Reviewerly/exHarmony huggingface dataset:
Payable Outsourced Decryption for Functional Encryption Using BlockChain

In many new implementations that needs data storage and exchange (such as cloud storage services), the notion of Functional Encryption $$({\varvec{F}}{\varvec{E}})$$ was initiated to label the defects of Public-key Encryption $$({\varvec{P}}{\varvec{K}}{\varvec{E}})$$ . One of the biggest issues with most FE charts is performance, as they are calculated on very expensive pairs of two lines. The general acquired solution to this issue is to load a massive load on a strong third party and let the client to do the simple calculations. However, it is unrealistic to accept that a mediator provides a free service. According to our perception, $${\varvec{F}}{\varvec{E}}$$ plans using External Decryption Scheme $$({\varvec{F}}{\varvec{E}}{\varvec{O}}{\varvec{D}})$$ will not terminate the payment process between the user and the third party only if both are untrusted. In this article, the proposed method is to design a $${\varvec{F}}{\varvec{E}}$$ with Payable Outsourced Decryption $$({\varvec{F}}{\varvec{E}}{\varvec{P}}{\varvec{O}}{\varvec{D}})$$ in the treaty. The payment according to FEPOD method is made by a blockchain based cryptocurrency. This allows users to pay a mediator when the allocated decoding is successfully completed. After defining the paradoxical model of the $$\mathbf{F}\mathbf{E}\mathbf{P}\mathbf{O}\mathbf{D}$$ design, the asymmetric structure of the $${\varvec{F}}{\varvec{E}}{\varvec{P}}{\varvec{O}}{\varvec{D}}$$ design is introduced. It also measures the overall performance of the proposal by executing a specific $${\varvec{F}}{\varvec{E}}{\varvec{P}}{\varvec{O}}{\varvec{D}}$$ scheme on the blockchain policy.
Security Enhancement of Content in Fog Environment

Research has discovered that typical security measures only provide a limited level of protection for cloud-based data. However, neither dependability nor security can be guaranteed at this time. This study gives a review of previous research that has been done. In addition, a more secure strategy has been proposed in this study. In the future, research activities may divide data as it is being sent. Because of this, there will be a lower likelihood of any security being breached. A soon-to-be-developed encryption technique could have the ability to prevent data from being lost or stolen. Not only would this give security at the application layer, but it would also provide security at the session level. It is not possible to send all of the data on a single channel. The data would be broken up into two parts: one for transmission through the fog, and another via the cloud. In the light of this, forthcoming work will make use of an integrated fog-based strategy for improving the security of the cloud. Because existing security mechanisms suffer from some drawbacks, there is a pressing need for such a system.
Large-Scale Swarm Control in Cluttered Environments

In the evolving era of social robots, managing a swarm of autonomous agents to perform particular tasks has become essential for numerous industries. The task becomes more challenging for large-scale swarms and complex environments, which have not been fully explored yet. Therefore, this research introduces a methodology incorporating multiple coordinated robotic shepherds to effectively guide large-scale agent swarms in obstacle-laden terrains. The proposed framework commences with deploying an unsupervised machine-learning algorithm to categorise the swarm into clusters. Then, a shepherding algorithm with coordinated robotic shepherds drives the sub-swarms towards the goal. Also, a path planner based on an evolutionary algorithm is proposed to help robotic shepherds move in a way that minimises the dispersion of each sub-swarm and avoids potential hazards and obstructions. The proposed approach is tested on different scenarios, with the results showing a success rate of 100% in guiding swarms with sizes up to 3000 agents.
The Role of Scientific and Methodological Support for the Professional Development of Teachers in the Context of Reforming Basic Secondary Education

This article discusses the state of scientific and methodological support of teachers, which is aimed at implementing the tasks of reforming basic secondary education on the basis of the Concept of «New Ukrainian School».&#x0D; The presented material is based on the results of the analysis of scientific, methodological and innovative activities of institutions of postgraduate pedagogical education, aimed at improving the system of quality assurance of education and professional growth of the teacher.&#x0D; Taking into account the changes in the content of education, the practical implementation of student-centered learning, the introduction of innovative pedagogical technologies into the educational process, it is indicated how the professional formation of a teacher in the system of postgraduate education is practically ensured. On the basis of the study, the authors determine the organizational and pedagogical conditions for improving the process of professional growth of teachers in the system of postgraduate pedagogical education, as a factor in increasing their competitiveness.&#x0D; The role of the system of postgraduate pedagogical education, as a system of adult education, in improving the process of professional growth of a teacher is disclosed. It is noted that the system of postgraduate pedagogical education, under the influence of socio-economic changes that take place in society, ensures the continuous professional development of teachers and their mastery of competencies that are closely related to the implementation of their professional pedagogical activities and the transition to a new quality of education.
Current State of Implementation of the Concept of Development of Natural Science and Mathematical Education (STEM Education)

The article presents the results of a study analyzing the progress of activities related to the implementation of the Concept of Development of Natural Science and Mathematical Education (STEM education) by 2027.&#x0D; The research aims to analyze the status of implementing measures related to the Concept of Development of Natural Science and Mathematical Education (STEM education).&#x0D; The authors examine key aspects of STEM education implementation, including familiarizing educational professionals with the regulatory framework, issues of quality qualification improvement, educational STEM environments, and methods of organizing STEM learning.&#x0D; It is demonstrated that systematic monitoring of STEM education development and its results serve to justify managerial decisions, develop the education system, and help reveal the essence and significance of innovative approaches to managerial activities that enhance the course and outcomes of the educational process.
Distance Learning: Issues and Ways to Overcome Them

This paper explores the concept of "distance learning" and theoretical and practical approaches to studying the problems that arise for academic and educational professionals, as well as higher education students, when implementing distance learning technologies in the educational process of higher education institutions. Based on the results of our research, the advantages and problems of distance learning that affect the success of education in higher education institutions are justified and summarized, including technical, psycho-pedagogical, organizational, managerial, and others. The combination of this type of learning with traditional methods allows time for obtaining, exchanging, and processing acquired educational information and completing tasks. It also provides opportunities for better communication, socialization, collaborative work between teachers and higher education students, and receiving assistance when needed.&#x0D; Promising directions for improving distance learning have been identified: ensuring reliable internet connectivity for all participants in the educational process, creating accessible basic content for educators with the use of quality scientific and methodological support, utilizing modern information technologies, effective teaching methods, and electronic resources that consider the specifics of the distance format, and enhancing the professional development of academic and educational professionals.
Implementing the Propeller Speed Control of Drone Designed in Solidworks in CoppeliaSim Robotic Simulator Environment with Codes Prepared in Matlab

Real-time applications of autonomous systems are simulated in the computer environment to ensure that they operate error-free or with minimal errors. Coppeliasim, used in this field, is a platform where several sample models, robots, sensors and actuators are used together, a virtual world is created and interacted with it throughout the working period. Having a comprehensive toolbox, autonomous vehicle training and virtual reality, Coppeliasim's compatibility with Solidworks, a very useful design program for drawing, seems to be a great advantage. Due to these features, CoppeliaSim is very important in predicting and solving problems that may arise in many different applications. The propeller movement of the drone, which we designed with the Solidworks 2020 program and transferred to the Coppeliasim platform using the URDF exporter method, was carried out with the Coppeliasim simulator. In our work, Coppeliasim is synchronized with the simulator and MATLAB API codes. While the drone propellers were working on the Coppeliasim platform, angular speed and timing controls were made using the codes we prepared in the MATLAB program. Additionally, this work shows that drones or different autonomous systems can be controlled and designed before real-time operation using the Coppeliasim simulator and the MATLAB program.
Knowledge-Aware Graph Self-Supervised Learning for Recommendation

Collaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and noise in real scenarios. In recent years, researchers have introduced graph self-supervised learning (SSL) techniques into CF to alleviate the sparse supervision problem. The technique first augments the data to obtain contrastive views and then utilizes the mutual information maximization to provide self-supervised signals for the contrastive views. However, the existing approaches based on graph self-supervised signals still face the following challenges: (i) Most of the works fail to effectively mine and exploit the supervised information from the item knowledge graph, resulting in suboptimal performance. (ii) Existing data augmentation methods are unable to fully exploit the potential of contrastive learning, because they primarily focus on the contrastive view of data structure changes and neglect the adjacent relationship among users and items. To address these issues, we propose a novel self-supervised learning approach, namely Knowledge-aware Graph Self-supervised Learning (KGSL). Specifically, we calculate node similarity based on semantic relations between items in the knowledge graph to generate a semantic-based item similarity graph. Then, the self-supervised learning contrast views are generated from both the user–item interaction graph and the item similarity graph, respectively. Maximization of the information from these contrastive views provides additional self-supervised signals to enhance the node representation capacity. Finally, we establish a joint training strategy for the self-supervised learning task and the recommendation task to further optimize the learning process of KGSL. Extensive comparative experiments as well as ablation experiments are conducted on three real-world datasets to verify the effectiveness of KGSL.
Mobile Sensoring Data Verification via a Pairing-Free Certificateless Signature Secure Approach against Novel Public Key Replacement Attacks

To achieve flexible sensing coverage with low deployment costs, mobile users need to contribute their equipment as sensors. Data integrity is one of the most fundamental security requirements and can be verified by digital signature techniques. In the mobile crowdsensing (MCS) environment, most sensors, such as smartphones, are resource-limited. Therefore, many traditional cryptographic algorithms that require complex computations cannot be efficiently implemented on these sensors. In this paper, we study the security of certificateless signatures, in particular, some constructions without pairing. We notice that there is no secure pairing-free certificateless signature scheme against the super adversary. We also find a potential attack that has not been fully addressed in previous studies. To handle these two issues, we propose a concrete secure construction that can withstand this attack. Our scheme does not rely on pairing operations and can be applied in scenarios where the devices’ resources are limited.
Rewarded Meta-Pruning: Meta Learning with Rewards for Channel Pruning

Convolutional neural networks (CNNs) have gained recognition for their remarkable performance across various tasks. However, the sheer number of parameters and the computational demands pose challenges, particularly on edge devices with limited processing power. In response to these challenges, this paper presents a novel approach aimed at enhancing the efficiency of deep learning models. Our method introduces the concept of accuracy and efficiency coefficients, offering a fine-grained control mechanism to balance the trade-off between network accuracy and computational efficiency. At our core is the Rewarded Meta-Pruning algorithm, guiding neural network training to generate pruned model weight configurations. The selection of this pruned model is based on approximations of the final model’s parameters, and it is precisely controlled through a reward function. This reward function empowers us to tailor the optimization process, leading to more effective fine-tuning and improved model performance. Extensive experiments and evaluations underscore the superiority of our proposed method when compared to state-of-the-art techniques. We conducted rigorous pruning experiments on well-established architectures such as ResNet-50, MobileNetV1, and MobileNetV2. The results not only validate the efficacy of our approach but also highlight its potential to significantly advance the field of model compression and deployment on resource-constrained edge devices.
Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models

Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has not been evaluated in an experimentation framework designed specifically for the task. We present an approach for named entity recognition in Spanish medical texts that makes use of pre-trained models from the Spanish biomedical domain. We also use data augmentation techniques to improve the identification of less frequent entities in the dataset. The domain-specific models have improved the recognition of name entities in the domain, beating all the systems that were evaluated in the eHealth-KD challenge 2021. Language models from the biomedical domain seem to be more effective in characterizing the specific terminology involved in this task of named entity recognition, where most entities correspond to the "concept" type involving a great number of medical concepts. Regarding data augmentation, only back translation has slightly improved the results. Clearly, the most frequent types of entities in the dataset are better identified. Although the domain-specific language models have outperformed most of the other models, the multilingual generalist model mBERT obtained competitive results.
Event-Centric Temporal Knowledge Graph Construction: A Survey

Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.
Cross-Camera Tracking Model and Method Based on Multi-Feature Fusion

Multi-camera video surveillance has been widely applied in crowd statistics and analysis in smart city scenarios. Most existing studies rely on appearance or motion features for cross-camera trajectory tracking, due to the changing asymmetric perspectives of multiple cameras and occlusions in crowded scenes, resulting in low accuracy and poor tracking performance. This paper proposes a tracking method that fuses appearance and motion features. An implicit social model is used to obtain motion features containing spatio-temporal information and social relations for trajectory prediction. The TransReID model is used to obtain appearance features for re-identification. Fused features are derived by integrating appearance features, spatio-temporal information and social relations. Based on the fused features, multi-round clustering is adopted to associate cross-camera objects. Exclusively employing robust pedestrian reidentification and trajectory prediction models, coupled with the real-time detector YOLOX, without any reliance on supplementary information, an IDF1 score of 70.64% is attained on typical datasets derived from AiCity2023.
The concept of sufficiency in conditional frequentist inference

We consider inference about the parameter that determines the distribution of the data. In frequentist inference a very important and useful idea is that data reduction to a sufficient statistic does not lose any information about this parameter. We recall two justifications for this idea in frequentist inference. We then examine the extent to which these justifications carry over to conditional frequentist inference inference, which consists of carrying out frequentist inference conditional on an ancillary statistic. This examination shows that, in the context of conditional frequentist inference, first reducing data to a sufficient statistic is not always justified, so we should first condition on an ancillary statistic. Finally, we describe two types of practically important statistical models that illustrate this finding.
Nonparametric conditional mean testing via an extreme‐type statistic in high dimension

Abstract We propose a new test to investigate the conditional mean dependence between a response variable and the corresponding covariates in the high‐dimensional regimes. The test statistic is an extreme‐type one built on the nonparametric method. The limiting null distribution of the proposed extreme type statistic under a mild mixing condition is established. Moreover, to make the test more powerful in general structures we propose a more general test statistic and develop its asymptotic properties. The power analysis of both methods is also considered. In real data analysis, we also propose a new way to conduct the feature screening based on our results. To evaluate the performance of our estimators and other methods, extensive simulations are conducted.
Thermal quantum coherence of two–qutrit Heisenberg XXZ model with Herring-Flicker coupling and Dzyaloshinskii-Moriya interaction under magnetic field

Abstract In this study, we use the concept of l 1 -norm coherence to characterize the entanglement of a two–qutrit Heisenberg XXZ model for subject to a uniform magnetic field and z–axis Dzyaloshinskii–Moriya interaction with Herring-Flicker coupling. We show that the temperature, magnetic field, DM interaction, and distance of Herring-Flicker coupling can all control the entanglement. However, the state system becomes less entangled at high temperatures or strong magnetic fields and vice versa. Our findings suggest that entanglement rises when the z–axis DM interaction increases. Additionally, we show that plateau behavior in the entanglement between spins (1, 1) occurs in the XXZ Heisenberg spin system and is influenced by the magnetic field, demonstrating that thermal agitation can weaken entanglement plateaus. Moreover, by setting the strengths coupling of the spin, we quickly recover the isotropic XY and XXX Heisenberg models. Finally, Herring-Flicker coupling affects the degree of entanglement. When Herring-Flicker coupling and temperature are at small values, the degree of entanglement is at its highest. Still, when Herring-Flicker coupling is at substantial values, the degree of entanglement tends to stabilize.