Reviewerly/exHarmony huggingface dataset:
Domain Ontology Development Methodology for Construction Contract

The objective of this research is to present a method for developing the domain ontology of construction contracts. Ontology is a promising tool to organize domain knowledge and improve knowledge sharing. In this article, we propose a mixed contract ontology development approach. We first define the root classes by expert knowledge; then collect various concepts to build the subclasses and their interrelations. Finally, we transfer the contract ontology into a knowledge graph based on the results to test the ability of the ontology in implementation. The method provides a reference for the creation of the construction contract ontologies and serves as a fundamental support for the development of construction contract knowledge graphs.
An Innovative Metric-based Clustering Approach for Increased Scalability and Dependency Elimination in Monolithic Legacy Systems

Scalability is one of the system’s characteristics highlighted in the recent literature, and it is directly related to issues that are encountered in state-of-the-practice technology. The scalability of a system is challenging because monolithic legacy systems are hard to scale due to the high level of component dependencies. To the best of our knowledge, there is no published work available that can identify the components from a monolithic legacy system in the context of dependent and independent components and scale them accordingly. The main contribution of this paper is the proposal of a novel approach for the exclusive identification of dependent and independent monolithic legacy system components. The proposed approach also helps to remove the dependency among components of monolithic legacy systems. As a result, it establishes a precise method that identifies all the components of an application and removes the dependency among components, helping to increase the scalability of the resulting application. This approach was validated by several experiments, and the key findings were the identification of dependent and independent components, the identification of relationships among components, and the identification of the abstract level architecture of the monolithic legacy system. In future work, the proposed method will be enhanced toward the recovery of the whole system’s architecture.
PERANCANGAN USER PERSONA DAN CUSTOMER JOURNEY MAP SEBAGAI REPRESENTASI PENGGUNA SISTEM REPOSITORY PERPUSTAKAAN UNIVERSITAS LAMPUNG

The University of Lampung (Unila) Library repository system which can be accessed in http://digilib.unila.ac.id is used to store all scientific papers of Unila students. On this repository system, users can not only upload, but also download scientific papers files for reading and using as a reference in their papers. The purpose of this study is to design user persona and customer journey map as a representation of the users of the Unila Library repository system. This study uses the strategy plane stage of the five planes framework. The strategy plane stage includes setting business goals, brand identity, segmentation, and research on users of the repository system. The data used as a basis for designing user persona and customer journey map is taken from the Google Analytic repository system and questionnaires distributed to repository system users. Data includes segmentation, acquisition and behaviour. The questionnaire data is related to user experience, responses and user satisfaction when using the Unila repository system. The results of this study are in the form of two user persona designs and a customer journey map.
Covid-19 Information-Seeking Behavior (Study on Pokja Kelurahan Sehat Karangbesuki's Committee)

The purpose of this study is to find out, explain, and analyze the information-seeking behavior of COVID-19 conducted by Kelompok Kerja (POKJA) Kelurahan Sehat Karangbesuki’s Committee. There are eight indicators used based on Ellis’s Information-seeking Behavior Model such as; starting, chaining, browsing, differentiating, monitoring, extracting, verifying, and ending. This study used a mixed approach (mixed method). The results of this study indicate that the information-seeking behavior at the starting, chaining, and ending stages of the Kelompok Kerja (POKJA) Kelurahan Sehat Karangbesuki’s Committee is carrying out well. Furthermore, at the stage of browsing, differentiating, monitoring, extracting, and verifying, it is quite good. Middle-age is the age demographics majority of Kelompok Kerja (POKJA) Kelurahan Sehat Karangbesuki contribute the results of information-seeking behavior. This study shows how well Ellis’s Information-seeking Behavior Model analyzes information-seeking behavior and guides people to get information as they need.
An improved graphical convolutional network model based on interactive aspect-level sentiment analysis

Aspect-level text sentiment analysis is a more complete and specific task of text sentiment analysis, which determines the sentiment information expressed by a text with respect to each aspect. In recent years, the use of syntactic dependency trees as well as sentence structure information for aspect-level sentiment analysis has become more common, but they often ignore the role of sentence semantics involved in sentiment analysis and fail to combine them. In this paper, we improve and propose an interactive graph convolutional network (SInterGCN) model incorporating syntactic semantic structure for text-level sentiment analysis tasks. Specifically, a dependency graph is first constructed for each sentence in the syntactic relational dependency tree. This is followed by combining the graph of structural relations with the associated semantic graph, which is used to capture key aspect-oriented and contextual aspects. Furthermore, in order to interactively extract dependencies between aspect words and other aspects, an aspect-oriented GCN is used to model the representation learned by the aspect-oriented GCN based on the inter-aspect graph. Thus, the model can be aware of important contexts and aspect words when interactively learning aspect-specific sentiment features. Experimental results on four relevant benchmark datasets demonstrate that our proposed model outperforms the original interactive approach, achieving superior performance and validating the effectiveness of our model.
Software architecture evaluation model of the utility management system

We build the Utility Management System software architecture evaluation model for reducing the risk of software system. we got t the Utility Management System software architecture needs, Combined with the Quality Attribute Workshop method and the PALM method, from the main function, external cross-linking relationship and development trend, so as to build five evaluation dimensions of software architecture, including modularity, technology maturity, competition processing, independence, reliability. Then grading each evaluation dimension to get the Utility Management System software architecture evaluation model. The model can realize the risk identification of early development of the Utility Management System software system through the comprehensive evaluation of five dimensions of software architecture, so as to help improve the quality of software system and reduce the cost of the whole life cycle of the system.
Design of sports multimedia teaching platform based on machine learning

Due to the rapid development of multimedia technology, communication technology and information technology in China, scholars in the field of education gradually focus their research on new educational models, which are represented by educational technology and can use multimedia to present internal forms, such as physical education. Therefore, under this background, this paper deeply develops a kind of PE teaching platform based on multimedia network system by combining modern communication technology, multimedia technology, computer technology, network technology and other mature college campus networks. Firstly, this paper investigates the design of machine learning decision tree algorithm and random forest algorithm and discusses the application of machine learning in multimedia feature extraction and data processing; Then according to the actual needs, the structure of sports multimedia teaching platform is constructed, the relevant functions are analyzed, and the results of QoE multimedia experiment are integrated and tested. After the establishment of sports multimedia teaching platform, this paper uses this platform to practice, so as to analyze the teaching skills of physical education teachers in sports multimedia teaching platform, evaluate students' satisfaction and discuss the application of multimedia teaching platform in sports teaching and the performance and influencing factors of the system with students. The experimental results show that the sports multimedia teaching platform proposed in this paper has a certain role in promoting the improvement of college students' physical education curriculum performance, so it is worth popularizing.
Mobile robot motion obstacle avoidance control based on improved A* algorithm and artificial potential field algorithm combination

We propose a new obstacle avoidance method for mobile robots in unknown and dangerous environments. The method involves scanning the environment with a laser radar to obtain map data and building a grid map environment model, followed by initial path planning using an improved A* algorithm. To address the shortcomings of the A* algorithm, such as paths that are too close to obstacles and large turning angles, we propose combining the A* algorithm with an improved artificial potential field method. This method establishes a repulsive field between the robot and obstacles, ensuring that the robot's motion path stays away from obstacles. Simulation experiments have demonstrated that the obstacle avoidance mobile robot has excellent motion control capabilities.
Educational sovereignty of an engineer of new reality: Evolutionary activity approach

Introduction. Global competition, innovative development of the economy, the implementation of the state policy to ensure the country's technological sovereignty have necessitated a change in approaches to the university training of engineers. Purpose setting. The aim of the study is to develop a parametric system for constructing the productive activity of an engineer-creator, which provides intellectual and informational support for the process of creating a competitive actual product with desired properties and value. The main objective of the study is to substantiate and present the didactic basis of the educational and methodological support of the educational process in the implementation of the parametric system for constructing the productive activity of an engineer-creator. Methodology and methods of the study. The methodological basis of the research methods are the results of the analysis of Russian and foreign scientific literature and Internet sources, modern means of mathematical and parametric modeling. Results. The parametric system for constructing the productive activity of an engineer-creator ensures the creation of a specialist»s personal knowledge in the process of constructing an actual product based on the actualization and aggregation of demanded connections of activity areas. The parameterization of the constructing process implements the correlation modeling of the information transformation process (the basis of the didactics of creation) between the directions of activity. The basics of creation and the main components of the educational and methodological support of the educational process of training engineers-creators, as part of a universal educational system that performs the function of navigation in a digital educational environment, are presented. The modular structure of the transdiscipline «Constructing the activity of an engineer» has been developed. Conclusion. The results of the study confirm the expediency and possibility of training engineers of a new reality, capable of personal and collective evolutionary productive activity in the process of deliberate creating a competitive product commensurate with the present and future in any area of interest regardless of external challenges.
“I Feel Like I’m in a Box”: Contrasting Virtual Reality “Imaginaries” in the Context of Academic Innovation Labs

ABSTRACTAs immersive technology grows in popularity, universities are developing academic innovation labs (AIL) that often introduce students to virtual reality (VR) and other emerging cross reality applications. Although these labs help educate students on emerging technology, a more critical eye is needed to examine user experience (UX). This article reports on a qualitative, multimethod study that employed a talk-aloud UX protocol to gather data on VR users’ experience at the University of Connecticut’s OPIM Research Lab. To fully define and contrast this data, we juxtapose these individual narratives with rhetorical analysis of marketing discourse, as presented by VR platform HTC Vive, Google’s VR application Tilt Brush, and the Research Lab’s promotional material. Based on our findings, we assert that sociotechnical imaginaries as constructed by promotional material often reduce the complexities of immersion in user experience. Such marketing rhetoric creates “top-down” imaginaries that contrast with “bottom-up” imaginaries generated in user experience, reinforcing the complex and fluid definitions of immersion. The resulting study has practical implications for stakeholders across higher education, especially in the context of innovation labs, as well as for technical and professional communication educators and practitioners.KEYWORDS: Immersive technologyinnovation labsvirtual realityimmersionuser experienceemerging technologyfuture imaginariessociotechnical imaginaries Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsBrent LuciaBrent Lucia is an Assistant Professor In-Residence at the University of Connecticut School of Business. He has a PhD in Composition and Applied Linguistics from Indiana University of Pennsylvania. His current research explores the rhetorics of technology and its relationship to the production of space. His recent scholarship can be found in Rhetoric Review, Business and Professional Communication Quarterly, and Enculturation.Matthew A. VetterMatthew A. Vetter is an Associate Professor of English and affiliate faculty in the Composition and Applied Linguistics PhD program at Indiana University of Pennsylvania. His research, which asks questions related to technology, rhetoric, and writing, has been published widely in venues such as Social Media and Society, Rhetoric Review, Studies in Higher Education, and Computers and Composition. His co-authored book, Wikipedia and the Representation of Reality, was published by Routledge in 2021.David A. SolbergDavid A. Solberg is a teaching assistant at the Holy Family Institute in Pittsburgh, Pennsylvania. He received his MA degree in TESOL (Teaching English to Speakers of Other Languages) from Indiana University of Pennsylvania. His master’s thesis was entitled The Use of Parallelism in Poetry Writing for the Acquisition of English Grammar (available from ProQuest).
Navigational and thematic exploration–exploitation trade-offs during web search: effects of prior domain knowledge, search contexts and strategies on search outcome

ABSTRACTWhile a clear distinction is established between simple lookup tasks and complex exploratory tasks, few studies have documented the differences between complex lookup tasks and simple exploratory tasks. To address this empirical gap, which has implications for system adaptability, the present study focused on these differences and the impact of users' prior domain knowledge (low vs. high) on exploration-exploitation search strategies (thematic and navigational). The effects of prior domain knowledge on information search performance were also investigated, as well as the extent to which the navigational and thematic search strategies predicted users' search scores and learning outcomes. Sixty students (35 in psychology, 25 in computer science) were asked to perform two complex lookup tasks and two simple exploratory tasks within and outside their knowledge domain. Results showed that when users performed tasks in their knowledge domain, they formulated more specific queries, achieved higher search scores, and produced higher quality factual knowledge. Complex lookup tasks led to more thematic exploitation and more navigational exploration than simple exploratory tasks. The search strategies predicted output performances differently, depending on the search domain (in vs. out). In light of these results, solutions helping users to improve their search strategies are given.KEYWORDS: Search strategiesexploration-exploitation trade-offsprior domain knowledgelookup taskexploratory task AcknowledgementsThe authors also thank Claire Ibarboure for the double-annotation process and Mylène Sanchiz for her relevant statistical advice. This work was supported by the French National Research Agency (ANR) CoST project [grant number ANR-18-CE23-0016].Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 The dataset used from the resources paper (Dosso et al. Citation2021) is available at https://doi.org/10.6084/m9.figshare.152863532 Reliability (Cronbach's Alpha) for the two MCQs: Cognitive Psychology MCQs (α = .87); Computer Science MCQs (α = .84).
A learned pixel-by-pixel lossless image compression method with 59K parameters and parallel decoding

This paper considers lossless image compression and presents a learned compression system that can achieve state-of-the-art lossless compression performance but uses only 59K parameters, which is one or two order of magnitudes less than other learned systems proposed recently in the literature. The explored system is based on a learned pixel-by-pixel lossless image compression method, where each pixel’s probability distribution parameters are obtained by processing the pixel’s causal neighborhood (i.e. previously encoded/decoded pixels) with a simple neural network comprising 59K parameters. This causality causes the decoder to operate sequentially, i.e. the neural network has to be evaluated for each pixel sequentially, which increases decoding time significantly with common GPU software and hardware. To reduce the decoding time, parallel decoding algorithms are proposed and implemented. The obtained lossless image compression system is compared to traditional and learned systems in the literature in terms of compression performance, encoding-decoding times and computational complexity.
Block Chain Technology: Applications, Challenges, and Future Directions

Block chain technology has gained significant attention in recent years due to its potential to revolutionize various industries. This research paper explores the applications, challenges, and future directions of Block chain technology. It provides an overview of Block chain fundamentals, examines its real-world applications across different sectors, discusses the challenges and limitations associated with Block chain implementation, and explores future directions with a specific example
Inflectional classes without class features

Abstract In this paper, we propose a comprehensive account of the paradigms of Frisian verb-classes. Verb-classes in Frisian are an example of a more general phenomenon of inflectional classes that we encounter in many natural languages across the major word classes. Members of different inflectional classes show different paradigms. Traditionally, inflectional classes have been analyzed using class-features (see e.g., Marzi et al. 2020). However, such features suffer from being ad hoc devices that seem to have no other function in the grammar than to code this difference. In the present analysis we propose that the verb stems from different classes show a difference in size. Using phrasal spell-out, we will show that these stems differ in the amount of morpho-syntactic structure that they may realize, rendering class-features superfluous.
Improved Mersenne Twister random number generator based on FPGA

With the development of the field of scientific computing, the rapid generation of high-quality random numbers has become a crucial aspect of research. However, traditional methods are often insufficient in meeting the efficiency and quality requirements of random number generation. In this paper, we propose an improved version of the Mersenne Twister algorithm, implemented on the FPGA platform. By optimizing the characteristic polynomial in the Transform module, the randomness and uniformity of the Mersenne Twister (MT) algorithm are enhanced. The core modules of the algorithm are realized through a parallel structure one the FPGA, with the AXI4-Stream data bus utilized as the protocol for data interaction with peripherals. Finally, the quality of the random numbers by the designed generator is tested, and the results demonstrate that it can efficiently produce high-quality, uniformly distributed random numbers. These numbers are well-suited for applications that require the rapid generation of large quantities and high-quality random numbers, such as Monte Carlo simulations.
Research on photovoltaic power prediction method based on improved extreme learning machine

The power of photovoltaic generation fluctuates greatly under the influence of weather conditions, so accurate prediction is beneficial to the management and dispatching of power grid. In this paper, pearson correlation coefficient method is used to analyze the factors affecting the photovoltaic power generation. the features with high correlation are selected as input variables. A hybrid kernel extreme learning machine model optimized by particle swarm optimization algorithm is proposed. The parameters in the model are optimized. Based on the model, the data was divided into four seasons. The prediction was carried out. The results showed that the PSO-HKELM model had a high power prediction accuracy for different seasons, so as to realize the effective prediction of photovoltaic power generation.
Occluded pedestrian detection based on improved YOLOv5n

Aiming at the problem of pedestrian targets occlusion and multi-scale error and missed detection in pedestrian detection, a lightweight pedestrian detection algorithm based on improved EA-YOLOv5n is proposed. This method introduces the ECA attention module into the backbone feature extraction network, and learns the channels of pedestrian images by learning Information, improve the accuracy of pedestrian object detection in the case of occlusion, improve the calculation method of Bounding box loss function for the disadvantages of loss function calculation, adopt EIoU Loss and introduce power transformation to obtain higher bounding box regression accuracy. The experimental results show that using the improved model to conduct experiments on the Widerperson dataset reaches 69.6% mAP, which is 2.0% higher than the original algorithm, and the detection speed reaches 65FPS.