Reviewerly/exHarmony huggingface dataset:
Robot-enhanced diabetes care for middle-aged and older adults living with diabetes in the community: A small sample size mixed-method evaluation

This study assessed robot-enhanced healthcare in practical settings for the purpose of community diabetes care.A mixed method evaluation collected quantitative and qualitative data on diabetes patients over 45 (N = 30) and community pharmacists (N = 10). It took 15-20 min for the diabetes patients to interact with the robot. Before and after the interaction, questionnaires including a diabetes knowledge test, self-efficacy for diabetes, and feasibility of use of the robot was administered. In-depth interviews with both pharmacists and patients were also conducted.After interacting with the robot, a statistically significant improvement in diabetes knowledge (p < .001) and feasibility of the robot (p = .012) was found, but self-efficacy (p = .171) was not significantly improved. Five themes emerged from interviewing the diabetes patients: Theme 1: meets the needs of self-directed learning for the elderly; Theme 2: reduces alertness and creates comfortable interaction; Theme 3: vividness and richness enhance interaction opportunities; Theme 4: Robots are not without disadvantages, and Theme 5: Every person has unique tastes. Three themes emerged from interviewing pharmacists: Theme 1: Technology must meet the real needs of the patient; Theme 2: creates new services, and Theme 3: The use of robots must conform to real-life situations.Both the diabetes patients and the pharmacist reported more positive feedback on the robot-enhanced diabetes care than concerns. Self-directed learning, comfortable interaction, and vividness were the most focuses when using robot to enhance self-management for the patients. Pharmacists were most receptive to fit conforming with reality and creating new services.
IPAS-Net: A deep-learning model for generating high-fidelity shoeprints from low-quality images with no natural references

Single image super-resolution (SISR) typically reconstructs higher-resolution (HR) images from the corresponding low-resolution (LR) images in the presence of natural HR images. SISR is highly important in generating high-quality images in forensic scenarios since it facilitates close investigation and examination of captured shoeprints. However, it becomes more challenging when there are no available natural HR ground truth images that correspond to the input LR images. In such cases, HR reconstruction becomes even more crucial for providing HR versions that retain the natural characteristics of shoeprints. For this purpose, we propose IPAS-Net, which utilizes U-Net for feature extraction, shares the learned parameters from LR space in HR space, and innovatively upscales, refines, and enhances the HR space via special treatments. The upsampling-and-refinement block comprises a parallel pipeline composed of attention mechanism block (AMB) and one-step-high-iteration (OSHI). All upsampling solutions are infused so that distinct upscaling can compensate each others' weaknesses. The generated HR shoeprints are evaluated using blind/non-reference evaluation metrics, and the proposed method outperforms the state of the art (SOTA) deep learning modalities on low-quality shoeprints.
Design of Informatization College and University Teaching Management System Based on Improved Decision Tree Algorithm

At present, the teaching management system used in colleges cannot classify and store the teaching material information well and also has some problems, such as inaccurate calculation results of resource information weight, long response time, and large data query error. Therefore, this study designs an information college teaching management system based on improved decision tree algorithm. The hardware structure of the system consists of information communication structure, information teaching resource sharing structure, processor, and crystal oscillator circuit, and the core module is the data output control module. This study designs the system software based on the improved decision tree algorithm, creates a decision tree recursively, uses the CART decision tree to calculate the weight of teaching resource information, and constructs the fitness objective function of teaching resource information according to the mean clustering algorithm, so as to accurately extract the teaching resource information and realize the efficient processing of college teaching resource. The experimental results show that the response time of the system in this study is only 8 ms, the maximum convergence value is only 40, there is only one wrong data in the data query, and the storage time is 40 s. This system has a short response time, fast convergent rate, and a low probability of data search error when more than one client access the database at the same time.
Impact of Integrity and Internal Audit Transparency on Audit Quality: The Moderating Role of Blockchain

In today's corporate environment, the success of external auditors for producing quality reports depends on internal audit functions.Hence, ensuring internal audit quality is a must.Auditors' integrity and the presence of a transparent internal auditing process may all help to ensure internal audit quality.In this regard, with its irreversible nature, evolving blockchain technology (BT) is playing a critical role in offering a triple entry accounting system.Thus, the purpose of this article is to describe how integrity (INTI) and internal audit transparency (TRPY) affect internal audit quality (AQLY).It also assesses the potential of blockchain technology as a moderating function in influencing AQLY.The Partial Least Square Structural Equation Model (PLS-SEM) was used to describe the causal connection in this research.A self-administrated questionnaire was used to obtain primary data from Bangladeshi accounts and audit practitioners.According to the results, integrity and internal audit transparency substantially influence AQLY.The potential application of blockchain (APBN) has been discovered to moderate the relation between INTI and AQLY.TRPY and AQLY have a similar relationship, which APBN moderates.Thus, this research established a unique model employing INTI, TRPY, and APBN as the determinants, which provided a novel outlook in explaining the factors that can help in improving audit quality.
Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework

Entity recognition is a fundamental task in understanding document images.Traditional sequence labeling frameworks treat the entity types as class IDs and rely on extensive data and high-quality annotations to learn semantics which are typically expensive in practice.In this paper, we aim to build an entity recognition model requiring only a few shots of annotated document images.To overcome the data limitation, we propose to leverage the label surface names to better inform the model of the target entity type semantics and also embed the labels into the spatial embedding space to capture the spatial correspondence between regions and labels.Specifically, we go beyond sequence labeling and develop a novel label-aware seq2seq framework, LASER.The proposed model follows a new labeling scheme that generates the label surface names word-by-word explicitly after generating the entities.During training, LASER refines the label semantics by updating the label surface name representations and also strengthens the label-region correlation.In this way, LASER recognizes the entities from document images through both semantic and layout correspondence.Extensive experiments on two benchmark datasets demonstrate the superiority of LASER under the few-shot setting.
Perceptual Hash of Neural Networks

In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models.
Analytical Review of Education Policies on Digital Transformation of School Education Worldwide

The article reflects on the changes of educational landscape worldwide connected with the sustained development of society and introducing ideas of modernizing school education, as well as the "shock digitalization" triggered by the challenges of 2020.The aim of this research is to compare education policies of different countries in the field of digital transformation of school education by means of studying state programme documents on digitalization, and digital learning services and resources for students.The studied material includes open sources of information: articles, websites of state education authorities, content and tools of digital learning platforms and services available to registered users.The article features a characteristic of digital learning resources and services in the selected countries, a descriptive analysis of national policies and trends in digital transformation of education in each country.The authors have come to the following conclusions: school digital learning systems are used as a means of transporting values, languages, cultures and maintaining a unified learning space; school digital learning systems of different countries become interconnected due to the expansion of supranational digital learning resources and services, translation of learning content into different languages.The evolvement of the new model of school education is characterized by removed geographical and time barriers, openness of the learning process, customized training programmes, using mass network educational formats.To overcome the digital gap, measures are taken at different levels, from states to individuals who start their own educational blogs.Repositories of learning content are created in collaboration of users.
Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset

In handwriting recognition research, a public image dataset is necessary to evaluate algorithm correctness and runtime performance. Unfortunately, in existing Thai language script image datasets, there is a lack of variety of standard handwriting types. This paper focuses on a new offline Thai handwriting image dataset named Burapha-TH. The dataset has 68 character classes, 10 digit classes, and 320 syllable classes. For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets that were then digitized using a 300 dpi scanner. De-skewing, detection box and segmentation algorithms were applied to the raw scans for image extraction. The experiment used different deep convolutional models with the proposed dataset. The result shows that the VGG-13 model (with batch normalization) achieved accuracy rates of 95.00%, 98.29%, and 96.16% on character, digit, and syllable classes, respectively. The Burapha-TH dataset, unlike all other known Thai handwriting datasets, retains existing noise, the white background, and all artifacts generated by scanning. This comprehensive, raw, and more realistic dataset will be helpful for a variety of research purposes in the future.
The Potential of Artificial Intelligence as a Method of Software Developer's Productivity Improvement

Artificial Intelligence finds application at all stages of Software Engineering, and uses the Neural Networks, Machine Learning, Natural Language Processing concepts. This paper attempts to review the instance of such approach - neural network programmer’s assistant Copilot, based on Codex, the AI system developed by OpenAI. The differences between Codex language model’s versions and analogous systems were analyzed. The main problems and gaps of this innovation, such as correct commands formulation, copyrighting, safety issues, inefficient code, good practice examples and restrictions are also considered. Additionally, the opportunities for Copilot’s growth, development and possible features’ proposed recommendations were suggested.
Possibilities and limits of virtual and augmented reality in the purchase decision process for clothing

This paper examines the influence of virtual and augmented reality on the purchasing decision-making process for clothing. The aim of the study is to gain well-founded insights into the possibilities and limitations of using virtual and augmented reality (VR and AR) techniques in that process. It can be shown that VR and AR applications as interactive systems can offer valuable support in the purchasing decision-making process. New chances and possibilities arise for future shopping in virtual or augmented realities, in which customers are supported in their decision-making. So far, the simulation of visual properties, the limited range of offered pieces, the lack of customer acceptance as well as high barriers to entry and expensive hardware solutions have weakened the full exploitation of potential.
A cost-effective approach for mining near-optimal top-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1204" altimg="si695.svg"><mml:mi>k</mml:mi></mml:math> patterns

Nowadays, frequent pattern mining (FPM) on large graphs receives increasing attention, since it is crucial to a variety of applications, e.g., social analysis. Informally, the FPM problem is defined as finding all the patterns in a large graph with frequency above a user-defined threshold. However, this problem is nontrivial due to the unaffordable computational and space costs in the mining process. In light of this, we propose a cost-effective approach to mining near-optimal top-k patterns. Our approach applies a "level-wise" strategy to incrementally detect frequent patterns, hence is able to terminate as soon as top-k patterns are discovered. Moreover, we develop a technique to compute the lower bound of support with smart traverse strategy and compact data structures. Extensive experimental studies on real-life and synthetic graphs show that our approach performs well, i.e., it outperforms traditional counterparts in efficiency, memory footprint, recall and scalability.
An analysis of the appearance characteristics of Korean ceramics per era through statistical analysis of metadata annotated with a visual element classification system of ceramics

Abstract This study is to create metadata with a ceramic visual element classification system, such as color, form, material, and pattern, and then to analyze the visual characteristics of Korean ceramics by era through this metadata statistical analysis. To achieve this, first, the visual element classification system for ceramics was established. Second, 7,346 ceramic photos were acquired and annotated using the visual element classification system to create metadata. Third, through statistical analysis of the metadata, the preferred visual elements in each era were organized, and their characteristics were analyzed. In particular, the differences in form implementation, color technology, and pattern representation, which vary depending on material properties, were identified. Through this study, the flow of visual elements of Korean ceramics and the reason for each type of appearance and production method could be comprehended more systematically.
Explainable document-level event extraction via back-tracing to sentence-level event clues

Document-level event extraction (DEE) aims to extract events directly from a document. Current state-of-the-art methods follow the entity-centric framework, capturing sentence-level event clues into entity instance representations for DEE. However, in the entity-centric framework, sentence-level event clues are captured in a black-box fashion, and thus cannot be traced back to explain extracted events. In this paper, we propose a novel DEE framework ExpDEE to support explaining each extracted event. Specifically, ExpDEE explicitly captures sentence-level event clues as a series of middle results during event extraction, which reversely enables ExpDEE to back-trace each extracted event to its sentence-level event clues for explanation. To verify the effectiveness of ExpDEE, we conduct experiments on two public benchmarks: ChFinAnn and DuEE-fin. Compared with existing methods, ExpDEE is shown to achieve state-of-the-art performances on both benchmarks. Furthermore, we apply ExpDEE to explain some typical extracted results in case study, which verifies its reasonable explainability.
Evolutionary Game and Simulation Research of Blockchain-Based Co-Governance of Emergency Supply Allocation

Recently, with the spread of COVID-19 pandemic, emergency supply allocation system is drawing more and more social attention. Emergency supply allocation system is an important part of emergency governance system. It reflects social organizations’ credibility, public safety, and the modernization level of social governance. However, emergency supply allocation system still has some problems, such as information asymmetry, different desires of participants, unreasonable allocation, and so on. At present, it is widely accepted that the advantage of blockchain in co-governance could be of great help in solving above problems. And in order to distinguish the effect of blockchain to the emergency supply allocation, the paper builds a tripartite evolutionary game model among the government, social organizations, and the public to analyse the impact of blockchain platform on emergency supply allocation. The simulation analysis shows the following: (1) The strategy choices of the government have a crucial impact on the evolution and stability of social organizations’ strategies. It needs a long-term process to guide social organizations practicing active allocation, and the government should accelerate to build the blockchain platform to promote this process. (2) With the help of blockchain platform, the increment of penalty intensity of the government is conductive to increasing the probabilities that social organizations practice active allocation and the government practices strict supervision. (3) Blockchain platform has a significant impact on social organizations’ choice for active allocation in many aspects, such as the positive and negative effects of social organizations, effect’s increasing multiple, and the cost of the public informing. In the end, some suggestions are presented to improve the co-governance of emergency supply allocation.
Towards a stronger theory for permutation-based evolutionary algorithms

While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve permutation-based problems. To overcome the lack of permutation-based benchmark problems, we propose a general way to transfer the classic pseudo-Boolean benchmarks into benchmarks defined on sets of permutations. We then conduct a rigorous runtime analysis of the permutation-based $(1+1)$ EA proposed by Scharnow, Tinnefeld, and Wegener (2004) on the analogues of the \textsc{LeadingOnes} and \textsc{Jump} benchmarks. The latter shows that, different from bit-strings, it is not only the Hamming distance that determines how difficult it is to mutate a permutation $\sigma$ into another one $\tau$, but also the precise cycle structure of $\sigma \tau^{-1}$. For this reason, we also regard the more symmetric scramble mutation operator. We observe that it not only leads to simpler proofs, but also reduces the runtime on jump functions with odd jump size by a factor of $\Theta(n)$. Finally, we show that a heavy-tailed version of the scramble operator, as in the bit-string case, leads to a speed-up of order $m^{\Theta(m)}$ on jump functions with jump size~$m$.%
Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification

We study a quantum stochastic neural network (QSNN) based on quantum stochastic walks on a graph, and use gradient descent to update the network parameters. We apply a toy model of QSNN with a few neurons to the problems of function approximation, two-dimensional data classification, and sequence classification. A simple QSNN with five neurons is trained to determine whether a sequence of words is a sentence or not, and we find that a QSNN can reduce the number of training steps. A QSNN with 11 neurons shows a quantum advantage in improving the accuracy of recognizing new types of inputs like verses. Moreover, with our toy model, we find the coherent QSNN is more robust against both label noise and device noise, compared with the decoherent QSNN. These results show that quantum stochastic walks may be a useful resource to implement a quantum neural network.
Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

The goal of 6G communication networks requires higher transmission speeds, tremendous data processing, and low-latency communication. Orthogonal frequency-division multiplexing (OFDM), which is widely utilized in 5G communication systems, may be a viable alternative for 6G. It significantly reduces inter symbol interference (ISI) in the frequency-selective fading environment. Channel estimation is critical in OFDM to optimize system performance. Deep learning has been employed as an appealing alternative for channel estimation and signal detection in OFDM-based communication systems due to its better potential for feature learning and representation. In this study, we examine the deep neural network (DNN) layers created from long-short term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. We investigate the performance of the system under various conditions. The simulation results show that the signal bit error (SER) is equivalent to and better than that of the minimum mean squared error (MMSE) and least square (LS) methods.
Analysis and Optimization of the Online Vocal Teaching System Based on Intelligent Computing

With the continuous development of science and technology, network technology has become more and more advanced and network applications are widely used in all walks of life, and vocal music teaching is no exception. The Intelligent Agent plays a unique role in networked teaching by making up for the lack of intelligence, adaptiveness, autonomy, and interoperability and interactivity in all aspects of traditional teaching. This paper introduces the knowledge of Intelligent Agent and then discusses its application in the networked teaching environment. Compared with the traditional teaching environment, the intelligent network teaching environment is more conducive to the initiative and innovation of students, reflecting the characteristics of student-centred learning and achieving a variety of functions such as teaching resource sharing, information interaction, online communication, and distance learning with the support of computer network technology. It is a new field to be explored, especially opening up a new chapter of online vocal music teaching.