Reviewerly/exHarmony huggingface dataset:
FTX fiasco and global equity markets: evidence from event study approach

Purpose This study aims to investigate the impact of FTX bankruptcy on the global stock markets, including both the developed and emerging markets, as per the Morgan Stanley Capital Investment (MSCI) country classification. Design/methodology/approach Using the daily closing prices for leading stock market indices of all 47 countries in the MSCI market classification, comprising 23 developed markets and 24 emerging markets, the event study methodology is used to examine the impact of the event on developed markets, emerging markets and overall global equity markets. Findings The study finds heterogeneous effects of the event on different countries. Results indicate that overall global equity markets experienced a statistically significant positive cumulative average abnormal returns of 15.8533% in the complete event window of 28 days from t − 7 to t + 20. The authors conclude that traditional global equity markets can be used as a hedge against potential financial risk posed by unfavorable events in the cryptocurrency markets and have safe haven properties. Practical implications The study emphasizes the global financial system’s interconnectedness and the potential of traditional equity markets to hedge risks in the cryptocurrency market. The findings are relevant for investors seeking portfolio diversification and mitigating their exposure to potential risks in the cryptocurrency market. Originality/value To the best of the authors’ knowledge, the present study is the earliest attempt to comprehensively examine the impact of the bankruptcy of the world’s fourth largest cryptocurrency exchange, FTX, on the global equity markets.
CLARIN-Emo: Training Emotion Recognition Models Using Human Annotation and ChatGPT

In this paper, we investigate whether it is possible to automatically annotate texts with ChatGPT or generate both artificial texts and annotations for them. We prepared three collections of texts annotated with emotions at the level of sentences and/or whole documents. CLARIN-Emo contains the opinions of real people, manually annotated by six linguists. Stockbrief-GPT consists of real human articles annotated by ChatGPT. ChatGPT-Emo is an artificial corpus created and annotated entirely by ChatGPT. We present an analysis of these corpora and the results of Transformer-based methods fine-tuned on these data. The results show that manual annotation can provide better-quality data, especially in building personalized models.
Weakly Supervised 3D Multi-Person Pose Estimation for Large-Scale Scenes Based on Monocular Camera and Single LiDAR

Depth estimation is usually ill-posed and ambiguous for monocular camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate depth information in long-range scenes, it can benefit both the global localization of individuals and the 3D pose estimation by providing rich geometry features. Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light. Specifically, we design an effective fusion strategy to take advantage of multi-modal input data, including images and point cloud, and make full use of temporal information to guide the network to learn natural and coherent human motions. Without relying on any 3D pose annotations, our method exploits the inherent geometry constraints of point cloud for self-supervision and utilizes 2D keypoints on images for weak supervision. Extensive experiments on public datasets and our newly collected dataset demonstrate the superiority and generalization capability of our proposed method. Project homepage is at \url{https://github.com/4DVLab/FusionPose.git}.
A Comparison of YOLO Based Vehicle Detection Algorithms

The use of vehicle object detection in intelligent video surveillance and vehicle-assisted driving has expanded as science and technology have advanced. Traditional car object detection algorithms have some limitations in their generalization capacity and recognition rate. The primary goal of this survey is to detect the vehicle, which forms managing crucial traffic data, including vehicle detection, vehicle count, and vehicle movement. This research compares modern object detectors that incorporate traffic situation estimations To determine which version of the YOLO algorithm is the best for detecting the vehicle explained here. Process of the YOLO algorithm the dataset is the first clustered using the clustering analysis approach, and the network structure is improved to increase the vehicle prediction capacity and the final numbers of output grids. In the second process, it maximizes both input image and dataset collection. This research suggests a better vehicle identification technique based on YOLO (You Only Look Once) to address this issue. Three versions of the YOLO (You Only Look Once) algorithm are evaluated to detect the vehicle.
Model-Free Subsampling Method Based on Uniform Designs

Subsampling or subdata selection is a useful approach in large-scale statistical learning. Most existing studies focus on model-based subsampling methods which significantly depend on the model assumption. In this article, we consider the model-free subsampling strategy for generating subdata from the original full data. In order to measure the goodness of representation of a subdata with respect to the original data, we propose a criterion, generalized empirical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -discrepancy (GEFD), and study its theoretical properties in connection with the classical generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> -discrepancy in the theory of uniform designs. These properties allow us to develop a kind of low-GEFD data-driven subsampling method based on the existing uniform designs. By simulation examples and a real case study, we show that the proposed subsampling method is superior to the random sampling method. Moreover, our method keeps robust under diverse model specifications while other popular model-based subsampling methods are under-performing. In practice, such a model-free property is more appealing than the model-based subsampling methods, where the latter may have poor performance when the model is misspecified, as demonstrated in our simulation studies. In addition, our method is orders of magnitude faster than other model-free subsampling methods, which makes it more applicable for subsampling of Big Data.
Block Chain for Digital Financial Inclusion Towards Reduced Inequalities

Blockchain technology has shown significant promise in institutionalizing money transfers and extending access to financial services. The potential of this technology to simplify and replace the infrastructure that facilitates international payments and remittances, such as correspondent banking, has been investigated by regulatory authorities and industry professionals. This is achieved by verifying and documenting transactions using blockchain technology in a distributed ledger, ensuring that all transactions can be recorded and verified. In addition to improving financial services, blockchain technology has also demonstrated its potential in including formerly marginalized individuals and communities in mainstream financial systems. This has significant implications for sustainable development, as financial inclusion is a critical component of achieving economic growth and reducing poverty. One of the most important lessons that sustainable development has taught us is the importance of addressing inequality and promoting social and economic inclusion. Blockchain technology can play a critical role in achieving this goal by enabling secure and transparent financial transactions that are accessible to everyone, regardless of their location, background, or socioeconomic status. Overall, this chapter investigates the impact of blockchain technology on financial inclusion and sustainable development. It emphasizes the importance of leveraging technology to address social and economic inequality and highlights the potential of blockchain technology in achieving these goals.
Exploring international students’ adoption of AI-enabled voice assistants in enrolment decision making: a grounded theory approach

ABSTRACTABSTRACTThe proliferation and increasing significance of AI-enabled voice assistants (AIVAs) for consumer purchase decisions imply the need for a better understanding of their acceptance in the higher education (HE) enrollment context. Using the Grounded Theory (GT) research method and a consumer value perspective, we examine the enrolment decision-making of international students in the presence of AIVAs to propose a conceptual model linking value to adoption. Findings highlight the influence of the key antecedents of hedonic and functional value in AIVA adoption. The findings also indicate that the student and the AIVA can co-create novel experiences during information search and evaluation of enrollment alternatives. Some participants proactively suggest a larger role for the AIVA to aid their enrollment decision-making. Points of attention for university marketing managers are suggested based on the proposed core concept and the resulting tentative hypotheses.KEYWORDS: AI-enabled voice assistantsinternational marketing of higher educationuniversity marketing strategyconsumer behaviourinternational studentsenrollment decision-making Disclosure statementNo potential conflict of interest was reported by the authors.
PPFLHE: A privacy-preserving federated learning scheme with homomorphic encryption for healthcare data

Healthcare data are characterized by explosive growth and value, which is the private data of patients, and its characteristics and storage environment have brought significant issues of data privacy and security. People are reluctant to share their data for privacy concerns during machine learning. To balance this contradiction, Federated Learning was proposed as a solution to train on private data without sharing it. However, many studies show that there is still the possibility of privacy leakage during the training process of federated learning. In light of this, we propose a privacy-preserving federated learning scheme with homomorphic encryption(PPFLHE). Specifically, on the client side, homomorphic encryption technology is used to encrypt the training model shared by users to ensure its security and privacy. In addition, to prevent internal attacks, Access Control (AC) technology is used to confirm the user’s identity and judge whether it is trusted; on the server side, the Acknowledgment (ACK) mechanism is designed to remove the dropped or unresponsive users temporarily, which reduces the waiting delay and communication overhead, and solves the problem of user’s exiting during training. Theoretical analysis and experimental results show that the proposed scheme achieves high data utility and classification accuracy (81.53%), and low communication delay while achieving privacy preserving, compared to state-of-the-art methods.
A Hierarchical Approach to Anomalous Subgroup Discovery

Understanding peculiar and anomalous behavior of machine learning models for specific data subgroups is a fundamental building block of model performance and fairness evaluation. The analysis of these data subgroups can provide useful insights into model inner working and highlight its potentially discriminatory behavior. Current approaches to subgroup exploration ignore the presence of hierarchies in the data, and can only be applied to discretized attributes. The discretization process required for continuous attributes may significantly affect the identification of relevant subgroups.We propose a hierarchical subgroup exploration technique to identify anomalous subgroup behavior at multiple granularity levels, along with a technique for the hierarchical discretization of data attributes. The hierarchical discretization produces, for each continuous attribute, a hierarchy of intervals. The subsequent hierarchical exploration can exploit data hierarchies, selecting for each attribute the optimal granularity to identify subgroups that are both anomalous, and with enough elements to be statistically and practically significant. Compared to non- hierarchical approaches, we show that our hierarchical approach is more powerful in identifying anomalous subgroups and more stable with respect to discretization and exploration parameters.
PENGEMBANGAN KOMIK SEBAGAI MEDIA BELAJAR MATEMATIKA DI SEKOLAH DASAR

The curriculum in Indonesia has been developing from year to year, until now the curriculum applied is the independent curriculum that demands innovation in learning, including the use of teaching materials. In learning mathematics, the teaching materials used were less contextual and minimal in visualization until finally, comics were created as learning media. Characteristic comics can change material that is difficult to reach into material that is easily accessible, this happens because one of the comics is that it can visualize abstract material into something real. The purpose of this research is to make learning media in the form of comics which is further developed because it is based on local wisdom which is expected to be one of the media for learning mathematics for elementary school students. This study uses the Research and Development/ (RD) method, while the model used in this development is the ADDIE (Analysis, Design, Development, Implement, Evaluation) model. The results of this study obtained very good validation with an Aiken value of 0.76 which indicates that the learning media is valid. And, obtaining a practicality test score of 77% which shows that the media is practical and ready to be tested by elementary school students.
Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding

Over the years, the humanities community has increasingly requested the creation of artificial intelligence frameworks to help the study of cultural heritage. Document Layout segmentation, which aims at identifying the different structural components of a document page, is a particularly interesting task connected to this trend, specifically when it comes to handwritten texts. While there are many effective approaches to this problem, they all rely on large amounts of data for the training of the underlying models, which is rarely possible in a real-world scenario, as the process of producing the ground truth segmentation task with the required precision to the pixel level is a very time-consuming task and often requires a certain degree of domain knowledge regarding the documents at hand. For this reason, in this paper, we propose an effective few-shot learning framework for document layout segmentation relying on two novel components, namely a dynamic instance generation and a segmentation refinement module. This approach is able of achieving performances comparable to the current state of the art on the popular Diva-HisDB dataset, while relying on just a fraction of the available data.
Quantum Inverse Algorithm via Adaptive Variational Quantum Linear Solver: Applications to General Eigenstates

We propose a quantum inverse algorithm (QInverse) to directly determine general eigenstates by repeatedly applying the inverse power of a shifted Hamiltonian to an arbitrary initial state. To properly deal with the strongly entangled inverse power states and the resultant excited states, we solved the underlying linear equation, both variationally and adaptively, to obtain a faithful inverse power state with a shallow quantum circuit. QInverse is singularity-free and successfully obtains the target excited states with an energy closest to the shift ω, which is difficult to reach using variational methods. We also propose a subspace expansion approach to accelerate convergence and show that it is helpful to determine the two nearest eigenvalues when they are equally close to ω. These approaches were compared with the folded-spectrum method, which aims to generate excited states through variational optimization. It is shown that, whereas the folded-spectrum approach often fails to predict the target state by falling into a local minimum owing to its variational features, the success rate and accuracy of our algorithms are systematically improvable.
Long-distance continuous-variable quantum key distribution with feasible physical noiseless linear amplifiers

Noiseless linear amplifiers (NLAs) provide a powerful tool to achieve long-distance continuous-variable (CV) quantum key distribution (QKD) in the presence of realistic setups with nonunit reconciliation efficiency. We address a NLA-assisted CV QKD protocol implemented via realistic physical NLAs, namely, quantum scissors and single-photon catalysis, and compare their performance with respect to the ideal NLA ${g}^{\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{n}}$. We investigate also the robustness of two schemes against inefficient conditional detection and discuss the two alternative scenarios in which the gain associated with the NLA is either fixed or optimized.
Compact Lattice Gadget and Its Applications to Hash-and-Sign Signatures

Lattice gadgets and the associated algorithms are the essential building blocks of lattice-based cryptography. In the past decade, they have been applied to build versatile and powerful cryptosystems. However, the practical optimizations and designs of gadget-based schemes generally lag their theoretical constructions. For example, the gadget-based signatures have elegant design and capability of extending to more advanced primitives, but they are far less efficient than other lattice-based signatures. This work aims to improve the practicality of gadget-based cryptosystems, with a focus on hash-and-sign signatures. To this end, we develop a compact gadget framework in which the used gadget is a square matrix instead of the short and fat one used in previous constructions. To work with this compact gadget, we devise a specialized gadget sampler, called semi-random sampler, to compute the approximate preimage. It first deterministically computes the error and then randomly samples the preimage. We show that for uniformly random targets, the preimage and error distributions are simulatable without knowing the trapdoor. This ensures the security of the signature applications. Compared to the Gaussian-distributed errors in previous algorithms, the deterministic errors have a smaller size, which lead to a substantial gain in security and enables a practically working instantiation. As the applications, we present two practically efficient gadget-based signature schemes based on NTRU and Ring-LWE respectively. The NTRU-based scheme offers comparable efficiency to Falcon and Mitaka and a simple implementation without the need of generating the NTRU trapdoor. The LWE-based scheme also achieves a desirable overall performance. It not only greatly outperforms the state-of-the-art LWE-based hash-and-sign signatures, but also has an even smaller size than the LWE-based Fiat-Shamir signature scheme Dilithium. These results fill the long-term gap in practical gadget-based signatures.
Monitoring variations of refractive index via Hilbert–Schmidt speed and applying this phenomenon to improve quantum metrology

Abstract In this paper, we investigate the role of the nonlinear response of a material to improve quantum metrology. We show that the collective optical behavior of an atomic ensemble can be applied to enhance frequency estimation through one of the atoms. In fact, the collective optical behavior of the atomic ensemble by analyzing the quantum information extracted from one of its elements. Moreover, we introduce Hilbert–Schmidt speed (HSS), an easily computable theoretical tool, to monitor the variations of linear as well as nonlinear refractive indices and evaluate the strength of the nonlinear response of optical materials. Furthermore, we illustrate that quantum Fisher information and HSS can efficiently detect negative permittivity and refractive index, which is of great importance from a practical point of view.
Single image relighting based on illumination field reconstruction

Relighting a single low-light image is a crucial and challenging task. Previous works primarily focused on brightness enhancement but neglected the differences in light and shadow variations, which leads to unsatisfactory results. Herein, an illumination field reconstruction (IFR) algorithm is proposed to address this issue by leveraging physical mechanism guidance, physical-based supervision, and data-based modeling. Firstly, we derived the Illumination field modulation equation as a physical prior to guide the network design. Next, we constructed a physical-based dataset consisting of image sequences with diverse illumination levels as supervision. Finally, we proposed the IFR neural network (IFRNet) to model the relighting progress and reconstruct photorealistic images. Extensive experiments demonstrate the effectiveness of our method on both simulated and real-world datasets, showing its generalization ability in real-world scenarios, even training solely from simulation.
Metrics and properties of optimal gauges in multimode cavity QED

Multimode cavity quantum electrodynamics describes, for example, the coupling between an atom and a multimode electromagnetic resonator. The gauge choice is important for practical calculations in truncated Hilbert spaces, because the exact gauge invariance is recovered only in the whole space. An optimal gauge can be defined as the one predicting the most accurate observables for the same number of atomic levels and modes. Different metrics quantifying the gauge performance can be introduced depending on the observable of interest. In this paper we demonstrate that the optimal choice is generally mode dependent, i.e., a different gauge is needed for each cavity mode. While the choice of gauge becomes more important for increasing light-matter interaction, we also show that the optimal gauge does not correspond to the situation where the entanglement between light and matter is the smallest.
Relational Context Learning for Human-Object Interaction Detection

Recent state-of-the-art methods for HOI detection typically build on transformer architectures with two decoder branches, one for human-object pair detection and the other for interaction classification. Such disentangled transformers, however, may suffer from insufficient context exchange between the branches and lead to a lack of context information for relational reasoning, which is critical in discovering HOI instances. In this work, we propose the multiplex relation network (MUREN) that performs rich context exchange between three decoder branches using unary, pair-wise, and ternary relations of human, object, and interaction tokens. The proposed method learns comprehensive relational contexts for discovering HOI instances, achieving state-of-the-art performance on two standard benchmarks for HOI detection, HICO-DET and V-COCO.
Realistic Saliency Guided Image Enhancement

Common editing operations performed by professional photographers include the cleanup operations: de-emphasizing distracting elements and enhancing subjects. These edits are challenging, requiring a delicate balance between manipulating the viewer's attention while maintaining photo realism. While recent approaches can boast successful examples of attention attenuation or amplification, most of them also suffer from frequent unrealistic edits. We propose a realism loss for saliency-guided image enhancement to maintain high realism across varying image types, while attenuating distractors and amplifying objects of interest. Evaluations with professional photographers confirm that we achieve the dual objective of realism and effectiveness, and outperform the recent approaches on their own datasets, while requiring a smaller memory footprint and runtime. We thus offer a viable solution for automating image enhancement and photo cleanup operations.