Reviewerly/exHarmony huggingface dataset:
TAIRA-BSC - Trusting AI in Recruitment Applications through Blockchain Smart Contracts

Artificial intelligence (AI) and blockchain technology (BCT) are considered two of the most trending and disruptive technologies. BCT, although commonly associated with cryptocurrencies, has shown a tremendous impact among many other distributed applications domains. BCT characteristics, such as the distribution of data storage among independent nodes and the use of consensus algorithms offer immutability and transparency and remove the need for a central authority making BCT trustworthy. However, decision-makers and stakeholders currently lack the confidence to overcome uncertainty related to AI technology, which affects the acceptance of AI technology in wider application domains, such as the recruitment process. Furthermore, current research literature does not adequately investigate the role of trust as an integral part of an AI-based recruitment application. Therefore, this paper aims to investigate how emerging BCT and AI technologies can improve decision making and stakeholder trust in a job recruitment system that is traditionally focused on just human expert decision-making. In this paper we propose the design of a new solution for trusting AI in recruitment applications through the use of Blockchain Smart Contracts (TAIRA-BSC). TAIRA-BSC integrates Blockchain Smart Contracts (BSC) with the Data Lake (DL), Machine Learning (ML) and AI technologies in our AI Recruitment Model (AIRM) architecture. TAIRA-BSC improves transparency and interoperability in the recruitment process while protecting sensitive job candidate data and ensures data integrity delivery and traceability in the recruiting process through a verifiable decentralized ledger, i.e., the blockchain and associated smart contracts. The paper presents a discussion on the state-of-the-art of integrating AI with BCT focusing on how BCT can be used to bridge trust concerns with AI systems. We also present a conceptual architecture TAIRA-BSC proof of concept that is developed to serve as a foundation for future studies focused on enhancing trust in AI applications through the integration of BCT.
An Improved Image Denoising Method Based on Variable-order Fractional-Order Anisotropic Diffusion

In recent years, the fractional partial differential equations based method has been widely studied in the image denoising field. Usually, constant-order fractional differential order is employed to fractional partial differential equations based method, which fails to simultaneously achieve the optimization of denoising effect and structure preserve ability. In this paper, we proposed a variable-order fractional-order anisotropic diffusion model. The method adaptively divides the image into the homogeneous region and the non-homogeneous region by local variance. In the homogeneous region, a relatively small fractional differential order is employed to improve the denoising effect and avoid the staircase effect. In the non-homogeneous region (including edge and texture), a relatively large fractional differential order is employed to improve structure-preserving ability. Experimental results show that the proposed method has a better denoising effect and structure-preserving ability than the traditional.
The traversal method for user address space in Windows 10 system based on VAD tree

The existing traversal method for user address space in the memory forensic research is only applicable to Windows XP and Windows 7 32-bit system. Windows 10 64-bit system is currently used by most users, which is the main target of network attackers. A method to traverse Windows 10 user address space based on VAD(virtual address descriptor) tree is proposed. The memory kernel and user address space metadata of Windows 10 64-bit system was located. The related metadata such as mapping files, shared memory, heap, stack and reserved system structures were parsed and matched with the information in VAD tree nodes. The starting address, ending address, used size, allocating protection, memory type and details of each memory area were output. The results show that the method is compatible with all versions of Windows 10 64-bit system and can effectively traverse common structures when dealing with processes with different complexity.
Techno-entrepreneurship - pathway to sustainable business performance: empirical evidence from SMEs in Ogun state, Nigeria

This study examines how techno-entrepreneurship, measured as Intellectual Property Rights (IPRs) and Research, Development and Innovation (R, D & I), impacts sustainable business performance in small and medium-sized enterprises (SMEs). A random sample of 126 respondents was taken from purposively selected agro-businesses for a survey and responses were analysed using linear regression. The study results demonstrate significant and positive impact of technopreneurship on the profitability and earned revenue of SMEs, thus suggesting a technopreneurship model - nexus of drivers, constructs and prospects - as a solution to drive sustainable business performance. Based on findings, the study recommends that businesses should possess or sell and commercialise IPRs and practice open innovation (R, D & I). Finally, policy makers - primarily the government - should formulate and implement supportive policies, strategies and as well create an enabling environment for technopreneurship to thrive, towards growth of SMEs and the larger economy.
Urban Tales Visible With Augmented Reality

With the pandemic lockdowns, society was confronted with its social relations and everyday routines and how it relates with the conformity of life. To understand how we may evolve from this experience, past and present history (knowledge) comes to fruition, and its importance becomes paramount. So, an alternative way of information availability and relation gives ground to experiment with new ways of story consciousness, of true and unbiased knowledge about anything. This chapter presents a form of augment community search for purpose and consciousness through current and future technology. In the case of augmented reality, and how information its exhibited to the public, it brings a lively dimension as if information becomes commonly present in this reality, as past meets the now, as history is alive in the present. This chapter shows an example of how, in a context of an urban exhibition, a life tales' exhibition in an emptied neighborhood of Porto city, augmented reality might connect and socially relate people, either locals or tourists, with interesting information.
Factors Affecting the Security Threat Avoidance Behaviors of Mobile Financial Service Users

Mobile financial services (MFSs) help customers execute basic financial services with their mobile phones, thereby bringing common people under the formal financial system. Despite the inherent potentiality of MFS, an increasing number of mobile financial frauds reported in different media are threatening customers. To fully analyze factors affecting the avoidance behaviors of the users, the study has employed two widely used theories, namely the theory of planned behavior (TPB) and the technology threat avoidance theory (TTAT). To reveal the complex relationship among the constructs, the partial least squared based structural equation modeling (PLS-SEM) was used to analyze the data of 255 respondents in Bangladesh. The results indicate an interesting finding that the avoidance motivation of the users is not affected by attitude. However, all other exogenous factors such as subjective norm, perceived behavioral control, perceived cost of non-avoidance significantly impact the avoidance motivation. The results also support that avoidance motivation leads to the avoidance behavior in a significant manner.
The Effect of the Quality of Lifelong Education Service on Successful Aging of the Elderly

This paper examines the relationships between lifelong education service quality, education satisfaction, self-efficacy, and successful aging of the elderly in lifelong education facilities for the elderly, and to suggest implications. To achieve the purpose of this paper, a survey was conducted on the elderly who had experience participating in lifelong education facilities in Busan and Gyeongnam, and 276 cases were used for the final survey analysis. Reliability and validity of the collected data were reviewed, and hypotheses were verified through structural equation modeling.BR As a result of the study, first, it was confirmed that the tangible, reliability sub-factors of lifelong education service quality, except for empathy, had a positive effect on the educational satisfaction of the participating elderly, respectively. Second, educational satisfaction has the significant positive effect on self-efficacy and successful aging, respectively. Third, self-efficacy also has a significant positive effect on successful aging. Based on these research results, the importance of quality of lifelong education service is suggested in the successful aging of the elderly, and it is required necessary that the elderly need to have the provision of opportunities for lifelong education programs considering the various needs of the elderly such as psychological factors.
Thinking about the Problems in the Faculty Mechanical Engineering Program from the Experience of Design Training

In this study, we examined the contents of the original design training program conducted over the past 21 years and confirmed the necessity of a paradigm shift in education. Within this training program, students are familiarized with the entire process of developing an original machine or device. Through processes such as product planning and cost control, mechanical and structural sketching, and product manufacturing and assembly, students acquire substantial knowledge that cannot be gained from specialized classes. Based on the results of this training, we identified the issues concerning engineering education in the current faculty. Further, we proposed that the educational paradigm should be reformed by considering the social progress, technological development, and changes in the current learning environment.
Feature-Weighted Echo State Network for Dynamic Frequency Offset Modeling of Quartz Crystal Resonators

Quartz crystal resonators are widely used as reference frequency sources in modern electronic systems. However, their frequency often deviates from the nominal value due to the significant change in the ambient temperature. Therefore, it is of great value to develop an accurate dynamic frequency offset model regarding temperature changes. In this article, a novel feature-weighted echo state network (FWESN) method is presented to capture the dynamic frequency-temperature ( f - T ) characteristic of quartz crystal resonators. Different from the traditional echo state network (ESN), which simply takes the temperature measurement signal as a single model input variable, the proposed method mines the feature information hidden in the temperature measurement series to construct the model input vector. Specifically, five dynamic features are designed to substitute for the original temperature signal by investigating the influence mechanism of temperature versus frequency. Furthermore, considering the difference in these features' importance, two feature weighting strategies, including the Pearson's correlation coefficient (PCC)-based and particle swarm optimization (PSO)-based, are proposed to assign the different weights to the five features. Finally, the weighted features are fed into the ESN model to implement the dynamic frequency offset estimation. The application results on the real experiment datasets demonstrate that the presented FWESN method can estimate the frequency offset more precisely than the basic ESN method.
Elasticity in the cloud related to database autonomies and scalability

Cloud computing has been a very popular paradigm for implementing online applications. Scalability, elasticity, cost of use, and large-scale economies are the main reasons for the effective and widespread acceptance of cloud computing. In this paper, we outline our work to inject the aforementioned "cloud capabilities" into a database system designed to support various applications deployed in the cloud: designing scalable databases using autonomies database and elasticity that enables lightweight resiliency using low-cost live database migrations and an intelligent and autonomous controller designed for system management without human intervention.
Global Multi-scale Fog Salient Object Detection Based on Local Attention Feature

In foggy conditions, the target in the image is affected by fuzzy edge and low resolution, which limits the accuracy of target detection. In order to improve the detection accuracy of salient targets in foggy conditions, a detection algorithm with two-path global multi-scale and local attention features was proposed. Firstly, the image is defogged. Then the network model is divided into two branches after passing through the backbone network to extract the local and global features of the image respectively. At the same time, attention mechanism is introduced to enhance local features. Finally, local and global features at different scales are integrated to obtain more expressive output features. The proposed algorithm is tested on three data sets of PASCAL-S, DUT-OMRON and SOD. It was found that the maximum values of F-measure were 0.901, 0.832 and 0.896, and MAE were 0.061, 0.043 and 0.073, respectively. The results show that the proposed algorithm performs well in foggy weather compared with existing algorithms, which verifies the effectiveness of the proposed algorithm.
A Breadth-First Search Algorithm Accelerator Based on CSCI Graph Data Format

Breadth first search (BFS) algorithm is a building block of many graph applications; The sparsity of graph data and power-law distribution restrict the performance improvement of graph applications. In order to improve the access efficiency of data in graph computing accelerator, a variety of graph data formats have been proposed successively. However, the current scheme is complex, and the compressed data often needs to be decompressed to perform subsequent processing. In order to solve this problem, starting with the graph data organization format, this paper proposes a new data compression format compressed sparse columns independently(CSCI), and designs a BFS accelerator based on CSCI to improve the performance of graph computing accelerator and weaken the influence of sparsity. In this paper, the proposed accelerator circuit is tested on a prototype system based on XCVU9P-L2FLGA2104 using SNAP datasets. The experimental results show that the proposed BFS accelerator can reduce the resource utilization rate of LUT, FF and BRAM by 55%, 98%, 98.2% at most. At the same time, compared with the classical GraphBIG and Gemini graph framework, the running time of BFS accelerator is reduced by 61% and 64%, and the power is reduced by 34% compared with GraphBIG.
The Research of Chain Model Based on CNN-TDNNF in Yulin Dialect Speech Recognition

As a local dialect in China, the Yulin dialect is relatively thinly studied in speech recognition, and the available corpus data is relatively small. In this paper, we collect local dialect speech and corpus, build a speech database, analyze its pronunciation characteristics, and build a dictionary corresponding to the dialect with dialect vowels and rhymes as the base element. Thus, the problem of low recognition performance of speech recognition system under dialects, accents, and low resource corpus is solved. Firstly, this paper uses velocity perturbation as a data enhancement scheme to increase the information contained in the input features during feature extraction. Secondly, CNN-TDNNF, capable of long time series training, is used in the model as a neural network combined with the N-gram language model. Finally, the experimental results show that the performance of this scheme is improved by 15.42% compared with the traditional dialect speech recognition system in a dialect environment with a low resource corpus.
Decentralized Storage Algorithm of Medical Courses on Cloud Platform in Colleges Under the Background of O2O Integration

With the development of computer and Internet technology, the current cloud computing platform can be more and more widely used, and the current cloud computing platform has begun to develop in the direction of decentralization, so it is necessary to study the implementation method of this technology. And use the configuration management function of the cloud computing platform to automate its management and transformation, and cooperate with the high-availability decentralized software architecture to achieve the first main design goal proposed at the beginning of this paper, that is, the self-management of the system. This paper proposes a cloud The realization method of the decentralized storage algorithm of the platform medical nursing course to meet the current and future development requirements of cloud computing.
Research on the Intelligent Framework of High-frequency Data Algorithms Assisting College Students' Legal Awareness Cultivation

In this paper, the CAViaR model based on high-frequency data modeling is extended to high frequency, and a mixed-frequency data sampling MV-CAViaR model combining high-frequency and low-frequency data is constructed. This paper puts forward several ways and methods to strengthen the cultivation of college students' legal awareness, including: creating a good social environment that the whole society pays attention to; strengthening and improving the legal education in schools and enhancing the subjective initiative of college students, so as to promote quality education in colleges and universities and the process of improving the legal quality of citizens, promote the creation of a good social environment under the rule of law, and accelerate the construction of a socialist harmonious society.
Social Media Use and Service Delivery by Professional Librarians in Ahmadu Bello University Library, Zaria

This study investigated the extent to which social media is used to improve library services in tertiary institutions in Ahmadu Bello University Library, Zaria. The survey research design was used for the study. The population of the study consisted of all 204 professional librarians in Ahmadu Bello University Library, Zaria. Total enumeration was used for the study. A self-structured questionnaire was used for data collection. Findings revealed that Facebook (95.1%) and WhatsApp (94.2%) are commonly used by librarians followed by respondents who also indicated that YouTube (83%), Twitter (85.3%), Google+ (83.3%) and LinkedIn (71%) are moderately used in the library. Also, the services provided by librarians were e-reference services, database search, online user education and e-reader services among other available library services. This indicated that the provision of library services was moderately low in the library while findings revealed that available social media tools were not fully utilised by librarians in Ahmadu Bello University Library, Zaria. The study concluded that social media use helps librarian on service delivery of librarians in Ahmadu Bello University Library, Zaria. The study recommended that professional librarians should promote e-library services such as e-current awareness services, sele ctive dissemination of information, instant messaging services and distance learning services using diverse social media tools such as Pinterest, Ning, Flicker, Del.icio.us, Instagram and Social Bookmarking that were sparingly used in the library.
Understanding stakeholders' intention to follow National Education Policy 2020 using the theory of planned behaviour

The National Education Policy (NEP) was introduced in the year 2020 after vigorous research and taking into consideration of all the views of the stakeholders to ensure that all the aspects are covered and made inclusive. Despite the research and the steps taken to ensure inclusiveness, the acceptance of change is always difficult. Hence, there is a great need to analyse and understand the stakeholder's behavioural intentions to follow NEP 2020 for successful implementation. Hence, the present study aims to understand the stakeholder's intention to follow National Education Policy 2020 using the theory of planned behaviour (TPB). This study is based on the data collected using convenience sampling from an online survey. The results of the structural equation modelling indicate that attitude, subjective norm, and perceived behavioural control positively influence the intention to follow NEP 2020. This study is the first in the Indian context to explore intention to follow NEP 2020 using TPB.
Asessmemt of the Profesional Competencies of Students Majoring in Early Childhood Education at Some Universities in Vietnam Today

In the process of a fundamental and comprehensive renovation of education and training today, the task of properly assessing the status of students' professional competence is significantly important in the process of teaching and learning at schools. The research article presents the competency requirements and analyzes and assesses the current situation of professional competence of students majoring in Early Childhood Education. From the research results, several causes and limitations are pointed out, and some measures are proposed to improve learning results and teaching quality.
Particle identification in particle tracking velocimetry using two-stage neural networks

In this paper, a new sub-pixel intensive particle image identification method used for particle tracking velocimetry (PTV), is discussed. With aiming to improve the performance of accuracy and robustness, a two-stage deep learning framework consisting of two independent convolutional neural networks (CNN) is proposed in this method. The first neural network is to segment the particle blobs from the image, and the second one is to locate the position of the segmented particles at the sub-pixel level. A synthetic dataset containing particle images and the ground-truth positions is generated for network training. The effect of different characteristic parameters (e.g., the particle conditions; the tuning of the loss function; the noise level) is evaluated on synthetic images. To further verify the generalization capabilities of the technique, we also apply the proposed network to real-world images. Both synthetic and real-world experiment results strongly demonstrate that the proposed method possesses better accuracy and robustness than other conventional methods.
Surveillance Video Defogging Algorithm Optimized by Background Extraction

To reduce the flicker artifacts caused by video defogging, a surveillance video defogging algorithm based on the background extraction and consistent constraints is proposed. First, an inter frame consistency constraint is constructed and applied to background modeling. Second, the extracted background is defogged with an improved static defogging approach. Third, the foreground is extracted using the extracted background and further defogged using constraints of the consistency between the foreground and background. Experimental results show that our algorithm can remove fog effectively and preserve the temporal coherence well.
The topical issues of modern agricultural education

The article presents the results of a scientific and theoretical experience of educational technologies in solving urgent problems of modern agricultural education on the basis of the State University of Land Use Planning at the implementation of the Federal State Educational Standard of Higher Education.