of 85.41% in the very good category, (2) students' activities in learning to read poetry using video media increased from cycle I got a score of 63.77% in the fair category, cycle II got a score of 71.16% good category, and in cycle III obtained a score of 78.43% in the good category, and (3) the results of improving students' poetry reading skills using video media in cycle I obtained a score of 46.42% in the poor category, cycle II experienced an increase with a score of 60 .71% in the sufficient category, and in cycle III there was an increase with a score of 82.14% in the very good category. The conclusion in this research is that learning Indonesian in poetry reading skills using video media can improve the quality of learning, namely increasing teacher skills, student activities and students' skills in reading poetry.
Detection of Fittings Based on the Dynamic Graph CNN and U-Net Embedded with Bi-Level Routing Attention

Accurate detection of power fittings is crucial for identifying defects or faults in these components, which is essential for assessing the safety and stability of the power system. However, the accuracy of fittings detection is affected by a complex background, small target sizes, and overlapping fittings in the images. To address these challenges, a fittings detection method based on the dynamic graph convolutional neural network (DGCNN) and U-shaped network (U-Net) is proposed, which combines three-dimensional detection with two-dimensional object detection. Firstly, the bi-level routing attention mechanism is incorporated into the lightweight U-Net network to enhance feature extraction for detecting the fittings boundary. Secondly, pseudo-point cloud data are synthesized by transforming the depth map generated by the Lite-Mono algorithm and its corresponding RGB fittings image. The DGCNN algorithm is then employed to extract obscured fittings features, contributing to the final refinement of the results. This process helps alleviate the issue of occlusions among targets and further enhances the precision of fittings detection. Finally, the proposed method is evaluated using a custom dataset of fittings, and comparative studies are conducted. The experimental results illustrate the promising potential of the proposed approach in enhancing features and extracting information from fittings images.
Feature Based Image Registration Using ORB and CNN for Remote Sensing Images

Objectives: To improve image registration by reducing the estimation error of the rotation transformation parameter under illumination change effect in remote sensing images using Oriented Fast and Rotated Brief (ORB) and Convolutional Neural Network (CNN). Also, to reduce computational complexity that can be increased due to use of CNN. Methods: The image registration process aligns two or more images geometrically and a novel feature based approaches for image registration is proposed here, where ORB and CNN are used to estimate rotation transformation parameter under illumination change effects in remote sensing images with novelty in generation of feature descriptor. The results of the proposed approach are compared with an approach which uses only ORB for both feature detection and descriptor generation. Findings: In the proposed approach, convolutional features from modified CNN are used in a novel manner with ORB descriptor to generate the final fusion descriptor. Also, this novel approach reduces the computational complexity by limiting the descriptor size that can be increased due to CNN. Here, three different combinations of CNN layers are provided for the generation of descriptor with ORB and this approach is also tested with transfer learning concept and other than remote sensing image, which shows improved results for taken cases. The results of novel approach show that the estimation of rotation transformation parameter and image registration is improved, and the estimation error is reduced to 0.1% to 0.9% for taken cases. Novelty: Novelty is provided in the generation of descriptor by fusion of CNN (modified visual geometry group (VGG19)) features with descriptor from ORB for reduction in estimation error, limiting descriptor size to reduce computational complexity, and improvement in image registration of remote sensing images. It is also tested for transfer learning case and other than remote sensing image, where improved results are also seen. Keywords: Image registration, CNN, Remote sensing, VGG19, Oriented fast and rotated brief
Blockchain-Based Platforms for Carbon Offsetting: A Survey of Existing Approaches and Their Potential to Promote Carbon Farming for Smallholders

This paper examines the use of blockchain technology in improving the carbon offset market and promoting carbon farming for smallholders. The paper presents a survey of six blockchain-based platforms for carbon offsetting, namely Blockchain for Climate Foundation, Open Climate Collabathon, Aircarbon Exchange, Cryptocarbon NFT, Toucan, and KlimaDAO. The platforms were evaluated based on their impact on the carbon offset market, and their potential to make carbon farming operational for smallholders was discussed. Overall, this paper demonstrates that blockchain technology has the potential to enhance the carbon offset market and empower smallholders in carbon farming.
3D Object Detection Using Multiple-Frame Proposal Features Fusion

Object detection is important in many applications, such as autonomous driving. While 2D images lack depth information and are sensitive to environmental conditions, 3D point clouds can provide accurate depth information and a more descriptive environment. However, sparsity is always a challenge in single-frame point cloud object detection. This paper introduces a two-stage proposal-based feature fusion method for object detection using multiple frames. The proposed method, called proposal features fusion (PFF), utilizes a cosine-similarity approach to associate proposals from multiple frames and employs an attention weighted fusion (AWF) module to merge features from these proposals. It allows for feature fusion specific to individual objects and offers lower computational complexity while achieving higher precision. The experimental results on the nuScenes dataset demonstrate the effectiveness of our approach, achieving an mAP of 46.7%, which is 1.3% higher than the state-of-the-art 3D object detection method.
Spot the Bot: Distinguishing Human-Written and Bot-Generated Texts Using Clustering and Information Theory Techniques

With the development of generative models like GPT-3, it is increasingly more challenging to differentiate generated texts from human-written ones. There is a large number of studies that have demonstrated good results in bot identification. However, the majority of such works depend on supervised learning methods that require labelled data and/or prior knowledge about the bot-model architecture. In this work, we propose a bot identification algorithm that is based on unsupervised learning techniques and does not depend on a large amount of labelled data. By combining findings in semantic analysis by clustering (crisp and fuzzy) and information techniques, we construct a robust model that detects a generated text for different types of bot. We find that the generated texts tend to be more chaotic while literary works are more complex. We also demonstrate that the clustering of human texts results in fuzzier clusters in comparison to the more compact and well-separated clusters of bot-generated texts.