Reviewerly/exHarmony huggingface dataset:
An Investigation into Sichuan Province's Technological Innovation Talent Development Environment Using the AHP-FCE Methodology

The role of technology talent in driving technological innovation and sustainable development cannot be overstated. This paper aims to delve into the development environment of technology innovation talent, utilizing the AHP-FCE method for assessment. By analyzing Sichuan Province as a case study, it uncovers the overall trends and achievements in the development environment of technology innovation talent, with a particular emphasis on the pivotal roles of technological innovation conditions and the employment and entrepreneurship environment. To optimize the development environment for technology innovation talent, this study puts forth policy recommendations including strengthening ecological awareness, enhancing mechanisms for talent cultivation and attraction, fostering industry-academia-research collaboration, and bolstering policy support. Through this research, valuable insights are provided to drive technological innovation in Sichuan Province, stimulate economic advancement, and promote sustainable development.
Development of Professional Foreign Language Competence of Economics Students with MOOCs during the Pandemic

Delving into the realm of massive open online courses (MOOCs), this investigation scrutinizes their role in enriching the professional language proficiency of economics undergraduates amidst a global coronavirus crisis. The research pivots on assessing the efficacy of MOOCs within higher learning curriculums while shedding light on the benefits and drawbacks of such an approach. The methodologies employed encompassed the analysis and interpretation of data derived from student polls and statistical analysis of yielded outcomes. The findings shed light on MOOCs as a potent instrument for honing language prowess and enriching the professional linguistic expertise of budding economists. The language experiment was participated in by a group of 34 students and their responses have been collated and interpreted as proposed guidelines for future reference. The participants notably appreciated the video-lecture format and exposure to native speakers’ elocution. However, the study did not shy away from spotlighting certain limitations such as complexities in platform navigation and the difficulties of watching videos without subtitles. To optimize MOOC utilization, a series of recommendations were drawn up. These included offering choices to students, simplifying platform navigation, extending support during challenges, and bolstering speaking abilities. This exploration holds valuable insights not just for educators and students but also for academic institutions at large, offering hands-on data regarding the perks and pitfalls of employing MOOCs to cultivate professional foreign language competence within the field of economic education.
A Corpus-driven Study of the Ecological Discourse Analysis of Energy Narrative in News: The New York Times as Example

The reductions in economic activities and mobility during the COVID-19 pandemic heavily challenged the global energy sector, due to which an energy crisis began to unfold in 2021. Under this social background, the linguistics community pay more and more attention to energy narrative in discourse. Based on Stibbe's definition and Fairclough's three-dimensional model, this study combined quantitative and qualitative methods to analyze the ecological discourses of the New York Times reported during the energy crisis. This study found that the selected news discourses contain ecologically destructive elements and use the "Erasure of causality" strategy more frequently. Ecological discourse analysis of news discourse is conducive to deconstructing ecological destructive discourse and helping the public to further distinguish misleading and deceptive energy reports.
Deformable element-wise dynamic convolution

The shape and values of a typical static convolution kernel remain fixed once the network is trained. Recently, dynamic convolutions were proposed to change the kernel’s values depending on the input during the test phase. We aim to extend the concept of dynamic convolutions by introducing an element-wise dynamic convolution approach. This method enables adaptive changes in kernel values for each output data element. Furthermore, a deformable element-wise dynamic convolution is proposed to enable simultaneous changes in kernel shape and value. The proposed deformable dynamic convolution is compatible with the static convolution in terms of input–output relationships. The capability of existing network architectures can be enhanced by replacing the static convolution with the suggested deformable dynamic convolution. Extensive experiments demonstrate that the proposed deformable dynamic convolution can improve the network performance in various computer vision tasks, including image classification and semantic segmentation.
Can Bitcoin be used for Zakat Payment?

Bitcoin (BTC) was developed as a digital currency to facilitate online payment transactions between one party or individual and another without using a third party. However, the use of BTC can also be misused for money laundering, fraud in investment schemes, engaging in cyber-crimes, funding activities, etc. Additionally, the highly complex method of calculating BTC makes BTC irrelevant as a measure of value. Although there are inherent advantages and disadvantages to using BTC, it can be subject to zakat. This study explores how zakat institutions can receive zakat in Bitcoin (BTC) transactions. BTC has both advantages and disadvantages, including potential misuse for illegal activities and vulnerability to security breaches. The study employs a qualitative method, including interviews with zakat institutions and BTC practitioners, and document analysis. The aim is to create a model for zakat institutions to receive donations in BTC, contributing to the management of zakat and adapting to technological changes. The study's findings can help prepare a strategy for receiving zakat in BTC and addressing potential challenges. By bridging the literature gap, the study offers new insights and perspectives on bitcoin as a zakat payment asset. Its findings can be valuable for policymakers, religious scholars, and practitioners in the realm of Islamic finance and zakat management. Ultimately, the research seeks to improve the early preparation and strategizing of zakat institutions in the face of evolving donation methods.
Lightweight accurate trigger to reduce power consumption in sensor-based continuous human activity recognition

Wearable devices have become increasingly popular in recent years, and they offer a great opportunity for sensor-based continuous human activity recognition in real-world scenarios. However, one of the major challenges is their limited battery life. In this study, we propose an energy-aware human activity recognition framework for wearable devices based on a lightweight accurate trigger. The trigger acts as a binary classifier capable of recognizing, with maximum accuracy, the presence or absence of one of the interesting activities in the real-time input signal and it is responsible for starting the energy-intensive classification procedure only when needed. The measurement results conducted on a real wearable device show that the proposed approach can reduce energy consumption by up to 95% in realistic case studies, with a cost of performance deterioration of at most 1% or 2% compared to the traditional energy-intensive classification strategy.
The Most Influential Factors On Stock Prices In The JII Index

This study determined the impact of changes in the Jakarta Islamic Index's Consumer Goods sector's Earnings Per Share, Return on Equity, and Gross Domestic Product from 2018 to 2020. This work uses panel data regression analysis and quantitative research techniques with secondary data. A purposive sampling method, or sample selection strategy based on several variables, was employed to choose the sample of firms in the consumer goods sector represented in the JII index. The F test results indicate that from 2018 to 2020, GDP, EPS, and ROE can impact the share price of JII issuers. According to the T-test, only EPS may have an enormous positive impact on stock prices, whereas ROE has a significant negative effect. GDP demonstrates that from 2018 to 2020, firms' stock prices in the consumer goods sector listed on JII are unaffected by GDP.
Systems' Librarians: Unidentified but Essential in Nepalese Libraries

Digital library dealt with information communication technology (ICT) infrastructures that is different from manual library infrastructure. Digital library is the growing demand in contrast to manual library, which demanded for systems' librarians or digital librarians. The collected data from seven libraries in Nepal to examine the availability of Systems' librarian manpower provided no light of hope. Those libraries were selected using convenient judgmental sampling through a short interview with currently available librarians about the concept of a Systems' librarian. Digitally skilled and responsible for digital data management personnel was defined as systems' librarians who have the necessary knowledge and skills in library software, networking, data entry, and troubleshooting of ICT-related issues. The study found that out of the seven libraries, only two had systems' librarians, yet their designation was not pin-pointedly named as' systems' librarian'. While others lacked the required manpower. The study also revealed that there were no Systems' Librarians in any of the libraries. The respondents' attitudes and perceptions towards the Systems' Librarian concept varied, with some accepting it as adaptable, acceptable, and appreciable. The study recommended for the recruitment of Systems' Librarians in Nepal as the library’s resources adopted the concept of paperless and library-without-wall. The lack of concerned manpower to look after electronic resources could lead to serious problems, and it's time for the Nepal Library Association and stake holders to advocate for the specialized manpower for the post of Systems' Librarian (also called digital librarian) and generate skilled manpower.
Ordinal compositional data and time series

There are several real applications where the categories behind compositional data (CoDa) exhibit a natural order, which, however, is not accounted for by existing CoDa methods. For various application areas, it is demonstrated that appropriately developed methods for ordinal CoDa provide valuable additional insights and are, thus, recommended to complement existing CoDa methods. The potential benefits are demonstrated for the (visual) descriptive analysis of ordinal CoDa, for statistical inference based on CoDa samples, for the monitoring of CoDa processes by means of control charts, and for the analysis and modelling of compositional time series. The novel methods are illustrated by a couple of real-world data examples.
Producing green users: environmental protection practice in a platform society

ABSTRACTIn a platform society, the realization of the public value of platforms is not only affected by goals constructed based on national interest but also closely related to the rights and interests of platform users. In this context, how a platform mediates between the interests of a country, its users, and its businesses becomes a topic worthy of discussion. Through participatory observation and in-depth interviews, this study reveals that online platforms strategically transfer public values that enjoy national policy support and have a broad popular base into their ecosystems, employing techniques such as bridging different parties, gamification management, and the masking effect to produce green users. The findings reveal the unique logic of contemporary China’s platform-driven environmental protection practices and mainstream value cultivation while also highlighting the conflicts and contradictions inherent in realizing public values. This study provides a valuable perspective for understanding the Chinese platform society in terms of public value realization.KEYWORDS: Platform societyplatform governanceenvironmental protectionthe green userpublic value Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 23NJYH10).Notes on contributorsQing YanQing Yan (PhD, Sichuan University, 2013) is a professor in the School of Journalism and Communication at Jinan University. His research focuses on entertainment and media culture especially in the context of new media. He is interested in examining fan cultures using the grounded theory approach and mixed methods.[yanqing2008@163.com].Hanbo HouHanbo Hou is a PhD candidate in the School of Journalism and Communication at Jinan University in China, and she focuses on platform economy.[hhb19480912@163.com].Meiling DuMeiling Du is a postgraduate student in the School of Journalism and Communication at Jinan University in China, and she focuses on media culture. [email:calledmeiling@gmail.com].Fan YangFan Yang (Ph.D., Pennsylvania State University) is an assistant professor in the Zimmerman School of Advertising and Mass Communications at the University of South Florida. Her research focuses on human and AI interactions. She is interested in examining psychological effects of new communication technologies on decision making using methods such as experiment, big-data analysis, and meta-data analysis. [yangfan6908@gmail.com]
Lax pullback complements in partial morphism categories

AbstractThe goal of this article is to characterize the lax pullback complement of a given partial morphism along a total morphism, in an -partial morphism category, where is an exponentiable stable system. To achieve this, we first show that if a lax pullback complement of a partial morphism exists in the partial morphism category, then a lax pullback complement of its total part exists in the base category. For the converse, we consider two cases. In the first case the base category is assumed to be adhesive and the morphism along which we take the lax pullback complement is admissible (i.e., its pushout along a monomorphism forms a pullback complement square), while in the second case the base category is -cohesive, however the morphism along which we take the lax pullback complement is an arbitrary total morphism. Finally as a byproduct we provide the connection between exponentials in the partial morphism category and its base category.Mathematics Subject Classification (2010): 18A3018B1018B2518B50Key words: (Distributivity) pullback(lax) pullback complement(couniversal, exponentiable, admissible) morphism(adhesive, cohesive, extensive, partial morphism, polynomial) categorygraph rewriting
Neuropsychological Considerations of Communication Competence in Prospective Educators

The article presents a theoretical analysis of the psychological and psycholinguistic literature on the research problem, substantiates scientific provisions regarding the understanding of communicative competence by future teachers as an integral quality of the individual, which synthesizes the general culture of communication and its specific manifestations in further pedagogical activity. The results of the diagnosis of the peculiarities of the understanding of the concept of "communicative competence" by future teachers of preschool education institutions are described.&#x0D; The purpose of the article is a psycholinguistic analysis of the understanding of the concept of "communicative competence" by future teachers of preschool education institutions by using this concept as a stimulus in a chain associative experiment.&#x0D; The research uses theoretical methods (analysis, synthesis, generalization, theoretical modeling) and diagnostic methods (observation, survey, questionnaire, free associative experiment, which serves as a source of accumulation of empirical data regarding students' understanding of the concept of communicative competence at various levels of professional training).&#x0D; The results not exhaust all possibilities of the research, however, the use of the concept of "communicative competence of the future educator" as a stimulus phrase in a chain associative experiment was determined, different reactions of the associative were recorded in students majoring in preschool education of different courses. It was established that for senior year students, the leading lever in understanding the concept of "communicative competence" is the motivational-value and reflexive-perceptive aspects, and for second-year students - the conceptual component. Such differences in the understanding of concepts are explained by the different amount of previous experience acquired by students in the process of professional training.
Membership inference attack on differentially private block coordinate descent

The extraordinary success of deep learning is made possible due to the availability of crowd-sourced large-scale training datasets. Mostly, these datasets contain personal and confidential information, thus, have great potential of being misused, raising privacy concerns. Consequently, privacy-preserving deep learning has become a primary research interest nowadays. One of the prominent approaches adopted to prevent the leakage of sensitive information about the training data is by implementing differential privacy during training for their differentially private training, which aims to preserve the privacy of deep learning models. Though these models are claimed to be a safeguard against privacy attacks targeting sensitive information, however, least amount of work is found in the literature to practically evaluate their capability by performing a sophisticated attack model on them. Recently, DP-BCD is proposed as an alternative to state-of-the-art DP-SGD, to preserve the privacy of deep-learning models, having low privacy cost and fast convergence speed with highly accurate prediction results. To check its practical capability, in this article, we analytically evaluate the impact of a sophisticated privacy attack called the membership inference attack against it in both black box as well as white box settings. More precisely, we inspect how much information can be inferred from a differentially private deep model's training data. We evaluate our experiments on benchmark datasets using AUC, attacker advantage, precision, recall, and F1-score performance metrics. The experimental results exhibit that DP-BCD keeps its promise to preserve privacy against strong adversaries while providing acceptable model utility compared to state-of-the-art techniques.
Application Research of Interior Design Style Migration from the Perspective of Artificial Intelligence

This article utilizes KuJiale application software as a case study to investigate the integration of artificial intelligence (AI) technology within the realm of environmental design. Employing style transfer algorithms, this study translates elements of flat design, elevations, and color compositions derived from exemplary design instances into interior design proposals tailored to user specifications, yielding distinctive artistic outcomes. The research undertaken herein substantiates the viability and scientific validity of implementing artificial intelligence style transfer algorithms in the realm of interior design. Furthermore, it furnishes novel insights and exemplars for the advancement of interior design endeavors.
Lung Nodule Data Enhancement Algorithm Based on Generative Adversarial Network

Due to the strong privacy of data and the difficulty of annotation, the amount of medical image data is relatively small, which further affects the effect of deep learning. Using data enhancement technology to expand existing data sets can significantly alleviate the problem of insufficient data. Deep Convolutional generative adversarial Network (DCGAN) technology has been widely used in the field of medical image data enhancement, but there are still some problems such as unstable training, difficulty in convergence, easy to produce mode collapse and insufficient quality of the generated images. In this paper, the mode collapse problem of deep convolutional generative adversarial network (DCGAN) is improved by using the fusion attention mechanism and modifying the loss function. Experiments show that the method proposed in this paper can effectively improve the enhancement effect of lung nodule image data.
Fully-attentive iterative networks for region-based controllable image and video captioning

Controllable image captioning has recently gained attention as a way to increase the diversity and the applicability to real-world scenarios of image captioning algorithms. In this task, a captioner is conditioned on an external control signal, which needs to be followed during the generation of the caption. We aim to overcome the limitations of current controllable captioning methods by proposing a fully-attentive and iterative network that can generate grounded and controllable captions from a control signal given as a sequence of visual regions from the image. Our architecture is based on a set of novel attention operators, which take into account the hierarchical nature of the control signal, and is endowed with a decoder which explicitly focuses on each part of the control signal. We demonstrate the effectiveness of the proposed approach by conducting experiments on three datasets, where our model surpasses the performances of previous methods and achieves a new state of the art on both image and video controllable captioning.
Quantum algorithm for estimating largest eigenvalues

Scientific computation involving numerical methods relies heavily on the manipulation of large matrices, including solving linear equations and finding eigenvalues and eigenvectors. Quantum algorithms have been developed to advance these computational tasks, and some have been shown to provide substantial speedup, such as factoring a large integer and solving linear equations. In this work, we leverage the techniques used in the Harrow-Hassidim-Llyod (HHL) algorithm for linear systems, the classical power, and the Krylov subsapce method to devise a simple quantum algorithm for estimating the largest eigenvalues in magnitude of a Hermitian matrix. Our quantum algorithm offers significant speedup with respect to the size of a given matrix over classical algorithms that solve the same problem.
A secure and lightweight batch authentication scheme for Internet of Drones environment

Internet of Drones (IoD) ushered in a rapid growth period and is gradually expanded from military field to civilian field. Currently, there are more and more scenarios where multiple drones are needed to complete a task together. Usually, identity authentication is required to ensure the execution of the task. The existing authentication schemes are basically aimed at the authentication of a single drone. For the multiple-drones scenario, the authentication protocol needs to be executed one by one for all the participating drones, which is extremely inefficient and will directly affect the efficiency of task execution. Therefore, we propose a novel lightweight authentication scheme, which supports users' batch authentication of multiple drones to ensure high efficiency of authentication. In our scheme, we only use hash function and point multiplication to complete batch authentication. In addition, Physical Unclonable Functions (PUFs) are introduced into our scheme to resist physical capture attack. Furthermore, our formal and informal security analyses reveal that our scheme can withstand most known attacks. The experimental result shows that our scheme can provide better security and lower storage and computation overheads when authenticating multiple drones, as compared with existing similar schemes.