Reviewerly/exHarmony huggingface dataset:
A study on blockchain-based marketplace governance platform adoption: a multi-industry perspective

Purpose Decentralised autonomous organisations (DAOs) are internet-native self-governing enterprises where individual groups, communities, agencies, consumers and providers work together using blockchain-led smart contracts (SCs). This study aims to examine the role of DAO marketplaces in technology-led autonomous organisation design for enterprise technology sourcing industries, with algorithmic trust and governance. Design/methodology/approach The authors examined the importance of an enterprise marketplace governance platform for technology sourcing using DAO as a decentralised/democratised business model. A total of 98 DAO products/services are evaluated across 11 industries that envisage DAO as a strategic choice for the governance of decentralised marketplace platforms. Findings The research findings validate how a DAO-led enterprise marketplace governance platform can create a cohesive collaboration between consumers (enterprises) and providers (solution vendors) in a disintermediated way. The proposed novel layered solution for an autonomous governance-led enterprise marketplace promises algorithmic trust-led, self-governed tactical alternatives to a strategic plan. Research limitations/implications The research targets multiple industry outlooks to understand decentralised autonomous marketplace governance and develop the theoretical foundation for research and extensive corporate suitability. Practical implications The research underpinnings boost the entrepreneurs’ ability to realise the practical potential of DAO between multiple parties using SCs and tokenise the entire product and service offerings over immutable ledger technologies. Originality/value To the best of the authors’ knowledge, this research is unique and the first of its kind to study the multi-industry role of algorithmic trust and governance in enterprise technology sourcing marketplaces driven by 98 decentralised and consensus-based DAO products across 11 industries.
A cross-database bibliometric analysis of ubiquitous learning: Trends, influences, and future directions

The concept of ubiquitous learning has emerged as a pedagogical approach in response to the advancements made in mobile, wireless communication, and sensing technologies. The domain of ubiquitous learning is distinguished by swift progression, thereby presenting a difficulty in maintaining current knowledge of its developments. The implementation of bibliometric analysis would enable the tracking of its development and current status. The objective of the present investigation is to perform a thorough bibliometric examination of the domain of ubiquitous learning. This research aims to discern significant attributes, patterns, and influencers within the discipline by analyzing scholarly works. The primary objective of this study is to provide a comprehensive depiction of the salient characteristics and patterns exhibited by the datasets employed in ubiquitous learning research, namely Scopus, Web of Science (WoS), and merged datasets. Additionally, the study seeks to trace the historical development of publications in this domain and to ascertain the most noteworthy publications and authors that have exerted a significant impact on this field. This study provides an extensive bibliometric analysis of ubiquitous learning, examining output from Scopus, WoS, and a merged dataset. It highlights the field’s growth and the rising use of diverse data sources, with Scopus and the merged dataset revealing broader insights. The analysis reveals an interest peak in 2016 and a subsequent decline likely due to incomplete recent data. Documents, predominantly articles, differ across databases, underscoring the unique contributions of each. The study identifies “Lecture Notes in Computer Science” and “Ubiquitous Learning” as major research sources. It recognizes Hwang, G.-J. as a highly influential author, with Asian institutions leading in research output. However, Western institutions also show strong representation in WoS and merged databases. Despite variations in total citation counts, countries like China, Switzerland, and Ireland contribute significantly to the field. Terms like “mobile learning” and “life log” have vital roles in bridging research clusters, while thematic maps reveal evolving trends like mobile learning and learning analytics. The collaborative structure and key figures in ubiquitous learning are illuminated through network analysis, emphasizing the importance of cross-database analysis for a comprehensive view of the field.
Seeing the Unseen

In this demonstration, we show a simple, yet effective protocol that detects a wireless Man-in-the-middle (MitM). The proposals that address this classic problem of detecting the MitM in wireless networks typically use knowledge of the attack vectors, rely on fingerprinting methods, and are exclusive to the communication technology. The REVEAL protocol is not so restricted. It poses specific timing-based challenges that force the MiTM to reveal itself, be it half, full, or double full duplex. As a prerequisite, we show that a MitM cannot interfere with the underlying clock synchronization process without getting detected. We demonstrate the capability of the REVEAL protocol to detect MitMs in a 4G/5G network, for which we have built half-duplex, full-duplex, and double-full duplex MitM. Our designs of MitM and REVEAL protocol are publicly available.
Bayesian modeling of interaction between features in sparse multivariate count data with application to microbiome study

Many statistical methods have been developed for the analysis of microbial community profiles, but due to the complexity of typical microbiome measurements, inference of interactions between microbial features remains challenging. We develop a Bayesian zero-inflated rounded log-normal kernel method to model interaction between microbial features in a community using multivariate count data in the presence of covariates and excess zeros. The model carefully constructs the interaction structure by imposing joint sparsity on the covariance matrix of the kernel and obtains a reliable estimate of the structure with a small sample size. The model also includes zero inflation to account for excess zeros observed in data and infers differential abundance of microbial features associated with covariates through log-linear regression. We provide simulation studies and real data analysis examples to demonstrate the developed model. Comparison of the model to a simpler model and popular alternatives in simulation studies shows that, in addition to an added and important insight on the feature interaction, it yields superior parameter estimates and model fit in various settings.
Large-Scale Networked Visual Data: Current Research and Future Trends in Associative Inference and Semantic Comprehension

Aiming at the fact that most of the existing visual data comprehension deals with individual visual objects in isolation and focuses on their inherent characteristics, and pays insufficient attention to the characteristics of network heterogeneous distribution and interconnections, which leads to difficulties in solving low computational efficiency, and understanding of low-level semantics, etc., we elaborate on the hotspots of the research of associative inference and semantic comprehension of large-scale networked visual data, analyze the current status of the research at home and abroad, and look forward to the development trend in this direction. We analyze the current research situation at home and abroad and make an outlook on the development trend of this direction.
ツォンカパ著『如意牛』における根本タントラと釈タントラ

This study summarizes the statements on the root tantras and explanatory tantras in Tsong kha pa’s All Fulfilling Wishes, which is the most detailed commentary on Lūipa’s tradition of the Cakrasaṃvara. Tsong kha pa states that it has been traditionally believed that there are three root tantras: the longer, middle, and shorter. In addition, he mentions that among them, the shorter tantra （Laghusaṃvara） is the only true root tantra. Since the first and second tantras did not exist at the time of Tsong kha pa, in his All Fulfilling Wishes he only cites the Laghusaṃvara as the root tantra.
CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System

In this paper, we investigate both the energy consumption and running time of different CNN tasks on GPUs or CPUs, and analyze their characterization for different CNN models under different application and system configuration factors. We find that this joint energy consumption and makespan optimization problem can be formulated as an integer linear programming problem. Then we propose CHESS (CNN-task Heterogeneous Efficient Scheduling System) with a two-stage heuristic scheduling algorithm, to better allocate computing resources for the upcoming tasks, and to schedule them dynamically on the heterogeneous cluster. Experiments show that our CHESS can save up to 15.9% energy and decrease up to 32.7% makespan over existing approaches.
Mask Detection Based on Yolov5s

Since the outbreak of the COVID-19 epidemic, wearing masks has become common sense and necessary protective equipment for go outside. The use of deep learning methods to detect whether a person is wearing a mask has also become a popular research direction in the field of computer vision. As an excellent object detection algorithm, Yolov5 is widely used in various fields. This article also applies the lightweight Yolov5s model for facial mask detection. Yolov5s uses a multi-scale detection method based on Feature Pyramid Network, which can effectively detect masks at different scales. This enables the model to obtain more accurate detection results on images of different scales. Yolov5s is a lightweight model with fewer parameters and faster detection speed compared to other Yolov5 models. The dataset in this article is from the Kaggle website. By preprocessing the dataset and training it on the Yolov5s network model, the trained model was tested and the effect of facial mask wearing detection was achieved.
Multi-semantic Representation with Transformer Network for Video Classification

Video classification is an important and challenging task. Videos usually contain a series of key actions and motion patterns. Video classifiers need to learn and describe them with an embedding vector. Generally, these actions and patterns imply different semantic information. However, existing methods usually only consider single-level semantic features, such as the last pooling layer, to represent the entire video. Complex video content cannot be effectively represented, and classification accuracy is not good enough. To address the limitation, we propose a novel multi-semantic representation method for video classification. Our method consists of several transformer network blocks, semantic graph attention modules, and a feature fusion module. Each transformer block extracts the visual features of video frames and the features of the last block are transformed into an embedding vector. These blocks indicate different levels of visual features. The graph attention module uses these features to generate multi-semantic vectors of a video. Finally, these multi-semantic vectors and the previous embedding vector are fused by a feature fusion module. The fused vector is applied to classify video. Extensive experiments on a benchmark video classification dataset demonstrate that our method outperforms various state-of-the-art methods.
Analysis of the Current Situation of TPACK Research for Primary School Teachers in China

TPACK (Technological Pedagogical And Content Knowledge) is a form of knowledge that involves the interaction and integration of subject content, teaching methods, and technology in education. It represents a novel knowledge framework essential for teachers in the information age. The TPACK framework necessitates a deep understanding of each facet of knowledge to effectively integrate technology, teaching methods, and subject content for impactful instruction. This study employs literature research methodology and CiteSpace software to comprehensively identify and visually analyze the research landscape of TPACK among primary school teachers in China. It explores the distribution patterns, research hotspots, knowledge base, transformations, and emerging trends within TPACK research. The findings reveal that literature on TPACK studies has been published in CSSCI journals since 2011, illustrating the increasing attention towards TPACK research. Initially, the research focuses on the entire group of primary school teachers and subsequently narrows down to rural primary school teachers, further segmented by disciplines. Researchers primarily investigate the current status, influencing factors, and strategies related to professional development of primary school teachers. Domestically, collaborative research groups primarily concentrate in key normal universities, often encompassing cooperation between different departments within the same institution, while cross-institutional collaboration remains limited. Furthermore, intercollegiate cooperation is sporadic, with minimal established collaborative relationships.
Boosting Few-Shot Image Classification with Feature Map Reconstruction Network via Statistical Information

Few-shot image classification aims to recognize classes with limited labeled data. Many works have been proposed to solve this problem. Recently, the feature map reconstruction network has attracted great attention. However, due to the scarcity of data, it is difficult to find an exact solution to the reconstructed model. We try to use statistical information to draw the support set distribution and query set distribution closer, which helps the model obtain a precise solution. We consider first-order information, calculate the mean of each set and eliminate the bias between them. In addition to one-dimensional information, high-dimensional information (i.e. the covariance matrix) is used to represent the data distribution and make Euclidean distance of them as the loss. Our statistical information feature reconstruction network(SI-FRN) does not bring in any other supervised information. Compared to recent state-of-the-art few-shot image classification methods with reconstruction, our SI-FRN gets excellent performance on four benchmarks: mini-ImageNet, tiered-ImageNet, CUB and Aircraft.
Conditional Probability in Machine Learning

To help teaching of machine learning course, manipulation rules and application examples of conditional probabilities in machine learning are presented. The emphasis is to make a clear distinction between reasonable assumptions and logical deductions developed from assumptions and axioms. The formula for conditional probability of conditional probability is presented with examples in Bayesian coin tossing, Bayesian linear regression, and Gaussian processes for regression and classification. The signal + noise model is formulated in terms of a proposition and exemplified by linear-Gaussian models and linear dynamical systems.
Practice of FATE: Performance Improving Techniques

Federated Learning is a type of privacy-preserving machine learning technology, which enables different parties to build a common, robust machine learning model together without sharing private data.
Architecture-Based Attack Path Analysis for Identifying Potential Security Incidents

Analyzing attacks and potential attack paths can help to identify and avoid potential security incidents. Manually estimating an attack path to a targeted software element can be complex since a software system consists of multiple vulnerable elements, such as components, hardware resources, or network elements. In addition, the elements are protected by access control. Software architecture describes the structural elements of the system, which may form elements of the attack path. However, estimating attack paths is complex since different attack paths can lead to a targeted element. Additionally, not all attack paths might be relevant since attack paths can have different properties based on the attacker’s capabilities and knowledge. We developed an approach that enables architects to identify relevant attack paths based on the software architecture. We created a metamodel for filtering options and added support for describing attack paths in an architectural description language. Based on this metamodel, we developed an analysis that automatically estimates attack paths using the software architecture. This can help architects to identify relevant attack paths to a targeted component and increase the system’s overall security. We evaluated our approach on five different scenarios. Our evaluation goals are to investigate our analysis’s accuracy and scalability. The results suggest a high accuracy and good runtime behavior for smaller architectures.
Injecting Commonsense Knowledge into Prompt Learning for Zero-Shot Text Classification

The combination of pre-training and fine-tuning has become a default solution to Natural Language Processing (NLP) tasks. The emergence of prompt learning breaks such routine, especially in the scenarios of low data resources. Insufficient labelled data or even unseen classes are frequent problems in text classification, equipping Pre-trained Language Models (PLMs) with task-specific prompts helps get rid of the dilemma. However, general PLMs are barely provided with commonsense knowledge. In this work, we propose a KG-driven verbalizer that leverages commonsense Knowledge Graph (KG) to map label words with predefined classes. Specifically, we transform the mapping relationships into semantic relevance in the commonsense-injected embedding space. For zero-shot text classification task, experimental results exhibit the effectiveness of our KG-driven verbalizer on a Twitter dataset for natural disasters (i.e. HumAID) compared with other baselines.
Making Schemas and Mappings Available and FAIR: A metadata and schema crosswalk registry from the FAIRCORE4EOSC project

Community standards like the Darwin Core (Darwin Core Task Group 2009) together with semantic artefacts (controlled vocabularies, ontologies, thesauri, and other knowledge organisation systems) are key building blocks for the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016), specifically as emphasized in the Interoperability principle I2 “(Meta)data use vocabularies that follow FAIR principles”. However, most of these artefacts are actually not FAIR themselves (Le Franc et al. 2020). To address this, the FAIRCORE4EOSC project (2022-25) is developing a Metadata Schema and Crosswalk Registry (MSCR) that will allow registered users and communities to create, register and version schemas and crosswalks that all have persistent identifiers (PIDs). The published content can then be searched, browsed and downloaded without restrictions. The MSCR will also provide an API to facilitate the transformation of data from one schema to another via registered crosswalks. It will provide projects and individual researchers with the possibility to manage their metadata schemas and/or relevant metadata schema crosswalks. The schema and crosswalks will be shared with the community for reuse and extension supported by a proper versioning mechanism. The registry tool will facilitate better interoperability between resource catalogues and information systems using different (metadata) schemas and encourage organisations and especially researchers to share their metadata interoperability by publishing the metadata crosswalks used in their workflows, which are currently not visible (FAIRification). By providing an easy-to-use graphical user interface (GUI) for creating crosswalks, the GUI will attract users currently relying on project-specific solutions.
Optical Remote Sensing Image Defogging Algorithm based on Different Wavelength

The shooting conditions of remote sensing images often cause them to be covered by clouds and fog, and remote sensing images handle more details more strictly, so it is very difficult to defog remote sensing images. In this paper, we propose an optical remote sensing image defogging algorithm based on different wavelengths. Firstly, the algorithm increases the transmittance attenuation value of the three channels to the initial energy value of the image based on different wavelength attenuation. Secondly, we use deep learning methods to segment the dense fog areas and determine the global atmosphere light candidate area to estimate the global atmosphere light. Finally, the proposed algorithm uses the atmospheric scattering model to defog the three channels respectively, and the final experimental results are obtained. Experimental results show that our method can achieve better defogging image effect and quality.
A generic sparse regression imputation method for time series and tabular data

Although many missing data imputation methods have been proposed in the relevant literature, they focus on either time series or tabular data, but not on both. Hence, a generic sparse regression method for missing data imputation is proposed. The imputed values of a target feature are generated by solving a sparse least squares problem using a preconditioned iterative method based on generic approximate sparse pseudoinverse. Sparsity is introduced by dummy encoding existing or constructed (through discretization) categorical features. Extensive experiments were conducted on several datasets, and the results demonstrate the effectiveness of the method for both time series and tabular data.
English teaching system design based on ID3 algorithm and node optimization

In order to optimize the integration of English multimedia resources and achieve the goal of sharing English teaching resources in education, this article reconstructs the traditional college English curriculum system. It divides professional English into learning modules according to different majors integrating public health teaching resources. How optimize the integration of English multimedia resources and achieving the goal of sharing English teaching resources (ETR) is the main direction of English teaching reform during the current COVID-19 pandemic. An English multimedia teaching resource-sharing platform is designed to extract feature items from multimedia teaching resources using the ID3 information gain method and construct a decision tree for resource push. In resource sharing, a structured peer-to-peer network is used to manage nodes, query location and share multimedia teaching resources. The optimal gateway node is selected by calculating the distance between each gateway node and the fixed node. Finally, a collaborative filtering (CF) algorithm recommends Multimedia ETR to different users. The simulation results show that the platform can improve the sharing speed and utilization rate of teaching resources, with maximum throughput reaching 12 Mb/s and achieve accurate recommendations of ETR.