Reviewerly/exHarmony huggingface dataset:
Understanding Users’ Ask Intention on Paid Q&amp;A Platform from the Perspective of Impression Management

With the widespread fusion of social media and knowledge economy, paid Q&A platform has become one of the most prominent ways for people to seek knowledge online. More and more users enroll as answerers to answer questions for gaining extra money, so it is necessary for them to find out ways to attract more askers in this competitive market. When investigating influential factors on the ask intention, most previous research focuses on the objective data displayed on answerers' accounts such as number of followers and price per question, while largely ignores the information that was voluntarily disclosed by answerers. In this regard, we draw from impression management theory to explore the role of answerers' self-image which is built through active information disclosure in this Q&A process. We constructed a cross-sectional dataset with 9,887 answerers on Weibo Q&A platform to test hypotheses. Using zero-inflated negative binomial model, we find that the congruence between answerers' self-image and their provided knowledge has a significant positive impact on knowledge seekers' ask intention. Particularly, the larger the congruence between those actively disclosed text information (i.e., biography) and picture information (i.e., avatar) and provided knowledge, the higher the askers' willingness to ask questions. Managerial implications on paid Q&A platforms are thus provided.
A Method for Recommending Resources Across Virtual Academic Communities Based on Knowledge Graph and Prompt Learning

In the era of big data, virtual academic communities are flourishing and resources are growing explosively. As a result, heterogeneous fragmentation of resources and massive disorder have created constraining problems, which exacerbate the "knowledge island" effect among academic communities and challenge researchers to acquire knowledge effectively. To solve these problems, we propose a method for recommending resources across virtual academic communities (MRRVAC) based on knowledge graph and prompt learning. Firstly, we use the knowledge graph to link resources in different communities, which enables resources to be transferred between communities. Secondly, prompt learning is used to acquire the potential knowledge of knowledge graph. The final recommendation list of academic resources is obtained by training the prompt template with the improved P-tuning method and using it to mine the injected knowledge in the model. Finally, data experiments were conducted on the datasets of two virtual academic communities, Zhihu and ScienceNet. The results show that the average improvement over the original method in HR and NDCG is 0.296% and 0.271%, which validates the effectiveness of the method.
A Case Study of Collaborative Learning Within a Digitalization Learning Environment

The Covid-19 epidemic ushered in a new era of hybrid learning. After the Covid-19 pandemic, information technology (IT) acceptance is no longer a problem. And given that it is the "postdigital", technology-related constructs should be updated and devoted to creating a lifelong agile learning environment (LE). The research question is how to manage collaborative learning (CL) activities through online integrated platforms to better serve outcome-based education (OBE). This research reviews the digital transformation advancement in learning management systems and points out that the digitalization LE breeds postdigital learning, which is based on IT "affordance" and well organization and distribution of course resources. When led by OBE, this paper employs qualitative synthesis research and to foster personal development summarizes the appropriate method for managing CL activities as student-centered lifelong learning, organization of role-based activities, and all-around assessment. Finally, the findings are a conceptual framework for student-centered learning within digitalization LE and an illustration of a curriculum with a customized Feishu of ByteDance performing CL. The contribution involves learning in less structured environments, such as an advanced enterprise collaboration and management platform, which prompts creative and innovative approaches to uncertainty and change. As a result, the practical implication facilitates students to find both their will and their own creative and exploratory ways of lifelong learning.
The Impact of Blockchain on the Credit Risk of Supply Chain Finance: A Tripartite Evolutionary Game Analysis

In light of the reality that supply chain finance is plagued by credit risk, this study constructs a tripartite evolutionary game model involving financial institution, small and medium-sized enterprise (SME), and core enterprise to analyze the credit risk in the case of accounts receivable factoring. The micro-mechanism of how blockchain mitigates the credit risk of supply chain finance is analyzed by comparing the changes in the system's evolutionary stability strategy before and after the introduction of blockchain. Numerical simulation is also conducted to verify the system's evolutionary stability strategy. The results reveal that whether the traditional supply chain finance business produces credit risk depends on the amount of accounts receivable, the income obtained by SME and core enterprise when maintaining the stability of the supply chain, and the default income and default cost of both. After the introduction of blockchain, a strict regulatory environment increases the default cost of enterprises in the supply chain. Therefore, the system strictly converges to the Pareto-optimal solution of financial institution accepting financing applications, core enterprise repayment, and SME compliance. In addition, the decrease in the amount of accounts receivable held by a single SME can accelerate the convergence of the tripartite evolutionary game to equilibrium after the introduction of blockchain. Thus, blockchain can effectively mitigate the credit risk that financial institutions face while conducting supply chain finance business. Our research provides theoretical support for optimizing credit risk management in supply chain finance using blockchain.
Bibliometric Analysis on the Research Hotspots of Recommender Systems

Research on the hot topics and future development trends of recommender system is of great significance for improving the accuracy of recommendation results and saving users' time. A total of 867 SCI and SSCI literatures related to recommender system were selected from the Web of Science database from 2013 to July 2022. The visual knowledge graph analysis tool CiteSpace was used to analyze the temporal and spatial distribution characteristics, knowledge basis, research hotspots and frontiers of personalized recommendation technology research from five dimensions: literature growth trend, regional distribution, literature co-citation relationship, keyword co-occurrence and emergence. The participation of Chinese and American researchers are much higher than that of other countries and the exchange and cooperation between countries need to be strengthened. The research focuses on the application of algorithm in recommender system, improvement of collaborative filtering algorithm, model construction, social network and neural network. The research frontiers include feature extraction of users and projects, machine learning and attention mechanism.
The small world's problem is everyone's problem, not a reason to favor CNT over probabilistic decision theory

The case for the superiority of Conviction Narrative Theory (CNT) over probabilistic approaches rests on selective employment of a double standard. The authors judge probabilistic approaches inadequate for failing to apply to "grand-world" decision problems, while they praise CNT for its treatment of "small-world" decision problems. When both approaches are held to the same standard, the comparative question is murkier.
Pedagogical conditions for the legal culture development of educators of the state employment service

The article proves that legal upbringing and legal education should be considered as the main factors in the formation of the legal responsibility of teachers, paying attention to the fact that they can both slow down and accelerate legal progress.This is a purposeful process of obtaining systematized legal knowledge by teachers, formation of abilities and skills to apply them in practice, development of the moral and intellectual sphere, ensuring certain legal education of the individual.It is specified that legal responsibility is a qualitative-quantitative characteristic, which includes information not only about the amount of legal knowledge, abilities and skills, about the number of mastered (or unmastered) its elements or types of relevant cultural values, but also information about its content, about the nature of spiritual values (or "anti-values"), about the social orientation of legal ideals, ideas, norms accepted by the individual, this is the culture of legal thinking, the culture of legal feelings, the culture of legal behavior.Pedagogical conditions for the formation of the legal culture of pedagogical workers were defined, to which we included: the presence of educational courses "Jurisprudence", "Constitutional law" as mandatory disciplines for teachers to study during advanced training courses; organization of public activities; Selfgovernment as an institution of civil society.The realization of selfgovernment by students is expressed in the fact that they participate in the development and implementation of local normative acts of the educational organization, make proposals to improve the educational process, resolve issues of individual students' education, etc.; inclusion in
Mobile assisted language learning of students: A qualitative meta-synthesis by ENTREQ statement

Mobile Assisted Language Learning (MALL) is an innovative approach to language learning that utilizes mobile devices, such as smartphones and tablets, to support language learning. MALL has become increasingly popular in recent years due to the widespread availability of mobile devices and the flexibility they offer for language learning. The aim of this research is to interpret and discuss the findings of qualitative research to understand how Mobile Assisted Language Learning (MALL) in English affects language learning through a systematic research method called meta-synthesis. As a result of this meta-synthesis research, academic, convenience, social, or cultural functions, and pedagogical functions codes were reached as the functions of using MALL in the classes from the teachers' perspectives. The advantages of MALL were identified as being portable and convenient, encouraging immediate feedback and self-feedback, improving language skills, increasing learner autonomy, providing a faster way of learning, releasing the pressure associated with traditional learning, increasing motivation, boosting self-confidence, and encouraging collaboration with other learners. The disadvantages were classified as leading to being easily distracted, being prone to disruptions due to technical problems such as internet connection problems, having challenges due to poor technology literacy and insufficient functions of mobile devices such as small screen size, being unable to study without an imposed structure or schedule, involving inauthentic feedback and self-assessment, having difficulty finding a satisfactory app, and having difficulty choosing the appropriate learning content.
Effectiveness of layered curriculum: A mixed-meta research synthesis

The mixed-meta analysis study aims to examine the effects of the layered curriculum on students based on the studies conducted between 2006 and 2021. The statistical data gathered from a total of five studies is indexed in national databases investigating the effect of layered curriculum on achievement and attitudes, and the overall effect of the layered curriculum on achievement is found to be moderate, while its effect on attitudes is found to be very large. In the qualitative part of this mixed-meta analysis, the meta-thematic analysis method is conducted through document review and content analysis of a total of nine studies, and codes are classified under the themes of cognitive domain, attitudinal dimension, and limitations. These themes are visualized in the Maxqda program and presented in the findings section in two models. Some of the codes in the cognitive domain are increased academic achievement, permanence, repetition, peer assessment, self-evaluation, creativity, and imagination. In the dimension of attitude, codes are collaborative learning, aroused excitement, desire, and curiosity towards the lesson, increased interest, increased inclass solidarity, and student motivation towards the lesson. Limitations such as the lack of focus in the classroom because of noise, the difficulty of students with no manual dexterity, the need for detailed planning, the difficulty of creating an original product, and the lack of time are turned into a second model on their own as a theme. Suggestions regarding time management, comprehension of schedules and tasks, class size, and planning are presented to address the difficulties encountered in the studies. The current study provides insights into the effects of a layered curriculum on achievement and learning attitudes.
Nonmonotonic inferences: Classical conclusions in an intuitionistic modal framework

Abstract We introduce a conceptual foundation for nonmonotonic reasoning which integrates an intuitionistic plurimodal logic with classical logic. The need for a multilogical system arises once we understand that to legitimately infer less than certain conclusions, we make two different kinds of assessments: an external one (what are the alternatives) and an internal one (which logic should govern the set of beliefs). We exploit this scheme to capture in novel ways basic nonmonotonic systems such as autoepistemic logic, default logic, one of the best known interpretations of negation-as-failure, as well as some conditional logics of normality. Among the aims of this paper is to explain why there are substantial overlaps between these systems and also to settle some problematic features that are of concern in logical investigations on common sense reasoning.
A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si68.svg" display="inline" id="d1e386"><mml:msup><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>DCT: A cubic acceleration TCP for data center networks

In recent years, online data-intensive (OLDI) applications have become particularly common in data center networks (DCNs), such as web search, advertisement systems, and distributed machine learning. OLDI applications have strict latency requirements for short flows and must operate under soft-real-time constraints (e.g., 300 ms latency). On the other hand, there exist long flows that are unaware of flow latency but are sensitive to throughput. Thus, a special transmission protocol that can satisfy different demands for both long flows and short flows is immediately needed. L2DCT is one of the most representative differentiated transmission protocols, which aim at the reduction in completion time for short flows. However, the performance of L2DCT becomes less significant in the scenario where short flows account for the majority (e.g., data mining). For the purposes of minimizing the flow completion time (FCT) of short flows, achieving the minimum average flow completion time (AFCT), and guaranteeing the deadline constraints upon flow transmission times, we propose a cubic acceleration TCP for DCNS, which is referred to as A3DCT. A3DCT employs the Shortest Remaining Process Time (SRPT) scheduling policy to adjust its congestion window according to the remaining bytes of the flow. Moreover, to improve the priority of short flows without affecting the throughput of long flows, A3DCT leverages a cubic function to indicate the urgency of short flows. Finally, A3DCT accelerates the recovery speed, in a flexible manner, depending on the urgency of different flows in a non-congestion state to request the bandwidth to finish transmission. We perform simulations of different scales by using NS-3 simulator to evaluate the performance of A3DCT. The evaluation results justify that A3DCT can not only achieve a low flow completion time, especially for short flows, but also guarantee the high throughput of long flows.
New near-mine prospecting approach using multivariate analysis and reflectance spectroscopy to define surface footprint: A case study of the Pequizão Gold Deposit, Crixás Greenstone Belt, Central Brazil

The Pequizão gold deposit in the Crixás greenstone belt, central Brazil, is structurally-controlled mineralization hosted mainly in carbonaceous phyllite with pervasive hydrothermalized zones and large amounts of disseminated sulfides and gold. Usually, regoliths of Au deposits are either unstudied or understudied in exploration surveys because of lower Au content and the difficulty and complexity of soil profiles. However, such investigation can be beneficial in brownfield exploration to assess larger areas than drilling. In this work, results of clustering and machine learning of soil geochemistry and reflectance spectroscopy integrated with a multivariate approach to determine soil footprints and to target new deposits in the surface of the Crixás greenstone belt. Reflectance spectroscopy was applied in 939 soil samples and is a valuable tool in mineral exploration for an immediate investigation of mineral assemblage from the target. It was calibrated with X-ray diffraction, geochemical, and multivariate approaches providing consistent vectors toward mineralization, with Pequizão soil samples developing gibbsite and phengitic-white mica as minerals related to mineralized samples. Multivariate analysis reveals that the deposit has a typical orogenic gold deposit chemical signature. Principal components and factor analysis first defined samples derived from carbonaceous phyllite and dolomite as the main ore hosts. The chemical aspects of hydrothermal alteration are As, Ag, Te, Ca, and Mg, with enrichment of Sb, V, Na, Ba, and W and depletion of Zn, Ga, and Pb that, according to ensemble learning, they have significant importance in the detection of gold. Understanding surface footprints of known deposits can be an exploratory guide for finding new soil geochemical halos related to mineralization zones. The research revealed new associations of minerals and chemical elements that can be determined as exploration vectors. It was possible to clarify further the knowledge about the footprint of mineralization in soil. Combining the soil-related methods applied in this study and a broad-coverage spectral approach in soil and drill cores can enhance success in prospecting in brownfield areas and expand to greenfields.
Improvement of the solar radiation prediction accuracy of a light environment simulation according to the positioning of an interpolation application during the process

Light environment is directly related to energy reduction and improving health and quality of life, and interest in it continues to increase. Therefore, in light-environment design, simulation tools are used to predict and evaluate the light environment early in the planning stage of a project. Consequently, scientific simulations are most effective from the early stages of sustainable design. However, choosing which simulation tools to use can be challenging, and the entry barriers to their use can be high; some tools are also more difficult to use. In addition, if there is no weather data available for the area to be analyzed, data from a nearby location is often used instead, which reduces the accuracy of the solar radiation prediction in the simulation. Therefore, this study aimed to reduce the difficulty in using environmental prediction simulation tools by applying adjusted Kriging. This study also attempted to determine where the interpolation method could be best applied in the solar radiation simulation process to produce better prediction results. The experimental results showed that the simulation values were more accurate when calculating the solar radiation by generating sky patches with the original typical meteorological year (TMY) data than by generating them with the interpolated TMY data. This interpolation position and method can increase a simulation's accuracy by creating new weather data in areas where no solar radiation data are available. BUILDIT, as a simulation tool for sustainable urban or building design solutions, applied these techniques, which helped the performance of sustainable designs in the early stages of projects.
The language of knowledge spaces in pre-topology1

The theory of knowledge spaces (KST) which is regarded as a mathematical framework for the assessment of knowledge and advices for further learning. Now the theory of knowledge spaces has many applications in education. From the topological point of view, we discuss the language of the theory of knowledge spaces by the axioms of separation and the accumulation points of pre-topology respectively, which establishes some relations between topological spaces and knowledge spaces; in particular, we show that the language of the regularity of pre-topology in knowledge spaces and give a characterization for knowledge spaces by inner fringe of knowledge states. Moreover, we study the relations of Alexandroff spaces and quasi ordinal spaces; then we give an application of the density of pre-topological spaces in primary items for knowledge spaces, which shows that one person in order to master an item, she or he must master some necessary items. In particular, we give a characterization of a skill multimap such that the delineated knowledge structure is a knowledge space, which gives an answer to a problem in [14] or [18] whenever each item with finitely many competencies; further, we give an algorithm to find the set of atom primary items for any finite knowledge space.
Language style and recognition of the answers in health Q&amp;A community: Moderating effects of medical terminology

The rise of social question and answer (Q&amp;A) platforms has changed the model of traditional health-information services. The quality of answers on a Q&amp;A platform is critical to attract users and increase their community engagement. Thus, recognition of the answer was used to measure users’ acceptance of the answer. A theoretical model of the impact of language style on community recognition of health questions was developed. Nearly 1330 answers from a health question from Zhihu were obtained to verify the model. Results showed that personality reliability, assertiveness, argumentation clarity, commitment, and reverse incentive of health information positively affected answer recognition, while argumentation structure negatively affected answer acceptance. Simultaneously, the degree of use of medical terminology has a significant moderating effect on the relationship between answer recognition and assertiveness, argumentation structure, argumentation clarity, and commitment. Introducing Aristotle’s rhetoric theory to language style and answer recognition could potentially develop healthy communities and disseminate health knowledge.