Reviewerly/exHarmony huggingface dataset:
Mobility-Aware Federated Learning Considering Multiple Networks

Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics. For instance, mobility can hinder training by causing a client dropout when a device loses connection with other devices on the network. To address this issue, we propose a FL coordination algorithm, MoFeL, to ensure efficient training even in scenarios with mobility. Furthermore, MoFeL evaluates multiple networks with different central servers. To evaluate its effectiveness, we conducted simulation experiments using an image classification application that utilizes machine models trained by a convolutional neural network. The simulation results demonstrate that MoFeL outperforms traditional training coordination algorithms in FL, with 156.5% more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects.
Volatility effects of cryptocurrencies on foreign tourism in India

ABSTRACTABSTRACTCryptocurrencies and tourism have gained traction worldwide in the last few years. However, no research has been conducted to understand the relationship between the two. This paper examines the impact of the volatility spillover effects (VSE) of cryptocurrencies on the tourism sector in India. Using monthly time-series data (from August 2015 to January 2021) of the selected cryptocurrencies and foreign tourist arrivals (FTA) and foreign exchange earnings (FEE) from foreign tourism, we assess the volatility (short and long-term) impacts of cryptocurrencies on tourism (through changes in the monthly number of FTAs in India and FEEs of India through foreign tourism). The study applies Multivariate GARCH models (BEKK-GARCH and mGJR-GARCH). The findings suggest that there is an existence of volatility connections between cryptocurrencies and foreign tourism in India. These findings have noticeable implications for policymakers to understand the importance of cryptocurrency and blockchain for tourism sector policies in India.KEYWORDS: Volatilitycryptocurrencymultivariate GARCHBEKK GARCHmGJR GARCHtourism Disclosure statementNo potential conflict of interest was reported by the author(s).
Optimality conditions for spatial search with multiple marked vertices

We contribute to fulfill the long-lasting gap in the understanding of the spatial search with multiple marked vertices. The theoretical framework is that of discrete-time quantum walks (QW), i.e., local unitary matrices that drive the evolution of a single particle on the lattice. QW based search algorithms are well understood when they have to tackle the fundamental problem of finding only one marked element in a $d\text{\ensuremath{-}}\text{dimensional}$ grid and it has been proven they provide a quadratic advantage over classical searching protocols. However, once we consider searching more than one element, the behavior of the algorithm may be affected by the spatial configuration of the marked elements and even the quantum advantage is no longer guaranteed. Here our main contribution is threefold: (i) we provide sufficient conditions for optimality for a multi-items QWSearch algorithm; (ii) we provide analytical evidence that almost, but not all spatial configurations with multiple marked elements are optimal; and (iii) we numerically show that the computational advantage with respect to the classical counterpart is not always certain and it does depend on the proportion of searched elements over the total number of grid points.
Controlling Chaotic Maps using Next-Generation Reservoir Computing

In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic H\'enon map, including controlling the system between unstable fixed-points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only 10 data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.
Domain Adaptation using Silver Standard Labels for Ki-67 Scoring in Digital Pathology: A Step Closer to Widescale Deployment

Deep learning systems have been proposed to improve the objectivity and efficiency of Ki- 67 PI scoring. The challenge is that while very accurate, deep learning techniques suffer from reduced performance when applied to out-of-domain data. This is a critical challenge for clinical translation, as models are typically trained using data available to the vendor, which is not from the target domain. To address this challenge, this study proposes a domain adaptation pipeline that employs an unsupervised framework to generate silver standard (pseudo) labels in the target domain, which is used to augment the gold standard (GS) source domain data. Five training regimes were tested on two validated Ki-67 scoring architectures (UV-Net and piNET), (1) SS Only: trained on target silver standard (SS) labels, (2) GS Only: trained on source GS labels, (3) Mixed: trained on target SS and source GS labels, (4) GS+SS: trained on source GS labels and fine-tuned on target SS labels, and our proposed method (5) SS+GS: trained on source SS labels and fine-tuned on source GS labels. The SS+GS method yielded significantly (p < 0.05) higher PI accuracy (95.9%) and more consistent results compared to the GS Only model on target data. Analysis of t-SNE plots showed features learned by the SS+GS models are more aligned for source and target data, resulting in improved generalization. The proposed pipeline provides an efficient method for learning the target distribution without manual annotations, which are time-consuming and costly to generate for medical images. This framework can be applied to any target site as a per-laboratory calibration method, for widescale deployment.
A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

In this paper, we propose a low complexity distributed approach to address the multitarget detection/tracking problem in the presence of noisy and missing data. The proposed approach consists of two components: a distributed flooding scheme for measurements exchanging among sensors and a sampling-based clustering approach for target detection/tracking from the aggregated measurements. The main advantage of the proposed approach over the prevailing Markov-Bayes-based distributed filters is that it does not require any priori information and all the information required is the measurement set from multiple sensors. A comparison of the proposed approach with the available distributed clustering approaches and the cutting edge distributed multi-Bernoulli filters that are modeled with appropriate parameters confirms the effectiveness and the reliability of the proposed approach.
Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games

Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90\% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.
Retracted: New Algorithm of Traditional Chinese Medicine and Protection of Intangible Cultural Heritage Based on Big Data Deep Learning

[This retracts the article DOI: 10.1155/2022/1645204.].
Implementasi dan Manfaat ICE-Breaking pada Proses Pembelajaran di SMP Negeri 11 Pekanbaru

In the learning process, the teacher's ability to direct students' attention and motivation to learn is tested, especially at the elementary school level, where students do not yet know how to improve themselves, at this level they often do not focus on receiving learning, so students no longer receive attention and motivation. Teachers must understand the psychological state of students in order to create a good, fun and effective learning atmosphere. So that the learning objectives or achievements of the day are realized. One way that can be done to attract students' attention is to apply ice-breakers in learning activities that are used to be able to arouse enthusiasm and also to attract students' attention. the results of the study it can be concluded that the application of the ice breaking strategy can overcome student boredom in the science learning process at SMP Negeri 11 Pekanbaru.
How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear Problems

Competitive co-evolutionary algorithms (CoEAs) do not rely solely on an external function to assign fitness values to sampled solutions. Instead, they use the aggregation of outcomes from interactions between competing solutions allowing to rank solutions and make selection decisions. This makes CoEAs a useful tool for optimisation problems that have intrinsically interactive domains.
Achievable Minimally Contrastive Counterfactual Explanations

Decision support systems based on machine learning models should be able to help users identify opportunities and threats. Popular model-agnostic explanation models can identify factors that support various predictions, answering questions like “What factors affect sales?” or “Why did sales decline?”, but do not highlight what a person should or could do to get a more desirable outcome. Counterfactual explanation approaches address intervention, and some even consider feasibility, but there has been no evaluation of their suitability for real-time applications, such as question answering. Here we address this gap by introducing a novel model-agnostic method that provides specific, feasible changes that would impact the outcomes of a complex black-box AI model for a given instance and assess its real-world utility by measuring its real-time performance and ability to find achievable changes. The method uses the instance of concern to generate high-precision explanations and then applies a secondary method to find minimally contrastive and maximally probable high-precision counterfactual explanations, while limiting contrasted features to changes that are achievable. We demonstrate that using this method, it is possible to find such explanations quickly enough for use in real-time systems. High-precision achievable minimally contrastive explanations would be useful in applications where people seek remedial actions or question how effective a proposed remedy is likely to be.
Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Abstract This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. By analyzing 150 research papers, we investigate major publication channels, temporal trends, prevalent CNN models, primary data sets, accuracy levels, primary research focuses, and future prospects for improving CNN-based facial recognition. A major focus is placed on identifying prevalent CNN architectures, techniques used for facial recognition and shedding light on the evolving landscape of CNN designs. Furthermore, we examine the datasets used for training and testing CNNs, and evaluate the accuracy levels achieved by these models. Lastly, we discuss future directions for enhancing CNN-based facial recognition, including addressing bias and fairness, improving robustness to environmental variations, privacy preservation, and exploring transfer learning and multimodal fusion. This paper serves as a valuable resource, summarizing major trends in CNN-based face recognition. It provides insights for researchers and practitioners, guiding future advancements in this rapidly evolving field.
Effects of clouds and aerosols on downwelling surface solar irradiance nowcasting and sort-term forecasting

Abstract. Solar irradiance nowcasting and short-term forecasting are important tools for the integration of solar plants in the grid. Understanding the role of clouds and aerosols in those techniques is essential for improving their accuracy. In this study, we introduce the improvements in the existing nowcasting/short-term forecasting operational systems SENSE/NextSENSE, based on a new configuration and upgrading of cloud and aerosol inputs methods and also, we investigate the limitations of such model evaluation with surface-based sensors due to cloud effects. We assess the real-time estimates of surface global horizontal irradiance (GHI) produced by the improved SENSE2 operational system at high spatial and temporal resolution (~5 km, 15 min) for a domain including Europe and Middle East-North Africa (MENA) region and the short-term forecasts of GHI up to 3 h ahead by the NextSENSE2 system, against ground-based measurements from 10 stations across the model domain, for a whole year (2017). Results show that the GHI estimates are within +/-50 W/m2 (or +/-10 %) of the measured GHI for 61 % of the cases, after the new model configuration and a proposed bias correction. The bias ranges between -12 W/m2 to 23 W/m2 (or 2 % to 29 %) with mean value 11.3 W/m2 (2.3 %). The correlation coefficient is between 0.83 to 0.96 with mean value 0.93. Statistics are improved a lot when integrating in daily and monthly scales (mean bias 6.6 W/m2 and 5.7 W/m2, respectively). We demonstrate that the main overestimation of the SENSE2 GHI is linked with the underestimation of cloud optical thickness from the Meteosat Second Generation (MSG) satellites, while the relatively low overestimation linked with aerosol optical depth (AOD) forecasts (derived from Copernicus Atmospheric Monitoring Service – CAMS) results in low overestimation of clear sky GHI. The highest deviations are associated with cloudy atmospheric conditions with clouds obscuring the sun over the ground-based station. Thus, they are much more linked with satellite/ground-based comparison limitations than the actual model performance. The NextSENSE2 GHI forecasts based on the cloud motion vector (CMV) model, outperform the smart persistence forecasting method, which assumes the same cloud conditions for the future time steps. The forecasting skill (FS) of the CMV based model compared to the persistence approach increases with cloudiness (FS up to ~20 %), linked mostly to periods with changes in cloudiness, that persistence by definition fails to predict. Our results can be useful for further studies on satellite-based solar model evaluations and, in general, for the operational implementation of solar energy nowcasting and short-term forecasting, supporting solar energy production and management.
Strategic thinking In Artificial Intelligence And Expert:Problem Solving and Creativity

This research presents the unprecedented potential of GPT-4, OpenAI's advanced language model, in the role of a workplace consultant. Past AI research mainly focuses on structural problems, when the answer is well-defined. Here we propose ill-structured problem-solving, which requires strategic thinking, creativity, and decision-making.
Efficiently Guiding K-Robots Along Pathways with Minimal Turns

INTRODUCTION: This paper addresses the navigation of a team of k protector robots within pathways, focusing on minimizing the total number of turns. These robots utilize orthogonal routes known as watchman routes, which prioritize finding the shortest path while maintaining visibility of all points in the environment from at least one robot on its designated route. The main objective of this research is to optimize robot navigation by reducing the overall number of turns.OBJECTIVES: The primary objective of this study is to develop a linear-time algorithm that efficiently processes and determines routes for k robots within a specified area. By minimizing the number of turns, this algorithm aims to enhance the navigation capabilities of watchman robots, enabling them to effectively traverse complex environments.METHODS: This research employs techniques derived from computational geometry to investigate the navigation of protector robots. The focus is on developing an algorithm that can efficiently process and determine the optimal routes for the robots, considering factors such as visibility and shortest path length. The algorithm is designed to minimize the number of turns while ensuring efficient coverage of the environment.RESULTS: The main results of this paper include the development of a linear-time algorithm for determining routes for a team of k protector robots. The algorithm efficiently processes the input data and produces separate routes for each robot. By minimizing the number of turns, the algorithm improves the overall navigation efficiency of the robots. The results demonstrate the effectiveness of the algorithm in optimizing robot paths and reducing the complexity of navigation in real-world scenarios.CONCLUSION: In conclusion, this research contributes to the field of robotic systems by addressing the navigation challenges faced by a team of protector robots. The introduced linear-time algorithm optimizes the routes for k robots, aiming to minimize the total number of turns. The outcomes of this study have significant implications for the advancement of watchman robots, enhancing their coverage and surveillance capabilities in real-world applications. The algorithm’s efficiency and effectiveness in minimizing turns open new opportunities for developing efficient navigation strategies in complex environments.
Exploiting Relations, Sojourn-Times, and Joint Conditional Probabilities for Automated Commit Classification

The automatic classification of commits can be exploited for numerous applications, such as fault prediction, or determining maintenance activities.Additional properties, such as parent-child relations or sojourn-times between commits, were not previously considered for this task.However, such data cannot be leveraged well using traditional machine learning models, such as Random forests.Suitable models are, e.g., Conditional Random Fields or recurrent neural networks.We reason about the Markovian nature of the problem and propose models to address it.The first model is a generalized dependent mixture model, facilitating the Forward algorithm for 1st-and 2nd-order processes, using maximum likelihood estimation.We then propose a second, non-parametric model, that uses Bayesian segmentation and kernel density estimation, which can be effortlessly adapted to work with nth-order processes.Using an existing dataset with labeled commits as ground truth, we extend this dataset with relations between and sojourn-times of commits, by re-engineering the labeling rules first and meeting a high agreement between labelers.We show the strengths and weaknesses of either kind of model and demonstrate their ability to outperform the state-of-the-art in automated commit classification.
Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks

In this article, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration. Momentum is calculated and aggregated in the three tiers. We provide convergence analysis for HierMo, showing a convergence rate of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{T})$</tex-math></inline-formula> . In the analysis, we develop a new approach to characterize model aggregation, momentum aggregation, and their interactions. Based on this result, we prove that HierMo achieves a tighter convergence upper bound compared with HierFAVG without momentum. We also propose HierOPT, which optimizes the aggregation periods (worker-edge and edge-cloud aggregation periods) to minimize the loss given a limited training time. By conducting the experiment, we verify that HierMo outperforms existing mainstream benchmarks under a wide range of settings. In addition, HierOPT can achieve a near-optimal performance when we test HierMo under different aggregation periods.
Students' Learning Difficulties at the Open University Webinar Tutorial

The Open University (UT) is one of the pioneer universities that implement distance learning. However, students still encounter learning difficulties which become obstacles in distance learning. The purpose of this research is to analyses the construct of learning difficulties of students’ learning at Open University and to compare the contribution of indicator variables to the construct. This research uses a combination method of Concurrent Embed model, the primary method used is factor analysis method (EFA and CFA) and qualitative descriptive method as a secondary method. The research respondents were 126 students who had attended the webinar tutorial (Tuweb) at least twice and voluntarily filled out the questionnaire. Primary data were collected through online questionnaires and interviews. Quantitative data were analyzed using EFA and CFA, while quantitative data were analyzed descriptively qualitatively. The results showed that there are six factors of learning difficulties, each of which consisted of 4-9 indicator variables. The indicator variables that contribute the most to learning difficulties in Tuweb according to student perceptions include: (1) there are no clear guidelines regarding the online learning procedures that are carried out; (2) learning materials/content delivered by online tutors are difficult to understand; (3) the internet signal is not stable/good from students’ location; (4) students' families are constrained in providing facilities for online learning/webinar tutorials due to low income; (5) students do not understand how to study online; (6) The device used by the tutor is constrained during the Tuweb learning process.
Leveraging Satisfiability Modulo Theory Solvers for Verification of Neural Networks in Predictive Maintenance Applications

Interest in machine learning and neural networks has increased significantly in recent years. However, their applications are limited in safety-critical domains due to the lack of formal guarantees on their reliability and behavior. This paper shows recent advances in satisfiability modulo theory solvers used in the context of the verification of neural networks with piece-wise linear and transcendental activation functions. An experimental analysis is conducted using neural networks trained on a real-world predictive maintenance dataset. This study contributes to the research on enhancing the safety and reliability of neural networks through formal verification, enabling their deployment in safety-critical domains.