Reviewerly/exHarmony huggingface dataset:
Neural Solution to Dynamic Overdetermined System With Applications to Data Fitting and Parameters Estimation

In recent years, dynamic overdetermined systems have sprung up and been broadly employed for handling different problems in real time. This article makes improvements in this direction by proposing, investigating, and analyzing an integral neural solution (INS) to solve the dynamic overdetermined system. Notably, an error function is constructed in the first place. Then, aided with a generated neural dynamic framework, an INS model is devised, which exploits not only saturated or even noncontinuous projection functions but also possesses noise-suppression ability with integral enhancement information. Theoretical analyses and computer simulations manifest that the proposed INS model is able to acquire the least-squares (LS) solution with superior convergence property, contrasted with the existing methods, e.g., zeroing neural network (ZNN). On top of that, applications to data fitting as well as parameters estimation ulteriorly validate the feasibility and effectiveness of the proposed INS model for handling the dynamic overdetermined system.
Privacy-Preserving and Forward Public Key Encryption With Field-Free Multi-Keyword Search for Cloud Encrypted Data

With the excessive growth of data and the rapid development of cloud technology, cloud adoption is expanding rapidly nowadays. To achieve the purpose of privacy protection, the cloud data may be transmitted, stored and retrieved in enciphered form. Public key searchable encryption (PKSE) provides a feasible solution for efficient retrieval over enciphered data without decryption. However, traditional PKSE suffers from some problems, such as keyword guessing (KG) attack and unauthorized ciphertext retrieval. In this paper, we present a practical PKSE scheme named forward public key authenticated encryption with field-free conjunctive keyword search (FW-PAE-FCKS). The scheme enjoys several good properties (e.g., flexible multi-keyword search with no keyword fields, forward ciphertext retrieval) and can effectively withstand the KG attack and the unauthorized ciphertext retrieval. Moreover, the executive overhead of the scheme is very friendly to the user terminals with limited resources as it totally avoids the operations with high computation cost (such as hash-to-point, bilinear pairing) on the user side. Based on the infeasibility assumption of the hash Diffie-Hellman problem, we formally prove its security without using the random oracle. Comparison analysis and experimental results show that it outperforms the existing related schemes.
An Anchor-Free and Angle-Free Detector for Oriented Object Detection Using Bounding Box Projection

The detection and recognition of oriented objects in remote sensing images is a challenging task due to their complex backgrounds, various sizes, diverse aspect ratios, and especially arbitrary orientations. Many oriented object detection algorithms need to obtain accurate angles or adopt anchors to predict the oriented bounding boxes. When directly predicting the angles of objects’ oriented bounding boxes, the loss of angle is discontinuous during training, which makes it difficult to obtain accurate boundary of oriented objects. And the anchors also aggravate the problems of class imbalance and computational cost. To address the above problems, this paper proposes an anchor-free and angle-free detector called AF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Det. AF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Det adopts the information of the bounding box projection instead of the angle to represent and reconstruct the object’s oriented bounding boxes, which could avoid the problem of boundary discontinuity. To predict the information of the bounding box projection, an anchor-free architecture is built to predict objects as points based on a simple but strong U-shaped architecture. And the deformable convolution and the bottom-up feature fusion method are integrated effectively to enhance AF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Det’ s capacity for objects’ shapes, orientations, and scales. The extensive experiments are conducted on multiple datasets, i.e., HRSC2016, FGSD2021, DOTA, and RSDD-SAR to validate the effectiveness of our method. The experimental results demonstrate that the proposed AF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Det outperforms other anchor-free algorithms and obtains competitive results on oriented object detection.
A True Random Number Generator Based on Semiconductor-Vacancies Junction Entropy Source and Square Transform Method

In this work, we demonstrate a high-throughput true random number generator (TRNG), which passes the National Institute of Standards and Technology (NIST) 800–22 and 800-90B randomness tests. A novel TiN/HfO2/n-Si structured semiconductor-vacancies (S-VOs) junction entropy source is developed with an area of 4 F2. The TRNGs are implemented by the proposed square transform method in the voltage domain using an analog-to-digital converter (ADC). According to the measured statistical characteristics, the TRNGs exhibit excellent robustness and a high throughput of 1.6 Mbps by experiment and 20 Gbps by theory.
SmallTrack: Wavelet Pooling and Graph Enhanced Classification for UAV Small Object Tracking

Aerial object tracking has recently shown great potential in the field of remote sensing. However, small objects with limited feature information pose a huge challenge to aerial trackers. Despite significant improvements, most trackers still struggle to capture enough discriminative features and to overcome background disturbances. In this work, we propose an efficient aerial tracker (SmallTrack) based on the Siamese network to improve the discrimination of small objects. It consists of two effective modules, namely Wavelet Pooling Layer (WPL) and Graph Enhanced Module (GEM). First, WPL decomposes the input into four subbands via wavelet domain learning, and fully utilizes the high- and low-frequency information in the subbands to preserve the discriminative features of small objects. Second, GEM embeds the pixels on the classification responses as nodes in graph learning through graph neural networks, which naturally mines the similarity between pixels. Based on the pixel-level modulation constructed from graph theory, GEM enhances the understanding of small objects and highlights them in the classification responses. The proposed tracker achieves leading performance on five aerial benchmarks, while maintaining a high running speed of 72.5 frames/s. Besides, real-world tests on an aerial platform have proven the effectiveness of SmallTrack. The code and models are available at https://github.com/xyl-507/SmallTrack.
Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents

The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according to the importance of argument roles. To address these issues, we propose a model based on graph neural networks which realizes the semantic interaction among documents, sentences, and entities. Additionally, our model adopts a dynamic argument detection strategy, extracting arguments depending on their number in correspondence with each role. The experimental results confirm the outperformance of our model, which surpasses previous methods by 7% and 1.9% in terms of an F1 score.
Exploring Zero-Shot Semantic Segmentation with No Supervision Leakage

Zero-shot semantic segmentation (ZS3), the process of classifying unseen classes without explicit training samples, poses a significant challenge. Despite notable progress made by pre-trained vision-language models, they have a problem of “supervision leakage” in the unseen classes due to their large-scale pre-trained data. For example, CLIP is trained on 400M image–text pairs that contain large label space categories. So, it is not convincing for real “zero-shot” learning in machine learning. This paper introduces SwinZS3, an innovative framework that explores the “no-supervision-leakage” zero-shot semantic segmentation with an image encoder that is not pre-trained on the seen classes. SwinZS3 integrates the strengths of both visual and semantic embeddings within a unified joint embedding space. This approach unifies a transformer-based image encoder with a language encoder. A distinguishing feature of SwinZS3 is the implementation of four specialized loss functions in the training progress: cross-entropy loss, semantic-consistency loss, regression loss, and pixel-text score loss. These functions guide the optimization process based on dense semantic prototypes derived from the language encoder, making the encoder adept at recognizing unseen classes during inference without retraining. We evaluated SwinZS3 with standard ZS3 benchmarks, including PASCAL VOC and PASCAL Context. The outcomes affirm the effectiveness of our method, marking a new milestone in “no-supervison-leakage” ZS3 task performance.
Utilizing Remote Sensing and Satellite-Based Bouguer Gravity data to Predict Potential Sites of Hydrothermal Minerals and Gold Deposits in Central Saudi Arabia

This article aims to aid in exploring and forecasting hydrothermal minerals and gold deposits in Central Saudi Arabia (SA), with a focus on structural contexts. Remote sensing (RS) and satellite-based Bouguer gravity (SBG) data were integrated in order to create a mineral prediction map for the researched location. Data from the Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) were transformed and enhanced using a variety of approaches. The delineation of hydrothermal alteration zones (HAZs) and highlighting of structural discontinuities in the OLI data were made possible using band ratios and oriented principal component analysis (PCA). Additionally, the underlying structural features were successfully exposed by processing the SBG using a variety of edge detection techniques, like the analytical signal (AS), total horizontal derivative (THD), tilt angle (TA), horizontal tilt angle (TDX), theta map (TM), horizontal derivative of the tilt derivative (HD_TDR), horizontal gradient of the tilt angle (HGTA), tilt angle of the analytical signal (TAAS), and soft sign function (SF). As a result, more prominent lineaments were found in the NW–SE, NNW–SSE, NE–SW, and NNE–SSW directions than in the N–S and E–W directions. The GIS incorporated surface/subsurface geological structure density maps with zones of hydrothermal alteration. It was found that the lineaments derived from the analysis of the RS and SBG data were more in line with the HAZs, which demonstrated the common connection between alteration zones and deep lineaments. The findings revealed a mineral prediction map with extremely low to extremely high probabilities. Overall, combining RS and SBG data effectively identified probable mineralization sites associated with hydrothermal processes and made it easier to create this study’s final predictive mineralization map.
K.D. Ushinsky as a teacher and mentor of Russian national pedagogy (to the 200th anniversary of his birth)

Considering the analysis of the scientific research, the author turns to revealing and understanding the pedagogical heritage of the outstanding teacher and scientist Konstantin Dmitrievich Ushinsky in the field of scientific knowledge. The paper focuses on the conceptual ideas of K.D. Ushinsky, which present the main provisions of the education and upbringing of the younger generation, the stages of professional training of the future teacher, the popular image of the teacher. The importance of the teaching profession for modern society is emphasized. The author pays special attention to the popularization of the teaching profession, the recognition of the special status of teaching staff, including mentoring, which is of particular importance in the Year of the Teacher and Mentor. The practical significance of the study is in the integration of pedagogical science and practice in the context of the challenges of the 21st century as a construct for the implementation of modern educational practices in the innovative activities of a teacher, tutor, mentor, facilitator by organizing and holding the International Competition for Research and Creative Works of Students In the World of Wise Thoughts K.D. Ushinsky. The presented results of the study update the pedagogical heritage of K.D. Ushinsky in modern pedagogical science and practice and can be used as a tool to attract students to the teaching profession.
Professional and competency-based approach as a basis for the higher and additional education modernization in the Russian Federation

Subject. This article discusses the issues of higher education development in Russia. Objectives. The article aims to substantiate the need to return to a profession-oriented model of higher education and offer recommendations for improving the professional standards of higher education teachers. Methods. For the study, we used the methods of logical and structural analyses. Results. The article finds that the rejection of the profession-oriented model of higher education has significantly weakened Russia's national security. It substantiates the key role of additional professional education in improving a specialist’s knowledge. Conclusions. The problem of the qualified personnel shortage has necessitated the higher and additional education system modernization, which has become the main condition for ensuring the technological sovereignty of the country.
Formal Modelling for Multi-Robot Systems Under Uncertainty

Abstract Purpose of Review To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution. In this paper, we review modelling formalisms for multi-robot systems under uncertainty and discuss how they can be used for planning, reinforcement learning, model checking, and simulation. Recent Findings Recent work has investigated models which more accurately capture multi-robot execution by considering different forms of uncertainty, such as temporal uncertainty and partial observability, and modelling the effects of robot interactions on action execution. Other strands of work have presented approaches for reducing the size of multi-robot models to admit more efficient solution methods. This can be achieved by decoupling the robots under independence assumptions or reasoning over higher-level macro actions. Summary Existing multi-robot models demonstrate a trade-off between accurately capturing robot dependencies and uncertainty, and being small enough to tractably solve real-world problems. Therefore, future research should exploit realistic assumptions over multi-robot behaviour to develop smaller models which retain accurate representations of uncertainty and robot interactions; and exploit the structure of multi-robot problems, such as factored state spaces, to develop scalable solution methods.
Blockchain in Financial Services: Current Status, Adoption Challenges, and Future Vision

Blockchain is undoubtedly considered one of the most innovative technologies in financial services from the past decade. Interests in blockchain technology continue to grow on a daily basis, while many promising blockchain-enabled applications and services continue to draw financial interest in the industrial sector and the broader financial services communities. Blockchain technology has the potential to streamline lending services and banking, decrease counterparty risk, and reduce settlement times and issuance. It indeed enables authenticated documentation and anti-money laundering (AML)/Know Your Client (KYC) data, minimizing operational ventures and allowing real-time/online validation of financial documents. Furthermore, as blockchain matures, it increases the value of a diversified range of industries and institutes with a better return on investment. However, such organizations must continue to adopt rigorous approaches to enhance their financial services and regulate their policies. In this article, we discuss the current status of blockchain-enabled financial services and applications, in addition to the impacts, implications, and regulations of incorporating blockchain technology in the finance industry. We further tackle the adaptation model for blockchain technology in global banking and provide a vision for the next generation of financial services based on this emerging technology.
Exploring factors influencing pre-service teacher’s digital teaching competence and the mediating effects of data literacy: empirical evidence from China

Abstract The development of pre-service teachers’ digital teaching competence is crucial for effectively infusing technology into teaching. With the growing importance of data in education, it is imperative to explore the influencing factors of digital teaching competence and the potential role of data literacy in facilitating competence. Thus, this study focused on investigating the factors influencing pre-service teachers’ digital teaching competence, namely technology attitudes, technology operations, technology ethics, and data literacy. Additionally, it examined the potential effect of data literacy on digital teaching competence. The study involved 244 Chinese pre-service teachers, and a Structural Equation Model (SEM) was created using SPSS and SmartPLS for analysis. The findings highlighted that technology attitudes, technology ethics, and data literacy directly influenced pre-service teachers’ digital teaching competence. Data literacy fully mediated the relationship between technology operations and digital teaching competence, and partially mediated the relationships between technology attitudes and digital teaching competence, as well as between technology ethics and digital teaching competence. Moreover, technology ethics acted as a partial mediator between technology attitudes and both digital teaching competence and data literacy. These results indicated that fostering positive technology attitudes, technology operations, and technology ethics could enhance pre-service teachers’ data literacy and improve their digital teaching competence.
Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation

Anomaly detection in sequences is a complex problem in security and surveillance. With the exponential growth of surveillance cameras in urban roads, automating them to analyze the data and automatically identify anomalous events efficiently is essential. This paper presents a methodology to detect anomalous events in urban sequences using pre-trained convolutional neural networks (CNN) and super-resolution (SR) models. The proposal is composed of two parts. In the offline stage, the pre-trained CNN model evaluated a large dataset of urban sequences to detect and establish the common locations of the elements of interest. Analyzing the offline sequences, a density matrix is calculated to learn the spatial patterns and identify the most frequent locations of these elements. Based on probabilities previously calculated from the offline analysis, the pre-trained CNN, now in an online stage, assesses the probability of anomalies appearing in the real-time sequence using the density matrix. Experimental results demonstrate the effectiveness of the presented approach in detecting several anomalies, such as unusual pedestrian routes. This research contributes to urban surveillance by providing a practical and reliable method to improve public safety in urban environments. The proposed methodology can assist city management authorities in proactively detecting anomalies, thus enabling timely reaction and improving urban safety.
Real-time Monitoring and Analysis of Edge and Cloud Resources

As Edge and Cloud platforms increasingly become the basis for hosting applications, monitoring systems need to be able to monitor both the hosts and the deployed applications. Ensuring seamless monitoring of Edge hosts is the most crucial factor, regardless of whether they are virtual machines, Raspberry Pis, or even typical personal computers. The application orchestration process could benefit greatly from monitoring systems, as they could serve as the main source of characterizing hosts and reporting metrics related to users and platforms. This paper introduces a Prometheus-based monitoring stack that is able to monitor both applications and resources on Kubernetes clusters. This monitoring stack has been tested and is already in use within the ACCORDION platform to efficiently monitor applications, regardless of their development unit and host types.
Using Microsoft Teams as a Learning Management System in English Courses: A story from a Vocational School

Microsoft Teams is an innovative online learning platform that offers a variety of one-of-a-kind features. These features boost the platform's ability to assist English as a foreign language instructor in facilitating a better learning environment and engagement online. The purpose of this study was to investigate the perceptions held by English teachers on the utilization of Microsoft Teams as a learning management system. A total of 35 English teachers from a vocational school participated in the research study. Questionnaires were used to gather data in order to acquire information English instructors’ perceptions towards the organization of the virtual classroom as well as the submission and grading of assignments using Microsoft Teams. The questionnaire was sent to the teachers' personal email addresses, so they could respond electronically. In addition, there was a free-form question that participants were asked to answer in order to investigate whether or not they had encountered any problems while using Microsoft Teams. A descriptive analysis was done on the data acquired from the questionnaires. According to the findings of the survey, English teachers have very positive perceptions on the utilization of Microsoft Teams as a learning management system. Almost no problems were discovered, with the exception of a glitch that occurred while a small number of students were trying to log in.
The mass formula for self-orthogonal and self-dual codes over a non-unitary commutative ring

&lt;abstract&gt;&lt;p&gt;In this paper, we establish a mass formula for self-orthogonal codes, quasi self-dual codes, and self-dual codes over commutative non-unital rings $ {{\mathit {I}_p}} = \left &amp;lt; a, b | pa = pb = 0, a^2 = b, ab = 0 \right &amp;gt; $, where $ p $ is an odd prime. We also give a classification of the three said classes of codes over $ {{\mathit {I}_p}} $ where $ p = 3, 5, $ and $ 7 $, with lengths up to $ 3 $.&lt;/p&gt;&lt;/abstract&gt;