Reviewerly/exHarmony huggingface dataset:
ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task learning (MTL) can predict multiple ADMET endpoints with fewer labels, but ensuring task synergy and highlighting key molecular substructures remain challenges. To tackle these issues, this work elaborates a multi-task graph learning framework for predicting multiple ADMET properties of drug-like small molecules (MTGL-ADMET) by holding a new paradigm of MTL, "one primary, multiple auxiliaries." It first adeptly combines status theory with maximum flow for auxiliary task selection. The subsequent phase introduces a primary-task-centric MTL model with integrated modules. MTGL-ADMET not only outstrips existing STL and MTL methods but also offers a transparent lens into crucial molecular substructures. It is anticipated that this work can promote lead compound finding and optimization in drug discovery.
The Environmental Footprint of Bitcoin Mining Across the Globe: Call for Urgent Action

Abstract Based on a multi‐attribute assessment of the environmental impacts and challenges associated with global Bitcoin (BTC) mining activities around the globe, we call for urgent action by the scientific, policy, and advocacy communities. The worldwide BTC mining network consumed 173.42 TWh of electricity during the 2020–2021 period, bigger than the electricity consumption of most nations. The mining process emitted over 85.89 Mt of CO 2 eq in the same timeframe, equivalent to the emission caused by burning 84 billion pounds of coal or running 190 natural gas‐fired power plants. The environmental footprint of BTC mining is not limited to greenhouse gas emissions. In 2020–2021, the global water footprint of BTC mining was about 1.65 km 3 , more than the domestic water use of 300 million people in rural Sub‐Saharan Africa. The land footprint of the global BTC mining network during this period was more than 1,870 square kilometers, 1.4 times the area of Los Angeles. These striking numbers highlight the heavy reliance of the BTC network on fossil fuels and natural resource‐intensive energy sources, resulting in major but unmonitored and unregulated environmental footprints. To mitigate the environmental costs of BTC mining, immediate policy interventions, technological advancements, and scientific research are crucial. Proposed measures include enhanced transparency, economic and regulatory tools, developing energy‐efficient alternative coins, and the adoption of greener blockchain validation protocols.
Anti-jamming Research of UAV Communication Based on Link-16 Datalink

In the context of Link-16 datalink UAV operation, aiming at the communication interference problem in UAV combat, taking the suppressive interference model as an example, the three-layer modeling model of UAV communication jammer based on Link-16, Link11 and Link-22 datalink is constructed on the OPNET software simulation platform. The model details the parameter meanings of each layer of the model, and uses suitable simulation scenarios for experimental verification. Through the comparative analysis of the experimental results, it is proved that Link-16 has strong anti-interference. The study of anti-jamming of Link-16 datalink has been a popular issue, and the experimental comparative study in this paper can provide a good understanding of the relevant communication capabilities of this datalink. Link-16’s stronger anti-jamming study lays the groundwork for subsequent jamming studies. Its unique immunity to jamming aids militarized warfare.
Fraud Detection using Recurrent Neural Networks for Digital Wallet Security

E-wallet use has exploded, bringing in a new era of convenient and secure digital transactions; nevertheless, it has also led to a surge in fraudulent transactions. Using Recurrent Neural Networks (RNNs), this research provides an experimental approach for detecting fraudulent use of e-wallets. To detect fraudulent behaviors in e-wallet transactions, we have developed Long-Short Term Memory (LSTM) architecture to make use of the sequential nature of transaction data. Using the United Payment dataset for training and testing, the proposed model successfully classifies fraudulent transactions while reducing the number of false positives. The findings underline the utility of the RNN-based strategy in strengthening the safety and reliability of e-wallet systems, lead off for more secure online financial dealings. This study adds to the growing body of literature on detecting fraudulent use of electronic wallets by developing a model that makes use of deep learning to enhance fraud detection effectiveness in the ever-changing domain of digital payments.
A Modern Turkish Poet: Fine-Tuned GPT-2

Generative tasks are getting more realistic thanks to the improvements in deep learning. Text generation is getting increasingly important as LLMs (large language models) get more advanced. Even though ChatGPT gave rise to many computer-generated literary works, it has several limitations, such as not being open-sourced and can't be fine-tuned. Because other LLMs are understudied in Turkish, they usually can't be used efficiently to generate literary work. In this paper, we fine-tuned GPT-2 [1] models on sixty Turkish poem categories and trained five models that convey different emotions and write on different topics. The results are gathered in a 70-page Turkish poem book named “Gerçekligin İçinde” that contains 50 poems in total, 10 poems for each chapter. The book got published by Amazon [2], Goodreads [3] and Google Books [4].
Blockchain-Powered Solution to Safeguard IoT Devices Against Attacks

In recent years, the development of the Internet of Things (IoT) has gained motion, surpassing previous growth rates. However, safeguarding IoT devices remains a daunting challenge. Blockchain is offering the potential solutions to this quandary. This paper presents a blockchain-powered approach to detect and prevent cyber-attacks targeting IoT devices. The proposed approach aims to establish a secure and user-friendly environment by coupling the immutable and decentralized nature of blockchain technology. This paper begins by providing an overview of IoT and Blockchain (BC) technology, followed by an exploration of the diverse challenges and attacks encountered at different layers of IoT systems. Additionally, related work in the field is reviewed. Moreover, the paper excavate into the proposed framework, which involves employing the Remix IDE to create Smart Contracts for IoT device registration. Algorithms for IoT device registration and IoT Network Monitoring are presented and discussed. Implementations of validation and verification techniques is done to identify and baffle cyber-attacks. The results demonstrate that the proposed solution successfully identifies and detects attacks on any IoT device.
Multi-Objective Approach for Detecting Vulnerabilities in Ethereum Smart Contracts

Smart contracts, which provide user-defined logic to blockchains, have become increasingly popular in recent years due to their decentralized system architecture. They are executable programs that can automate transactions on the ethereum blockchain. However, security concerns with certain aspects of smart contracts can be challenging to address. Therefore, this paper proposed a multi-objective approach using a neural network makes it a more scalable and effective tool for detecting smart contract vulnerabilities than traditional approaches. With the increasing complexity of smart contracts and the growing importance of security in the blockchain space, a technique like multi-objective is becoming increasingly necessary to ensure the safety and reliability of decentralized applications. The proposed approach evaluated over 11000 real word ethereum smart contracts and detected two vulnerabilities without expert knowledge. The results showed that it achieved an average F1-score of 86.7 and 84.4 percent for reentrancy and timestamp vulnerability, respectively, indicating that the proposed approach has an impressive level of accuracy in classifying complex smart contracts, which has significant implications in the blockchain security field.
A Survey on Cross-Chain Asset Transfer Schemes: Classification, Challenges, and Prospects

Blockchain technology has experienced a rapid growth in recent years, leading to the emergence of various blockchain-based applications. One of the most prominent examples of such applications is the creation of asset in the form of token. However, asset on a chain are difficult to transfer to another chain due to the heterogeneity of different types of blockchains. Cross-chain asset transfer technology enables the transfer of value between blockchains and can improve the scalability of blockchains. This paper provides a systematic overview of cross-chain asset transfer schemes, introducing their classification, main challenges, and representative implementations. Furthermore, this paper summarizes and proposes the future research directions of cross-chain asset transfer schemes.
Optimizing the Performance of Kalman Filter and Alpha-Beta Filter Algorithms through Neural Network

In this paper, we have developed two neural network-based algorithms: the neural network-based Kalman filter (KF) algorithm and the neural network-based alpha-beta (α-β) filter algorithm. These algorithms incorporate a neural network to improve prediction accuracy and performance. Both algorithms consist of three input layers (temperature sensor, humidity sensor, and sensor readings), 15 hidden layers, and different output layers. The KF algorithm has a single output layer, while the alpha-beta filter algorithm has two output layers. These output layers dynamically interact with the KF algorithm and α-β filter to predict the final output values. For the KF algorithm, we consider two factors: R computation and the noise factor F. To evaluate the performance of these algorithms, we utilize the root mean square error (RMSE). The sensor readings for both algorithms are relatively high, specifically 5.215. Through the neural network-based alpha-beta filter, we achieved a minimum error of 3.21. In the case of the neural network based Kalman filter, we obtained the best-case result of 2.41 with R=14 and F=0.01. The proposed neural network-based system yields improved results compared to the simple Kalman filter and alpha-beta filter algorithms.
A Comparative Study on Time Series Prediction of Photovoltaic-Power Production Through Classic Statistical Techniques and Short-Term Memory Networks

The inherent variability in the power production of renewable energy sources (RES) limits the effectiveness of energy management systems (EMS) since optimal dispatch on power networks highly depends on the accuracy of predictors associated with the energy output and load demand. Consequently, power prediction tools for variable time horizons allow for improving energy management decisions. In this context, this work presents a detailed methodology for the deployment of predictive models for the photovoltaic (PV) power output of a small solar farm. The prediction models process a PV power dataset's time series using statistical techniques and neural networks with long-short term memory (LSTM). Before the data fitting, we develop a data preprocessing system, which involves evaluating missing data in the time series and getting descriptive analysis of the data set to either complete portions or delete atypical data. The results strongly suggest that the LSTM network performs better than the statistical model in exchange for more considerable computation times for long-term predictions.
Evaluating Regression Models with Partial Data: A Sampling Approach

Machine learning methods rely on data to uncover relationships between inputs and outputs of complex systems, making it crucial to have sufficient amounts of representative data. Therefore, recent research has focused on choosing informative input-output pairs, i.e., labeled data, to facilitate the adoption of machine learning in science and engineering applications. Despite these efforts, estimating the test error with a limited amount of labeled data still needs to be explored. Hence, this paper investigates a novel framework for selecting informative labeled samples from a set of unlabeled testing instances to evaluate regression models with the quadratic loss function. Key contributions of this work include the design of nonuniform sampling distributions over candidate testing points and the deployment of an unbiased estimator to achieve desirable tradeoffs between estimation accuracy and testing data size. Comprehensive experimental results corroborate the impressive performance and flexibility of the proposed approach in real-world applications, such as reducing the standard deviation of the resulting estimator by almost a factor of two compared to uniform sampling. The paper concludes with practical advice for researchers and practitioners who encounter difficulties related to limited labeled data.
Mining Association Rules for a Sustainable Supply Chain Using Improved Multiobjective Crystal Structure Algorithm

This paper introduces a supply chain quality sustainability decision support system (QSDSS). It uses association rule techniques to provide better logistics plans and handle risk in the supply chain. An improved Multiobjective Crystal Structure Algorithm using centroid opposition based learning and gaussian perturbation is proposed to avoid premature convergence and escape from local optimal. The experimental study is carried out in two phases, using two classic benchmark suites and the “DataCo SMART SUPPLY CHAIN” dataset for big data analysis.
Defamation with Bayesian Audiences

How strictly should the law regulate false defamatory statements? We first show that the presence of judicial errors often puts defamation law on a Laffer curve: regulation that is too lax or too strict is inferior to moderate regulation. While moderate regulation is ideal, it is not always attainable because of practical and legal constraints. With these constraints, we consider a Bayesian audience that takes the strictness of defamation law into account when evaluating statements. The optimal standard is then laxer than is prescribed by standard models with naive audiences. These findings underscore the importance of accounting for audience effects in analyzing defamation law.
The Credibility Effect: Defamation Law and Audiences

What should be the legal response to false statements? In the context of defamation law, courts try to set a standard that balances the interests of speakers and their potential targets. This article empirically demonstrates an unappreciated effect of such decisions on third parties: a credibility effect. Using a series of lab experiments, I find that defamation law makes individuals more trusting of reports from various media. This credibility effect is desirable when the report is true but can lead to unintended consequences in the case of misinformation. In particular, the credibility effect is shown to cast a stigma on innocent targets who choose not to file lawsuits. The existence of the credibility effect calls for different balances than are currently employed in defamation law; challenges the vindication justification; and, more broadly, illustrates the limits of policies intended to fight misinformation.
Il ruolo del contatto nell’aspetto perfettivo in griko: forme sintetiche ed analitiche in competizione

The aim of this article is to discuss a change in the expression of perfective aspect in Griko. According to Rohlfs (1977), this variety of Italo-Greek only possesses a synthetic past perfective form, thus failing to mark a distinction between the past punctual and present perfect values of the perfective. And yet, some changes in the expression of this macrocategory were already taking place at the time of Rohlfs when an originally resultative periphrastic form was beginning to be used beyond its original limits. By taking into account a wider array of sources, including both early and contemporary written attestations, as well as data collected with native speakers, we show how the regrammaticalization of this analytic construction, from resultative to present perfect marker, was already under way in the late nineteenth century and is now completed for some speakers. The article concludes with a number of reflections about the role played by contact in these developments.
Model-Based Feature Selection for Neural Networks: A Mixed-Integer Programming Approach

In this work, we develop a novel input feature selection framework for ReLU-based deep neural networks (DNNs), which builds upon a mixed-integer optimization approach. While the method is generally applicable to various classification tasks, we focus on finding input features for image classification for clarity of presentation. The idea is to use a trained DNN, or an ensemble of trained DNNs, to identify the salient input features. The input feature selection is formulated as a sequence of mixed-integer linear programming (MILP) problems that find sets of sparse inputs that maximize the classification confidence of each category. These “inverse” problems are regularized by the number of inputs selected for each category and by distribution constraints. Numerical results on the well-known MNIST and FashionMNIST datasets show that the proposed input feature selection allows us to drastically reduce the size of the input to $$\sim $$ 15% while maintaining a good classification accuracy. This allows us to design DNNs with significantly fewer connections, reducing computational effort and producing DNNs that are more robust towards adversarial attacks.
Robust Fluid Motion Estimator Based on Attentional Transformer

Estimating complex fluid motions from successive images, i.e., Particle Image Velocimetry (PIV), is of great research significance in physics and engineering applications. Although deep learning-based methods have made great progress for fluid motion estimation, it still remains a challenge in terms of robustness and generalization ability. To address this issue, we present a novel fluid flow estimator called RFMFlowNet in this paper, to robustly estimate complex fluid motion using attentional Transformer. Concretely, the feature encoder based on Transformer is customized to enhance features of two frames globally and stably. Here we also enlarge the receptive field of the encoder according to the characteristics of the flow image to extract more effective information. Then, efficient global matching is performed using 4D correlation volume. Furthermore, a refined flow field is iteratively predicted using a GRU-based optimizer. Extensive experiments, including on synthetic and real-world images, are performed to assess the proposed approach. Experimental results demonstrate that the RFMFlowNet achieves new state-of-the-art performance on the public dataset. Meanwhile, our model enjoys great robustness and generalization ability to challenging flow images, reasonably predicting the evolution trend of fluid flows.
Learning the Bias Weights for Generalized Nested Rollout Policy Adaptation

Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for single player games and optimization problems. In this paper we propose to modify GNRPA in order to automatically learn the bias weights. The goal is both to obtain better results on sets of dissimilar instances, and also to avoid some hyperparameters settings. Experiments show that it improves the algorithm for two different optimization problems: the Vehicle Routing Problem and 3D Bin Packing.
A survey on sign language literature

Individuals with hearing impairment encounter various types and levels of difficulties, highlighting the need for more research to provide effective support. One significant difficulty is communication and interaction with others. Given that these individuals employ sign language as their primary mode of communication, there exists a notable information void among those who can hear in comprehending and interpreting sign language. Consequently, to bridge this gap, the field of sign language research has seen significant growth. In this study, we emphasize the importance of sign language recognition and translation and provide a comprehensive review of relevant research conducted in this field. Our examination encompasses multiple perspectives, including sign language recognition, translation, and the availability of datasets. By exploring these aspects, we aim to contribute to the advancement of sign language literature and its practical applications.
mgpr: An R package for multivariate Gaussian process regression

<h2>Abstract</h2> Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables. We propose an R package that provides an easy-to-use interface for multivariate GPR. The <i>mgpr</i> package was originally developed for remote sensing-based forest inventories that require multivariate prediction of forest attributes. The <i>mgpr</i> package supports both univariate and multivariate responses using a separable kernel and includes a robust hyperparameter estimation algorithm. The <i>mgpr</i> package is suitable for various regression problems with single response or multiple responses and provides good prediction performance.
Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation

Retrieving relevant multimedia content is one of the main problems in a world that is increasingly data-driven. With the proliferation of drones, high quality aerial footage is now available to a wide audience for the first time. Integrating this footage into applications can enable GPS-less geo-localisation or location correction. In this paper, we present an orientation-guided training framework for UAV-view geo-localisation. Through hierarchical localisation orientations of the UAV images are estimated in relation to the satellite imagery. We propose a lightweight prediction module for these pseudo labels which predicts the orientation between the different views based on the contrastive learned embeddings. We experimentally demonstrate that this prediction supports the training and outperforms previous approaches. The extracted pseudo-labels also enable aligned rotation of the satellite image as augmentation to further strengthen the generalisation. During inference, we no longer need this orientation module, which means that no additional computations are required. We achieve state-of-the-art results on both the University-1652 and University-160k datasets.