Reviewerly/exHarmony huggingface dataset:
Application of Polynomial Models in Bayesian Fusion of Humidity Sensors

Local polynomial trend models are a special class of state-space models that can be used without having the full information about the process under study, since most of their parameters are embodied in the state vector and estimated immediately. This makes them attractive for use in signal processing. The present work considers problems that arise when using a polynomial model with a local quadratic trend for Bayesian fusion of two humidity sensors. The unknown sensor biases make it impossible for the model to satisfy the observability conditions. There is currently no general solution to this problem. To overcome this difficulty, an approach is presented where the humidity measurement result implicitly includes the bias of one of the sensors. The results of the study can be used to fuse quantities other than humidity when two or more sensors are available.
Entanglement verification with deep semisupervised machine learning

Quantum entanglement lies at the heart of quantum information-processing tasks. Although many criteria have been proposed, efficient and scalable methods to detect the entanglement of generally given quantum states are still not available yet, particularly for high-dimensional and multipartite quantum systems. Based on FixMatch and Pseudo-Label methods, we propose a deep semisupervised learning model with a small portion of labeled data and a large portion of unlabeled data. The data-augmentation strategies are applied in this model by using the convexity of separable states and performing local unitary operations on the training data. We verify that our model has good generalization ability and gives rise to better accuracies than traditional supervised learning models with detailed examples.
Distrustful quantum steering

Quantum steering is an asymmetric form of quantum nonlocality where one can trust the measurements of one of the parties. In this work, inspired by practical considerations we investigate the scenario if one can not fully trust their measurement devices but only up to some precision. We first find the effect of such an imprecision on standard device-dependent quantum tomography. We then utilise this result to compute the variation in the local bound of any general steering inequality depending on the amount of trust one puts in one of the party's measurement devices. This is particularly important as we show that even a small distrust on Alice might cause the parties to observe steerability even if the quantum state is unsteerable. Furthermore, this effect becomes more relevant when observing higher dimensional quantum steering.
Does Economic Freedom Improve FDI Inflows in Sub-Saharan Africa?

The objective of this study is to investigate the effect of economic freedom on foreign direct investment (FDI) in Sub-Saharan Africa (SSA). To this end, we employed a panel data analysis across 41 countries over the period 2000-2020. We used a two-stage least squares (2SLS) estimation method to address potential endogeneity issues. We found that economic freedom plays a crucial role in enhancing FDI inflows in SSA. Specifically, the results suggest that certain dimensions of economic freedom, such as the size of government and the legal system and property rights. Have a positive effect on FDI inflows in SSA. Conversely, components or dimensions like sound money and regulation have a negative impact. Freedom to trade has a positive sign on FDI. Regardless, these effects are statistically insignificant. Based on these outcomes, SSA countries should prioritize consolidating public spending on infrastructure development and enhancing the quality of investments in human capital formation. Additionally, strengthening the legal framework by ensuring the rule of law and a functional justice system that effectively safeguards the rights of investors is also necessary.
XGBoost–SFS and Double Nested Stacking Ensemble Model for Photovoltaic Power Forecasting under Variable Weather Conditions

Sustainability can achieve a balance among economic prosperity, social equity, and environmental protection to ensure the sustainable development and happiness of current and future generations; photovoltaic (PV) power, as a clean, renewable energy, is closely related to sustainability providing a reliable energy supply for sustainable development. To solve the problem with the difficulty of PV power forecasting due to its strong intermittency and volatility, which is influenced by complex and ever-changing natural environmental factors, this paper proposes a PV power forecasting method based on eXtreme gradient boosting (XGBoost)–sequential forward selection (SFS) and a double nested stacking (DNS) ensemble model to improve the stability and accuracy of forecasts. First, this paper analyzes a variety of relevant features affecting PV power forecasting and the correlation between these features and then constructs two features: global horizontal irradiance (GHI) and similar day power. Next, a total of 16 types of PV feature data, such as temperature, azimuth, ground pressure, and PV power data, are preprocessed and the optimal combination of features is selected by establishing an XGBoost–SFS to build a multidimensional climate feature dataset. Then, this paper proposes a DNS ensemble model to improve the stacking forecasting model. Based on the gradient boosting decision tree (GBDT), XGBoost, and support vector regression (SVR), a base stacking ensemble model is set, and a new stacking ensemble model is constructed again with the metamodel of the already constructed stacking ensemble model in order to make the model more robust and reliable. Finally, PV power station data from 2019 are used as an example for validation, and the results show that the forecasting method proposed in this paper can effectively integrate multiple environmental factors affecting PV power forecasting and better model the nonlinear relationships between PV power forecasting and relevant features. This is more applicable in the case of complex and variable environmental climates that have higher forecasting accuracy requirements.
A Neurophysiological Evaluation of Cognitive Load during Augmented Reality Interactions in Various Industrial Maintenance and Assembly Tasks

Augmented reality (AR) has been shown to improve productivity in industry, but its adverse effects (e.g., headaches, eye strain, nausea, and mental workload) on users warrant further investigation. The objective of this study is to investigate the effects of different instruction methods (i.e., HoloLens AR-based and paper-based instructions) and task complexity (low and high-demanding tasks) on cognitive workloads and performance. Twenty-eight healthy males with a mean age of 32.12 (SD 2.45) years were recruited in this study and were randomly divided into two groups. The first group performed the experiment using AR-based instruction, and the second group used paper-based instruction. Performance was measured using total task time (TTT). The cognitive workload was measured using the power of electroencephalograph (EEG) features and the NASA task load index (NASA TLX). The results showed that using AR instructions resulted in a reduction in maintenance times and an increase in mental workload compared to paper instructions, particularly for the more demanding tasks. With AR instruction, 0.45% and 14.94% less time was spent on low- and high-demand tasks, respectively, as compared to paper instructions. According to the EEG features, employing AR to guide employees during highly demanding maintenance tasks increased information processing, which could be linked with an increased germane cognitive load. Increased germane cognitive load means participants can better facilitate long-term knowledge and skill acquisition. These results suggested that AR is superior and recommended for highly demanding maintenance tasks since it speeds up maintenance times and increases the possibility that information is stored in long-term memory and encrypted for recalls.
Holographic Quantum Simulation of Entanglement Renormalization Circuits

While standard approaches to quantum simulation require a number of qubits proportional to the number of simulated particles, current noisy quantum computers are limited to tens of qubits. With the technique of holographic quantum simulation, a D-dimensional system can be simulated with a (D−1)-dimensional subset of qubits, enabling the study of systems significantly larger than current quantum computers. Using circuits derived from the multiscale entanglement renormalization ansatz (MERA), we accurately prepare the ground state of an L=32 critical, nonintegrable perturbed Ising model and measure long-range correlations on the ten-qubit Quantinuum trapped-ion computer. We introduce generalized MERA networks that interpolate between MERA and matrix product state networks and demonstrate that generalized MERA can capture far longer correlations than a MERA with the same number of qubits, at the expense of greater circuit depth. Finally, we perform noisy simulations of these two network ansatzes and find that the optimal choice of network depends on the noise level, available qubits, and the state to be represented.2 MoreReceived 31 August 2022Revised 5 June 2023Accepted 13 July 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030334Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Quantum spin chainsTechniquesTheoretical & Computational TechniquesLattice models in condensed matterSpin lattice modelsSpin chainsQuantum spin chainsResearch AreasQuantum computationTechniquesQuantum spin chainsTensor network methodsCondensed Matter, Materials & Applied PhysicsQuantum Information, Science & Technology
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization

Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Arman Cohan, Kyle Lo. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Inductive Graph Neural Networks for Moving Object Segmentation

Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results in MOS, they have mainly relied on transductive learning which assumes access to the entire training and testing data for evaluation. However, this assumption is not realistic in real-world applications where the system needs to handle new data during deployment. In this paper, we propose a novel Graph Inductive Moving Object Segmentation (GraphIMOS) algorithm based on a Graph Neural Network (GNN) architecture. Our approach builds a generic model capable of performing prediction on newly added data frames using the already trained model. GraphI-MOS outperforms previous inductive learning methods and is more generic than previous transductive techniques. Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications.
Estimation of Distribution Algorithms in Machine Learning: A Survey

The automatic induction of machine learning models capable of addressing supervised learning, feature selection, clustering and reinforcement learning problems requires sophisticated intelligent search procedures. These searches are usually performed in the possible model structure spaces, leading to combinatorial optimization problems, and in the parameter spaces, where it is necessary to solve continuous optimization problems. This paper reviews how the estimation of distribution algorithms, a kind of evolutionary algorithm, can be used to address these problems. Topics include preprocessing, mining association rules, selecting variables, searching for the optimal supervised learning model (both probabilistic and nonprobabilistic models), finding the best hierarchical, partitional or probabilistic clustering, obtaining the optimal policy in reinforcement learning and performing inference and structural learning in Bayesian networks for association discovery. Interesting guidelines for future work in this area are also provided.
Puzzles in the hadronic contributions to the muon anomalous magnetic moment

We summarize recent developments in the Standard-Model evaluation of the anomalous magnetic moment of the muon $a_\mu$, both in the hadronic-light-by-light and hadronic-vacuum-polarization contributions. The current situation for the latter is puzzling as we are confronted with multiple discrepancies that are not yet understood. We present updated fits of a dispersive representation of the pion vector form factor to the new CMD-3 data set and quantify the tensions with the other high-statistics $e^+e^-\to\pi^+\pi^-$ experiments in the contribution to $a_\mu$ in the energy range up to $1$ GeV, as well as in the corresponding contribution to the intermediate Euclidean window.
Revolutionizing Digit Image Recognition: Pushing the Limits with Simple CNN and Challenging Image Augmentation Techniques on MNIST

This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition accuracy. The results showed that the use of CNN with image augmentation techniques was effective in improving digit recognition performance. In the data collection stage, we used the MNIST dataset consisting of images of handwritten digits as training and testing data. After building the CNN model, we apply image augmentation techniques such as rotation, shift, and flipping to the training data to enrich the data variety and prevent overfitting. The evaluation results show that the CNN model that has been trained with image augmentation techniques produces significant accuracy, with a maximum accuracy of 99.81%. We also performed an ensemble of several CNN models and found that this approach increased the digit recognition accuracy to 99.79%. This research has the potential for further development. Recommendations for further research include exploring more specific and complex image augmentation techniques, as well as using more challenging datasets. In addition, future research may consider improvements to the CNN architecture used or combining it with other methods such as recurrent neural networks (RNN).
Perspectives on Augmented and Virtual Reality (AVR) in Education: Current Technologies and the Potential for Education

By combining information in the form of image alternatives with a software programme that stores knowledge on real images, augmented and virtual reality (AVR) technologies aid in the explanation of concepts. This methodology is developed to improve educational learning through two-dimensional media in education. In bloom taxonomy approach to teaching, integrating AR technology with academic content results in a new kind of automated application that serves to reinforce the usefulness of teaching-learning for learners in real-world situations. The learning outcome, which includes knowledge level and performance engagement, has a significant impact on all phases of higher education, from course planning to student evaluation and grading. AVR is a novel technique that combines elements of omnipresent computing, tangible computing, and social computing. This mode offers different affordances, combining the physical and virtual worlds, with continuous and implicit purposes of reading and interactivity. Digital resources are high-potential educational technology that enhances learning by supporting the learning environment through numerous e-resources. The various universities and Technical and Vocational Education Training (TVET) institutions give students an opportunity to complete an experiential learning component in their studies in order to complete their qualifications with the help of AVR implementation. This chapter provides an introduction to Augmented and Virtual reality (AVR) technology, the current status in education from different viewpoints, key technologies, and strategies mentioned in the context of higher educational learning output of students through these applications.
Generative AI and Lexicography: The Current State of the Art Using ChatGPT

Abstract In this article, all ten papers and talks that have been devoted to the use of ChatGPT in lexicography so far are critically analysed, their results tabulated and cross-compared, from which the leading trends are determined. Extrapolating from the trendlines, a single short but robust new prompt is fine-tuned with which articles from different word classes are generated fully-automatically for a dictionary which compares favourably to the best practice in dictionary compilation. The conclusion is that a new age, that of the successful application of generative AI in lexicography, has dawned.
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

AI explanations are often mentioned as a way to improve human-AI decision-making, but empirical studies have not found consistent evidence of explanations' effectiveness and, on the contrary, suggest that they can increase overreliance when the AI system is wrong. While many factors may affect reliance on AI support, one important factor is how decision-makers reconcile their own intuition---beliefs or heuristics, based on prior knowledge, experience, or pattern recognition, used to make judgments---with the information provided by the AI system to determine when to override AI predictions. We conduct a think-aloud, mixed-methods study with two explanation types (feature- and example-based) for two prediction tasks to explore how decision-makers' intuition affects their use of AI predictions and explanations, and ultimately their choice of when to rely on AI. Our results identify three types of intuition involved in reasoning about AI predictions and explanations: intuition about the task outcome, features, and AI limitations. Building on these, we summarize three observed pathways for decision-makers to apply their own intuition and override AI predictions. We use these pathways to explain why (1) the feature-based explanations we used did not improve participants' decision outcomes and increased their overreliance on AI, and (2) the example-based explanations we used improved decision-makers' performance over feature-based explanations and helped achieve complementary human-AI performance. Overall, our work identifies directions for further development of AI decision-support systems and explanation methods that help decision-makers effectively apply their intuition to achieve appropriate reliance on AI.
Wallet-Based Transaction Fraud Prevention Through LightGBM With the Focus on Minimizing False Alarms

E-wallets’ rising popularity can be attributed to the fact that they facilitate a wide variety of financial activities such as payments, transfers, investments, etc., and eliminate the need for actual cash or cards. The confidentiality, availability, and integrity of a user’s financial information stored in an electronic wallet can be compromised by threats such as phishing, malware, and social engineering; therefore, fintech platforms employ intelligent fraud detection mechanisms to mitigate the problem. The purpose of this study is to detect fraudulent activity using cutting-edge machine learning techniques on data obtained from the leading e-wallet platform in Turkey. After a comprehensive analysis of the dataset’s features via feature engineering procedures, we found that the LightGBM approach had the highest detection accuracy of fraudulent activity with 97% in the experiments conducted. An additional key objective of reducing false alerts was accomplished, as the number of false alarms went from 13,024 to 6,249. This approach resulted in the establishment of a machine-learning model suitable for use by relatively small fraud detection teams.
Automatic Generation of Image Caption Based on Semantic Relation using Deep Visual Attention Prediction

While modern systems for managing, retrieving, and analyzing images heavily rely on deriving semantic captions to categorize images, this task presents a considerable challenge due to the extensive capabilities required for manual processing, particularly with large images. Despite significant advancements in automatic image caption generation and human attention prediction through convolutional neural networks, there remains a need to enhance attention models in these networks through efficient multi-scale features utilization. Addressing this need, our study presents a novel image decoding model that integrates a wavelet-driven convolutional neural network with a dual-stage discrete wavelet transform, enabling the extraction of salient features within images. We utilize a wavelet-driven convolutional neural network as the encoder, coupled with a deep visual prediction model and Long Short-Term Memory as the decoder. The deep Visual Prediction Model calculates channel and location attention for visual attention features, with local features assessed by considering the spatial-contextual relationship among objects. Our primary contribution is to propose an encoder and decoder model to automatically create a semantic caption on the image based on the semantic contextual information and spatial features present in the image. Also, we improved the performance of this model, demonstrated through experiments conducted on three widely used datasets: Flickr8K, Flickr30K, and MSCOCO. The proposed approach outperformed current methods, achieving superior results in BLEU, METEOR, and GLEU scores. This research offers a significant advancement in image captioning and attention prediction models, presenting a promising direction for future work in this field.
Comparative Analysis of LPC and MFCC for Male Speaker Recognition in Text-Independent Context

peech is the utmost communication medium for human beings which conveys rich and valuable information such as accent, gender, emotion and unique identity. Therefore, automatic speaker recognition can be developed based on unique characteristics of one’s speech and utilized for applications such as voice dialing, online banking, and telephone shopping to verify the identity of its users. However, retrieving salient features which are capable of identifying speakers is a challenging problem in speech recognition systems since speech contains abundant information. In this study, a total of 438 audio data obtained from speakers uttering speech in text-independent context is proposed using speech data elicited from three Malay male speakers. The performance of two popularly used feature extraction techniques namely, linear prediction coefficients (LPC) and Mel-frequency cepstral coefficients (MFCC) were compared using discriminant analysis model. Although both features yielded impressive outcomes, the MFCC features surpassed that of LPC by achieving a higher accuracy rate of 99.09%, which was 4.34% higher than the latter.