Reviewerly/exHarmony huggingface dataset:
Development of a model of information process management in the information and educational environment of preschool education organizations

One of the important elements of the information infrastructure of educational institutions is the information and educational environment. The information and educational environment of preschool education organizations has its own characteristics, which must be taken into account when building a model for managing information processes. The object of study in this work is the information and educational environment for preschool education organizations. The problem to be solved is the need to develop a model for managing data and information processes, which will allow determining the learning outcomes of preschoolers and adjusting individual work with them. The introduction of the developed model allowed to reduce the time spent on adjusting individual work with students by 30 %. These results are explained by the optimization of information processes, as well as improved monitoring of the formation of skills of preschoolers and a reduction in the time for its implementation. When monitoring for each child, 211 indicators are examined. There are 633 indicators per year for three monitoring, in aggregate, per one child. By default, the data is entered into Microsoft Excel and processed manually. However, a large number of entries slows down the processing of Microsoft Excel data and increases the chance of errors. The use of this model will make it possible to carry out calculations automatically, save data and generate reports for each child or group of children. The developed model can be used in information and educational environments for preschool education organizations in order to improve the efficiency of monitoring and managing educational processes
Illumination-Insensitive Binary Descriptor for Visual Measurement Based on Local Inter-Patch Invariance

Binary feature descriptors have been widely used in various visual measurement tasks, particularly those with limited computing resources and storage capacities. Existing binary descriptors may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations. It can be observed that when image illumination changes dramatically, the relative relationship among local patches mostly remains intact. Based on the observation, consequently, this study presents an illumination-insensitive binary (IIB) descriptor by leveraging the local inter-patch invariance exhibited in multiple spatial granularities to deal with unfavorable illumination variations. By taking advantage of integral images for local patch feature computation, a highly efficient IIB descriptor is achieved. It can encode scalable features in multiple spatial granularities, thus facilitating a computationally efficient hierarchical matching from coarse to fine. Moreover, the IIB descriptor can also apply to other types of image data, such as depth maps and semantic segmentation results, when available in some applications. Numerical experiments on both natural and synthetic datasets reveal that the proposed IIB descriptor outperforms state-of-the-art binary descriptors and some testing float descriptors. The proposed IIB descriptor has also been successfully employed in a demo system for long-term visual localization. The code of the IIB descriptor will be publicly available.
Cyclic Learning: Bridging Image-Level Labels and Nuclei Instance Segmentation

Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis.Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density.To alleviate the annotation burden, we seek to solve the problem through image-level weakly supervised learning, which is underexplored for nuclei instance segmentation.Compared with most existing methods using other weak annotations (scribble, point, etc.) for nuclei instance segmentation, our method is more labor-saving.The obstacle to using image-level annotations in nuclei instance segmentation is the lack of adequate location information, leading to severe nuclei omission or overlaps.In this paper, we propose a novel image-level weakly supervised method, called cyclic learning, to solve this problem.Cyclic learning comprises a front-end classification task and a backend semi-supervised instance segmentation task to benefit from multi-task learning (MTL).We utilize a deep learning classifier with interpretability as the front-end to convert image-level labels to sets of high-confidence pseudo masks and establish a semi-supervised architecture as the back-end to conduct nuclei instance segmentation under the supervision of these pseudo masks.Most importantly, cyclic learning is designed to circularly share knowledge
Hey Robot, Tell It to Me Straight: How Different Service Strategies Affect Human and Robot Service Outcomes

With robots already entering simple service tasks in shops, it is important to understand how robots should perform customer service to increase customer satisfaction. We investigate two methods of customer service we theorize are better suited for robots than human shopkeepers: straight communication and data-driven communication. Along with an additional, more traditional customer service style, we compare these methods of customer service performed by a robot, to a human performing the same service styles in 3 online studies with over 1300 people. We find that while traditional customer service styles are best suited for human shopkeepers, robot shopkeepers using straight or data driven customer service styles increase customer satisfaction, make customers feel more informed, and feel more natural than when a human uses them. Our work highlights the need for investigating robot-specific best practices for customer service, but also for social interaction at large, as simply duplicating typical human–human interaction may not produce the best results for a robot.
Robust cortical encoding of 3D tongue shape during feeding in macaques

Dexterous tongue deformation underlies eating, drinking, and speaking. The orofacial sensorimotor cortex has been implicated in the control of coordinated tongue kinematics, but little is known about how the brain encodes-and ultimately drives-the tongue's 3D, soft-body deformation. Here we combine a biplanar x-ray video technology, multi-electrode cortical recordings, and machine-learning-based decoding to explore the cortical representation of lingual deformation. We trained long short-term memory (LSTM) neural networks to decode various aspects of intraoral tongue deformation from cortical activity during feeding in male Rhesus monkeys. We show that both lingual movements and complex lingual shapes across a range of feeding behaviors could be decoded with high accuracy, and that the distribution of deformation-related information across cortical regions was consistent with previous studies of the arm and hand.
Exploring the landscape of e-learning for lifelong education of public servants: trends, challenges, and implications

This article provides a comprehensive overview of the various components comprising the lifelong education system for public servants. With the advancement of e-government being a pivotal aspect of public administration, the significance of e-learning as a crucial tool for equipping public servants to fulfill this objective is examined. The study analyzes both domestic and international experiences of e-learning for public servants, encompassing distance education, blended learning, and its integration within traditional classroom settings. The article sheds light on the potential threats and benefits of Information and Communication Technology (ICT) training, drawing insights from a survey conducted among civil servants who received ICT training in Ukrainian higher educational institutions. By drawing upon scientific and empirical evidence, the trends in e-learning development are identified, with certain countries serving as exemplars in addressing the current challenges in this field. This article serves as a valuable resource for understanding the current landscape of e-learning in the context of lifelong education for public servants, highlighting emerging trends, inherent challenges, and the wider implications for effective governance.
Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation

Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge from an informative source domain to the target domain, which inevitably proposes stern challenges to data privacy and transferability during the transfer process. A small amount of recent CDR works have investigated privacy protection, while they still suffer from satisfying practical requirements (e.g., limited privacy-preserving ability) and preventing the potential risk of negative transfer. To address the above challenging problems, we propose a novel and unified privacy-preserving federated framework for dual-target CDR, namely P2FCDR. We design P2FCDR as peer-to-peer federated network architecture to ensure the local data storage and privacy protection of business partners. Specifically, for the special knowledge transfer process in CDR under federated settings, we initialize an optimizable orthogonal mapping matrix to learn the embedding transformation across domains and adopt the local differential privacy technique on the transformed embedding before exchanging across domains, which provides more reliable privacy protection. Furthermore, we exploit the similarity between in-domain and cross-domain embedding, and develop a gated selecting vector to refine the information fusion for more accurate dual transfer. Extensive experiments on three real-world datasets demonstrate that P2FCDR significantly outperforms the state-of-the-art methods and effectively protects data privacy.
Full-text search added to the Rogue Scholar science blog archive

In January I started the Rogue Scholar blog archive with the slogan "science blogging on steroids", promising to enhance science blogs in important ways. Earlier this month I began DOI registrations for blog posts, and I am well on track to complete this for the included 35 blogs with more than 1,000 blog posts in the next few weeks.
The eradication of hate speech on social media: a systematic review

Purpose The purpose of this paper is to present a quantitative and qualitative synthesis of the diverse academic proposals and initiatives for preventing and eliminating hate speech on the internet. Design/methodology/approach The foundation for this study is a systematic review of papers devoted to the analysis of hate speech. It has been conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol and applied to an initial corpus of 436 academic texts. Having implemented the suitability, screening and inclusion criteria, this corpus was refined to a sample of 74 articles. Findings The main subject categories studied in this corpus of academic research are legal issues and social media. In the majority of the articles, the use of hate speech via social media is associated with five typologies: religion, cyber racism, political slurs, misogyny and attacks on the LGTBI community. The absence of ethical reflection is one of the major shortcomings of IT-focused research and analysis devoted to online hate speech. Practical implications To date various systematic reviews have been presented, and they focus on detecting or describing hate speech. These have used either the search appraisal synthesis analysis framework or the Cochrane network. The PRISMA protocol was applied for this study, and both Scopus and texts in German were included. To date no major, rigorous systematic review has been undertaken of proposals to combat hate speech. Originality/value The link between hate speech and poverty has not been studied in depth within the academic sphere. Tolerance and ethical compassion are not granted the attention they merit when it comes to analysing the phenomenon of hate speech.
Spoofing Cross-Entropy Measure in Boson Sampling

Cross-entropy (XE) measure is a widely used benchmark to demonstrate quantum computational advantage from sampling problems, such as random circuit sampling using superconducting qubits and boson sampling (BS). We present a heuristic classical algorithm that attains a better XE than the current BS experiments in a verifiable regime and is likely to attain a better XE score than the near-future BS experiments in a reasonable running time. The key idea behind the algorithm is that there exist distributions that correlate with the ideal BS probability distribution and that can be efficiently computed. The correlation and the computability of the distribution enable us to postselect heavy outcomes of the ideal probability distribution without computing the ideal probability, which essentially leads to a large XE. Our method scores a better XE than the recent Gaussian BS experiments when implemented at intermediate, verifiable system sizes. Much like current state-of-the-art experiments, we cannot verify that our spoofer works for quantum-advantage-size systems. However, we demonstrate that our approach works for much larger system sizes in fermion sampling, where we can efficiently compute output probabilities. Finally, we provide analytic evidence that the classical algorithm is likely to spoof noisy BS efficiently.
Optical image conversion and encryption based on structured light illumination and a diffractive neural network

In this paper, an optical image encryption method is proposed based on structured light illumination and a diffractive neural network (DNN), which can realize conversion between different images. With the use of the structured phase mask (SPM) in the iterative phase retrieval algorithm, a plaintext image is encoded into a DNN composed of multiple phase-only masks (POMs) and ciphertext. It is worth noting that ciphertext is a visible image such that the conversion of one image to another is achieved, leading to high concealment of the proposed optical image encryption method. In addition, the wavelength of the illuminating light, all Fresnel diffraction distances, the optical parameters of the adopted SPM such as focal length and topological charge number, as well as all POMs in the DNN are all considered as security keys in the decryption process, contributing to a large key space and high level of security. Numerical simulations are performed to demonstrate the feasibility of the proposed method, and simulation results show that it exhibits high feasibility and safety as well as strong robustness.
Complexity Phase Transitions Generated by Entanglement

Entanglement is one of the physical properties of quantum systems responsible for the computational hardness of simulating quantum systems. But while the runtime of specific algorithms, notably tensor network algorithms, explicitly depends on the amount of entanglement in the system, it is unknown whether this connection runs deeper and entanglement can also cause inherent, algorithm-independent complexity. In this Letter, we quantitatively connect the entanglement present in certain quantum systems to the computational complexity of simulating those systems. Moreover, we completely characterize the entanglement and complexity as a function of a system parameter. Specifically, we consider the task of simulating single-qubit measurements of k-regular graph states on n qubits. We show that, as the regularity parameter is increased from 1 to n-1, there is a sharp transition from an easy regime with low entanglement to a hard regime with high entanglement at k=3, and a transition back to easy and low entanglement at k=n-3. As a key technical result, we prove a duality for the simulation complexity of regular graph states between low and high regularity.
Physical Zero-Knowledge Proof for Ball Sort Puzzle

Ball sort puzzle is a popular logic puzzle consisting of several bins containing balls of multiple colors. Each bin works like a stack; a ball has to follow the last-in first-out order. The player has to sort the balls by color such that each bin contains only balls of a single color. In this paper, we propose a physical zero-knowledge proof protocol for the ball sort puzzle using a deck of playing cards, which enables a prover to physically show that he/she knows a solution with t moves of the ball sort puzzle without revealing it. Our protocol is the first zero-knowledge proof protocol for an interactive puzzle involving moving objects.
Underwater Image Quality Improvement via Color, Detail, and Contrast Restoration

Due to the complex imaging mechanism, underwater images often suffer from multiple degradation issues, such as color cast, blurry detail, and low contrast, which affect the extraction of valuable information. To deal with these degradation issues, a simple yet effective underwater image quality improvement method based on color, datail and contrast restoration (CDCR) is developed, which consists of three key modules: a well-preserved finding-driven color balance module (CBM), a linear saturation transformation-based discriminant function-based detail restoration module (DRM), and a transmission minimization-oriented contrast restoration module (CRM). First, the CBM explores a well-preserved channel finding and employs a channel compensation strategy to balance the color differences among three color channels. Second, the DRM uses a piecewise underwater image saturation estimation strategy, which takes the various spectral properties of water into account and designs an additional linear saturation transformation-based discriminant function to prevent the transmission from being under-estimated. At last, the CRM estimates a global backscatter light based on transmission minimization and further improves the contrast by locally removing the backscatter light of the base layer. Our restored image is appealing in its natural color, fine details, and high contrast. Extensive experiments on three underwater image enhancement datasets show that our CDCR achieves better results than state-of-the-art methods, i.e., compared with the second-best method, the average PCQI and UIQM values of our method increase by 5.7% and 0.2%, and the average Blur and DFAD values of our method decrease by 8.0% and 5.3%. Meanwhile, experiments further suggest that the rate of new visible edges and the quality of contrast restoration of our CDCR at least increase by 7.7% and 51.2% in most tested sandstorm and foggy images, respectively, which demonstrates that our method has a good generalization capability for sandstorm and foggy image restoration.
SoK: A Taxonomy for Critical Analysis of Consensus Mechanisms in Consortium Blockchain

Consensus algorithms are central to blockchain technology and an emerging research area.In this paper, we begin with an overview of the different types and architectures of blockchain networks.Then, with a focus on consortium blockchains, we survey, classify, and assess their principal consensus mechanisms.Furthermore, as consensus mechanisms determine network reliability, enhance performance efficiency, and ensure system security, we conduct a critical analysis of the strengths and weaknesses of consensus algorithms using a taxonomy of three different criteria: reliability, performance, and security.We conclude with insights into current and future research challenges and opportunities in this domain.
GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models

Recent work has shown that Pre-trained Language Models (PLMs) store the relational knowledge learned from data and utilize it for performing downstream tasks.However, commonsense knowledge across different regions may vary.For instance, the color of bridal dress is white in American weddings whereas it is red in Chinese weddings.In this paper, we introduce a benchmark dataset, Geo-diverse Commonsense Multilingual Language Models Analysis (GEOMLAMA), for probing the diversity of the relational knowledge in multilingual PLMs.GEOMLAMA contains 3,125 prompts in English, Chinese, Hindi, Persian, and Swahili, with a wide coverage of concepts shared by people from American, Chinese, Indian, Iranian and Kenyan cultures.We benchmark 11 standard multilingual PLMs on GE-OMLAMA.Interestingly, we find that 1) larger multilingual PLMs variants do not necessarily store geo-diverse concepts better than its smaller variant; 2) multilingual PLMs are not intrinsically biased towards knowledge from the Western countries (the United States); 3) the native language of a country may not be the best language to probe its knowledge and 4) a language may better probe knowledge about a nonnative country than its native country.Code and data are released at https://github.com/WadeYin9712/GeoMLAMA.
Immersive virtual reality in orthopaedics—a narrative review

Abstract Purpose This narrative review explores the applications and benefits of immersive virtual reality (VR) in orthopaedics, with a focus on surgical training, patient functional recovery, and pain management. Methods The review examines existing literature and research studies on immersive VR in orthopaedics, analyzing both experimental and clinical studies. Results Immersive VR provides a realistic simulation environment for orthopaedic surgery training, enhancing surgical skills, reducing errors, and improving overall performance. In post-surgical recovery and rehabilitation, immersive VR environments can facilitate motor learning and functional recovery through virtual embodiment, motor imagery during action observation, and virtual training. Additionally VR-based functional recovery programs can improve patient adherence and outcomes. Moreover, VR has the potential to revolutionize pain management, offering a non-invasive, drug-free alternative. Virtual reality analgesia acts by a variety of means including engagement and diverting patients’ attention, anxiety reduction, and specific virtual-body transformations. Conclusion Immersive virtual reality holds significant promise in orthopaedics, demonstrating potential for improved surgical training, patient functional recovery, and pain management but further research is needed to fully exploit the benefits of VR technology in these areas.
Multi-Date Earth Observation NeRF: The Detail Is in the Shadows

We introduce Earth Observation NeRF (EO-NeRF), a new method for digital surface modeling and novel view synthesis from collections of multi-date remote sensing images. In contrast to previous variants of NeRF proposed in the literature for satellite images, EO-NeRF outperforms the altitude accuracy of advanced pipelines for 3D reconstruction from multiple satellite images, including classic and learned stereovision methods. This is largely due to a rendering of building shadows that is strictly consistent with the scene geometry and independent from other transient phenomena. In addition to that, a number of strategies are also proposed with the aim to exploit raw satellite images. We add model parameters to circumvent usual pre-processing steps, such as the relative radiometric normalization of the input images and the bundle adjustment for refining the camera models. We evaluate our method on different areas of interest using sets of 10-20 pre-processed and raw pansharpened WorldView-3 images.
Releasing commonmeta-py v0.8

Today I am happy to announce the release of commonmeta-py v0.8, the next major release of the Python scholarly metadata conversion library.