Reviewerly/exHarmony huggingface dataset:
Automated region-of-interest selection for computer-vision-based displacement estimation of civil structures

A recent trend in vision-based displacement measurement is to place a camera at the measurement point and capture the images of the surrounding areas. In this scheme, a proper region of interest (ROI) should be selected from the captured images. This paper proposes an automated ROI selection technique to improve displacement estimation accuracy. The image frames that capture larger movements of the surrounding areas were selected, and the features in the selected frames were grouped using clustering algorithms. The feature group with consistent movement and high density was finally selected as the optimum ROI. The proposed technique was validated through laboratory and field tests. A displacements estimation technique previously proposed by the authors were used to compared the optimum ROI and four intuitively selected ROIs. In all the tests, the displacement estimates from the optimum ROI showed a smaller RMSE (less than 2 mm) than those from other ROIs.
Dicer: Dialogue-Centric Representation for Knowledge-Grounded Dialogue through Contrastive Learning

Knowledge-grounded dialogue is a task that utilizes external knowledge to generate appropriate and fluent responses to statements. Owing to the relevance of generating responses based on relevant knowledge in diverse fields, the knowledge selection task has been spotlighted. In this study, we propose a novel selection model that applies contrastive-learning with negative sampling loss to create dialogue-centric representation of knowledge. A two-part loss is considered knowledge selection loss and topic prediction loss. The former increases the similarity between content representations of related knowledge and dialogue history, while the latter increases the similarity between their topic representations. The proposed model was evaluated on two well-known datasets,Wizard of Wikipedia and Holl-E, in terms of the knowledge-grounded dialogue task exhibiting remarkable improvement over previously proposed methods on both knowledge selection and response generation tasks.
Inversion and forward estimation with process-based models: An investigation into cost functions, uncertainty-based weights and model-data fusion

Process-based models for inversion and forward estimation have supported our understanding of earth, environment, and hazards processes. These methods are often applied on spatial data without accounting for their spatial nature and uncertainty. Using an example of reconstructing past volcanic eruption characteristics and associated tephra fallout from different sets of field observation, we demonstrate the importance of making the best use of data-related uncertainty and spatial information in inversion and forward estimation. We present strategies for: (1) the selection of appropriate cost functions accounting for their behaviour and implied distribution of residuals, (2) the treatment of differential uncertainty when combining multiple data, and (3) the leveraging of both model and data when estimating the spatial distribution of output. Results show that a data-informed choice of cost function and accounting for uncertainty and spatial characteristics of data leads to consistent improvements in model predictive performance for both inversion and forward models.
Novel adaptive zeroing neural dynamics schemes for temporally-varying linear equation handling applied to arm path following and target motion positioning

While the handling for temporally-varying linear equation (TVLE) has received extensive attention, most methods focused on trading off the conflict between computational precision and convergence rate. Different from previous studies, this paper proposes two complete adaptive zeroing neural dynamics (ZND) schemes, including a novel adaptive continuous ZND (ACZND) model, two general variable time discretization techniques, and two resultant adaptive discrete ZND (ADZND) algorithms, to essentially eliminate the conflict. Specifically, an error-related varying-parameter ACZND model with global and exponential convergence is first designed and proposed. To further adapt to the digital hardware, two novel variable time discretization techniques are proposed to discretize the ACZND model into two ADZND algorithms. The convergence properties with respect to the convergence rate and precision of ADZND algorithms are proved via rigorous mathematical analyses. By comparing with the traditional discrete ZND (TDZND) algorithms, the superiority of ADZND algorithms in convergence rate and computational precision is shown theoretically and experimentally. Finally, simulative experiments, including numerical experiments on a specific TVLE solving as well as four application experiments on arm path following and target motion positioning are successfully conducted to substantiate the efficacy, superiority, and practicability of ADZND algorithms.
Introduction

Monetary and financial systems have become exposed to privately issued digital representations of value with monetary characteristics that rely on encryption, such as distributed ledger technology (DLT), and are generally known as crypto assets. The rapidly expanding crypto asset sphere comes mainly in two forms: cryptocurrencies (e.g., Bitcoin) and stablecoins (e.g., USD Tether).
Gerakan Literasi Mahasiswa: Studi Kasus Gerakan Literasi Komunitas Akar Sosial di Universitas Muhammadiyah Makassar

The problem in this study examines the literacy movement of sociology students who are members of a social root community that does not use campus intra-organizations (HMJ) as a forum for literacy movements. This study aims to find out why the literacy movement of sociology students who are members of a social root community uses the form of community as a forum for their literacy movement. This research is a qualitative descriptive research and uses an intrinsic case study approach. The results of this study indicate that the choice of community as a container movement is a strategy in the social root community literacy movement. The conclusion from this study is that apart from being a literacy movement strategy used by pioneering social root communities, it is also an alternative platform compared to using non-strategic intra-organizations as a movement platform.
Introduction

In this chapter, the basic concept of a knowledge graph is introduced. We discuss why knowledge graphs are important for machine learning and data mining tasks, and we show classic feature extraction or propositionalization techniques, which are the historical predecessor of knowledge graph embeddings, and we show how these techniques are used for basic node classification tasks.
Tweaking RDF2vec

Depending on the problem at hand, one might think of different tweaks to the RDF2vec algorithm, many of which have been discussed in the past. Those tweaks encompass various steps of the pipeline: reasoners have been used to preprocess the knowledge graph and add implicit knowledge. Different strategies for changing the walk strategy have been proposed, starting from injecting edge weights to biasing the walks towards higher or lower degree nodes, and changing the structure of the extracted walk completely. Moreover, also the embedding creation itself has been analyzed in the past by using different variants of the word2vec word embedding method. In this chapter, we introduce a few of those approaches and highlight their advantages and shortcomings.
From Word Embeddings to Knowledge Graph Embeddings

Word embedding techniques have been developed to assign words to vectors in a vector space. One of the earliest such methods was word2vec, published in 2013 – and embeddings have gathered a tremendous uptake in the natural language processing community since then. Since RDF2vec is based on word2vec, we take a closer look at word2vec in this chapter. We explain how word2vec has been developed to represent words as vectors, and we discuss how this approach can be adapted to knowledge graphs by performing random graph walks, yielding the basic version of RDF2vec. We explain the CBOW and SkipGram variants of basic RDF2vec, revisiting the node classification tasks used in Chap. 1 .
RDF2vec at Scale

On larger knowledge graphs, RDF2vec models can be very expensive to train. In this chapter, we look at two techniques that make RDF2vec easier to use with large knowledge graphs. First, we look at a knowledge graph embedding server called KGvec2go, which serves pre-trained embedding vectors for well-known knowledge graphs such as DBpedia as a service. Second, we look at how we can train partial RDF2vec models only for instances of interest with RDF2vec Light.
Link Prediction in Knowledge Graphs (and its Relation to RDF2vec)

In recent years, a long list of research works has been published which utilize knowledge graph embeddings for link prediction (rather than node classification, which we have considered so far). In this chapter, we give a very brief overview of the main embedding techniques for link prediction and flesh out the main differences between the well-known link prediction technique TransE and RDF2vec. Moreover, we show how RDF2vec can be used for link prediction.
Benchmarking Knowledge Graph Embeddings

RDF2vec (and other techniques) provide embedding vectors for knowledge graphs. While we have used a simple node classification task so far, this chapter introduces a few datasets and three common benchmarks for embedding methods—i.e., SW4ML, GEval, and DLCC—and shows how to use them for comparing different variants of RDF2vec. The novel DLCC benchmark allows us to take a closer look at what RDF2vec vectors actually represent, and to analyze what proximity in the vector space means for them.
PRIORITIZED RISKS AND TREATMENT STRATEGIES IN GLOBAL SOFTWARE DEVELOPMENT (GSD)

The study conducts a review for the literature in order to compile the risks that are directly related to the GSD strategy. The case study adopts a Delphi methodology that allows the researcher to achieve consensus on the most relevant and critical risks associated with the GSD project. Twenty software development experts from all around the world participated in the study, with a minimum of fifteen expert in each Delphi round. The four rounds of the Delphi method used in this study are designed to gain consensus on the most crucial risks of the GSD strategy, as well as perform a risk assessment for all the risks compiled from the literature and verified by the participating experts. The results show that there are ten main risks that gained consensus by software development experts and need to be addressed as a priority in GSD projects, where all of them have medium to high probability of occurrence and impact on the software project success using a GSD strategy.
Enhanced Dct Image Watermarking Scheme Based on Zonal Sampling Algorithm Applied With Sspce Contour Extraction, Ramer and Adaptive Triangle Contour Compression Methods

This paper proposes an image watermarking scheme based on DCT transform and selected zonal sampling algorithms. The scheme used LPF and HPF transform coefficients in a certain geometric zone of the original image are retained. All the rest of the parameter is set to zero (0), quantized and transmitted where the watermark will be embedded. In order to test robustness, some attacks are performed. However, prior to the watermark extraction process, the attacked watermarked image will be subdued into contour processing methods using Single Step Parallel Contour Extraction, Ramer and Adaptive Triangle algorithms. The proposed method for contour compression will help enhance image recognition, security and especially where time (speed) is an essential factor. The results show that the proposed watermarking method is robust and secure against various signal/image processing attacks with PSNR values of approximately 35 dB with NCC values of 0.60 and above. In addition, experimental results from contour compression indicate that the proposed Adaptive Triangle method has a high compression ratio without significant visible distortion
Research and implementation of visual question and answer system based on deep learning

Abstract With the development and improvement of deep learning technology, its application and practice in modal data (image, speech, and text) has been achieved tremendously. In this paper, based on the neural network modality class model in deep learning, we analyze its adaptation to the visual question and answer system, propose a visual question and answer model based on the gated attention mechanism, and construct a question, and answer prediction mechanism adapted to the recurrent neural network transfer model. To address the problem of low accuracy of the model on complex problems, the inference network module is built using visual inference so that the model can extract complex problem features to improve the inference capability of the model. By predicting answers through semantic information about text and visual elements in images, correlations across modalities, and inference, advances in natural language processing and computational vision have led to improved answer accuracy in deep learning-based visual quiz models. Multiple sets of experiments show that models with deep learning inference capabilities answer complex questions with significantly higher accuracy than other existing methods.
Enhancement algorithm of low illumination image for UAV images inspired by biological vision

To address the issue of low brightness, high noise and obscure details of UAV aerial low-light images, this paper proposes an UAV aerial low-light image enhancement algorithm based on dual-path inspired by the dual-path model in human vision system. Firstly, a U-Net network based on residual element is constructed to decompose UAV aerial low-light image into structural path and detail path. Then, an improved generative adversarial network (GAN) is proposed to enhance the structural path, and edge enhancement module is added to enhance the edge information of the image. Secondly, the noise suppression strategy is adopted in detail path to reduce the influence of noise on image. Finally, the output of the two paths is fused to obtain the enhanced image. The experimental results show that the proposed algorithm visually improves the brightness and detail information of the image, and the objective evaluation index is better than the other comparison algorithms. In addition, this paper also verifies the influence of the proposed algorithm on the target detection algorithm under low illumination conditions, and the experimental results show that the proposed algorithm can effectively improve the performance of the target detection algorithm.
APPLICATION OF BLOCKCHAIN TECHNOLOGIES IN ENSURING THE SECURITY OF LENDING TRANSACTIONS IN THE DIGITAL ECONOMY

Currently, the process of digitization of the financial and banking sectors in our country continues consistently. The banking sector is one of the important links of the digital economy. Therefore, the introduction of modern innovative technologies in the digitization of this field is one of the important tasks. This article presents the opinions of world scientists and models of financial organizations on the application of blockchain technology to the field of bank lending, the blockchain model of the UzBCS platform for the implementation of the lending process, blockchain blocks and cryptographic algorithms used in the implementation of the blockchain lending process. Also, the proposed cryptographic algorithms and blockchain system software implementation results are presented.
State management of education and training in Vietnam

The relevance of the research is due to the fact that nowadays, public administration is a rather important factor that affects any social system, in particular, the structure of education and upbringing. Since the development of the education system is directly related to the development of the education mechanism itself, it is necessary to take measures to reform the state management of education. The purpose of the study was to find out how the level of state management of the education system in Vietnam affects the future profession and success of students. To achieve the goals and objectives, the following methodological approaches were used: the analysis method, the theoretical approach, the systematic approach, the deduction method, the induction method, and others. The article reveals and analyses the concept of “state management of education and training”. Modern methods of modernization of public administration in the field of education and training in Vietnam are also defined. It was revealed how important the educational sector is for the formation of the state. The results of the survey were analysed, which showed what factors should be added to improve the education system. The peculiarities of the implementation of state administration in the activity of a modern general educational institution are considered. The main components of the state model of management of education and training in educational institutions are defined, the purpose of public education in modern Vietnam is highlighted, as well as the criteria on which the quality of the education system depends. With the help of methods of analytical and statistical analysis, the features of the state administration system in the field of education and training were studied. The need to modernize education and training management is substantiated. It has been proven that the quality of education is related to the effectiveness of management. The educational process is considered as an important characteristic of the state’s competitiveness. The obtained research results provide an opportunity to determine effective ways to improve the quality of education management.
Reference-frame-independent quantum key distribution based on passive light source monitoring

In quantum key distribution (QKD), the users need to share the same reference frame. If their reference frames are inconsistent, the QKD system will not function properly. The most widely used method today is the active real-time calibration of both communication reference frames by using classical communication. In order to get rid of the real-time calibration operation of the reference frames, a QKD protocol independent of reference frame is proposed, called reference-frame-independent QKD (RFI-QKD). The RFI-QKD protocol is immune to the effects of slowly changing reference frame drift, requiring that only one set of bases should be aligned by Alice and Bob, and the remaining two sets of bases can slowly change in the channel. In the real QKD system, a set of basis vectors can always be found to maintain a stable alignment. However, some assumptions are made for the sources in most reported researches, i.e. with a trusted and fixed photon-number distribution (PND), which usually cannot be satisfied in practical implementations. Those unreasonable assumptions will inevitably compromise the security of practical QKD systems. To solve the problem, in this work, we present a passive light source monitoring (PLSM) scheme for RFI-QKD, which is accomplished by a passive monitoring module consisting of a beam splitter and two detectors on the source side. Through the PLSM module, we can have four monitoring events by using two local detectors and then precisely estimate the bounds of source distributions. Specifically, we take the three-intensity decoy-state-based RFI-QKD for example for illustraing the events. Compared with the original RFI-QKD, our PLSM method can passively monitor the PND and has many advantages, with light source fluctuations, finite-size effects and reference frame deflection angles taken into account.
THE ROLE OF A DECISION TREE AS A METHOD OF ARTIFICIAL INTELLIGENCE FOR THE ANALYSIS OF BIG DATA PROBLEMS

The article discusses the role and importance of decision trees as a method of artificial intelligence in solving analytical problems using big data. Decision trees are one of the most effective tools for data analysis and decision-making in various fields, including medicine, economics, business and many others. The article discusses the basic principles of decision trees, their advantages and disadvantages, as well as examples of using this method for analyzing big data.
Deeper Exploiting Graph Structure Information by Discrete Ricci Curvature in a Graph Transformer

Graph-structured data, operating as an abstraction of data containing nodes and interactions between nodes, is pervasive in the real world. There are numerous ways dedicated to extract graph structure information explicitly or implicitly, but whether it has been adequately exploited remains an unanswered question. This work goes deeper by heuristically incorporating a geometric descriptor, the discrete Ricci curvature (DRC), in order to uncover more graph structure information. We present a curvature-based topology-aware graph transformer, termed Curvphormer. This work expands the expressiveness by using a more illuminating geometric descriptor to quantify the connections within graphs in modern models and to extract the desired structure information, such as the inherent community structure in graphs with homogeneous information. We conduct extensive experiments on a variety of scaled datasets, including PCQM4M-LSC, ZINC, and MolHIV, and obtain a remarkable performance gain on various graph-level tasks and fine-tuned tasks.
PEER-TO-PEER LENDING AND BORROWING

P2P lending has become a popular financing option for individuals and small businesses, challenging the traditional lending system. The use of blockchain technology can optimize P2P lending by reducing intermediaries and utilizing smart contracts. This paper proposes a blockchain-supported framework for P2P lending, exploring blockchain 2.0 features in areas such as digital contracting, risk management, and regulation of P2P markets. Blockchain can improve loan processing time, reduce operational risks, and increase funding efficiency. The use of blockchain in P2P lending has the potential to benefit both lenders and borrowers.
PENERAPAN PROBLEM BASED LEARNING UNTUK MENINGKATKAN KEMAMPUAN LITERASI SOSIAL PADA MATA PELAJARAN IPS SEKOLAH DASAR

This study aims to enhance students' social literacy skills in the Social Studies subject at 076 Sukajadi Elementary School, Bandung City, West Java, through the implementation of the Problem Based Learning (PBL) model. The research method used in this study was Classroom Action Research (CAR) with the spiral model of Kemmis-McTaggart. The subjects of this study were 30 fifth-grade students, comprising 13 males and 17 females. Data were collected using observation sheets and learning outcome tests. The results of the study indicate that students' social literacy skills in the Social Studies subject at 076 Sukajadi Elementary School can be improved through the use of the Problem Based Learning model in teaching and learning. It is hoped that the findings of this study will assist teachers in selecting the appropriate instructional model to enhance students' social literacy skills, particularly in aspects such as improving intellectual abilities, cooperation, attitudes, and social values.