Reviewerly/exHarmony huggingface dataset:
Applications of Blockchain in Business Processes: A Comprehensive Review

Blockchain (BC), as an emerging technology, is revolutionizing Business Process Management (BPM) in multiple ways.The main adoption is to serve as a trusted infrastructure to guarantee the trust of collaborations among multiple partners in trustless environments.Especially, BC enables trust of information by using Distributed Ledger Technology (DLT).With the power of smart contracts, BC enforces the obligations of counterparties that transact in a business process (BP) by programming the contracts as transactions.This paper aims to study the state-of-the-art of BC technologies by (1) exploring its applications in BPM with the focus on how BC provides the trust of BPs in their lifecycles; (2) identifying the relations of BPM as the need and BC as the solution with the assessment towards BPM characteristics; (3) discussing the up-to-date progresses of critical BC in BPM; (4) identifying the challenges and research directions for future advancement in the domain.The main conclusions of our comprehensive review are (1) the study of adopting BC in BPM has attracted a great deal of attention that has been evidenced by a rapidly growing number of relevant articles.(2) The paradigms of BPM over Internet of Things (IoT) have been shifted from persistent to transient, from static to dynamic, and from centralized to decentralized, and new enabling technologies are highly demanded to fulfill some emerging functional requirements (FRs) at the stages of design, configuration, diagnosis, and evaluation of BPs in their lifecycles.(3) BC has been intensively studied and proven as a promising solution to assure the trustiness for both of business processes and their executions in decentralized BPM.(4) Most of the reported BC applications are at their primary stages, future research efforts are needed to meet the technical challenges involved in interoperation, determination of trusted entities, confirmation of time-sensitive execution, and support of irreversibility.
A study of factors influencing Chinese college students’ intention of using metaverse technology for basketball learning: Extending the technology acceptance model

Based on the expansion of flow constructs based on the TAM model, this study assesses the impact of metaverse technology in college basketball courses.We surveyed 849 effective samples using an online questionnaire survey, verified our analysis using structural equation modeling, and examined the moderating effect of gender on the path relationship.The perceived ease of use, the flow experience, and the perceived usefulness of the product are important predictors of behavioral intention. According to the study, perceived usefulness, and flow experience influence attitudes significantly. A moderating effect of gender is observed on perceived ease of use on the path to behavioral intention, and the results extend the theoretical research on the use of metaverse technology for basketball instruction and TAM.A metaverse-based learning experience can enhance the flow experience of basketball learning, thus increasing the willingness to use and the effectiveness of learning.
Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data

Cybercriminals use social media platforms to disseminate spam, misleading facts, fake news, and malicious links. Blocking such deceptive social media spam is essential. However, extracting relevant features from social networks is challenging due to privacy and time constraints. Traditional frequency-based word representation techniques are time-consuming and inefficient in producing contextual word vectors. Word embeddings and deep learning models have recently shown good results in text classification. Also, most existing approaches assumed balanced class distribution, which is false for most real-world datasets. In this paper, an attempt is made to advance the performance of the social spam detection system by leveraging dataset balancing, advanced word embedding techniques, machine learning, and deep learning approaches with the self-attention mechanism. In the proposed framework, the datasets are balanced using NearMiss and SmoteTomek techniques to feed several machine-learning models. Later, the baseline ML models and proposed voting-based ensemble models are evaluated on imbalanced and balanced datasets. For the proposed deep learning-based hybrid approaches, embeddings are generated using GloVe and FastText word embeddings on the balanced combined dataset and passed into the deep neural network comprised of Conv1D and Bi-directional recurrent neural network layers with the self-attention mechanism for improved context understanding and effective results. This study examines hybrid approaches for detecting social spam using imbalanced social network data and picks the optimum combination. Besides, Machine learning ensembles, word embeddings, deep learning with hyper-parameter optimization, and a self-attention method are compared thoroughly. Experiments and comparisons with other techniques show that the proposed hybrid framework with deep learning-based approaches achieves better performance.
ClusterSeg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets

The detection and segmentation of individual cells or nuclei is often involved in image analysis across a variety of biology and biomedical applications as an indispensable prerequisite. However, the ubiquitous presence of crowd clusters with morphological variations often hinders successful instance segmentation. In this paper, nuclei cluster focused annotation strategies and frameworks are proposed to overcome this challenging practical problem. Specifically, we design a nucleus segmentation framework, namely ClusterSeg, to tackle nuclei clusters, which consists of a convolutional-transformer hybrid encoder and a 2.5-path decoder for precise predictions of nuclei instance mask, contours, and clustered-edges. Additionally, an annotation-efficient clustered-edge pointed strategy pinpoints the salient and error-prone boundaries, where a partially-supervised PS-ClusterSeg is presented using ClusterSeg as the segmentation backbone. The framework is evaluated with four privately curated image sets and two public sets with characteristic severely clustered nuclei across a variety range of image modalities, e.g., microscope, cytopathology, and histopathology images. The proposed ClusterSeg and PS-ClusterSeg are modality-independent and generalizable, and superior to current state-of-the-art approaches in multiple metrics empirically. Our collected data, the elaborate annotations to both public and private set, as well the source code, are released publicly at https://github.com/lu-yizhou/ClusterSeg.
The eSports Medicine: Pre-Participation Screening and Injuries Management—An Update

Recently, electronic sports (eSports) became one of the growing forms of new media due to the wide diffusion of games and online technologies. Even if there is still a debate about the definition and characterization of eSports, eAthletes train heavily, compete in tournaments, must abide by competition, association, and governing body rules, just like all other athletes. Furthermore, as in any other competitive discipline, there can be injuries. Aberrant sitting posture, repetitive movements, screen vision, prolonged playing hours, and a sedentary lifestyle can lead to several medical hazards in musculoskeletal, ophthalmology, neurological, and metabolic systems. Moreover, several cardiovascular changes occur in eAthletes. This paper aims to explore the different injuries that can occur in a professional eAthlete, suggesting how every high-level gamer could benefit from a pre-participation evaluation and a correct injury prevention strategy.
Amazement and Trepidation: Implications of AI-Based Natural Language Production for the Teaching of Writing

AI-based natural language production systems are currently able to produce unique text with minimal human intervention. Because such systems are improving at a very fast pace, teachers who expect students to produce their own writing—engaging in the complex processes of generating and organizing ideas, researching topics, drafting coherent prose, and using feedback to make principled revisions that both improve the quality of the text and help them to develop as writers—will confront the prospect that students can use the systems to produce human-looking text without engaging in these processes. In this article, we first describe the nature and capabilities of AI-based natural language production systems such as GPT-3, then offer some suggestions for how instructors might meet the challenges of the increasing improvement of the systems and their availability to students.
Function value ranking aware differential evolution for global numerical optimization

Differential evolution (DE) has been experimentally demonstrated to be effective in solving optimization problems. However, the effectiveness of DE encounters rapid deterioration in the face of complicated optimization problems with many grotesque local basins, which are emerging increasingly frequently nowadays. To alleviate this predicament, this paper devises a simple yet effective mutation scheme named "DE/current-to-rwrand/1" to further promote the optimization ability of DE in solving complicated optimization problems. Specifically, this strategy assigns a non-linear selection probability for each individual, which is computed based on its function value ranking. As a result, all individuals can be potentially selected to direct the mutation of the population, but better individuals preserve exponentially larger selection probabilities. In this way, the resultant DE, which is named function value ranking aware differential evolution (FVRADE), is expectedly capable of balancing high diversity and fast convergence of the population well to find satisfactory solutions to optimization problems. In particular, two classical and popular mutation strategies, namely "DE/current-to-rand/1" and "DE/current-to-best/1", are two special cases of the proposed mutation strategy. Abundant experiments have been extensively executed on the well-known CEC'2017 and the latest CEC'2021 benchmark problem sets. Experimental results have verified that FVRADE performs highly competitively with or even significantly better than several representative and state-of-the-art peer methods. In particular, it is experimentally demonstrated that FVRADE is particularly good at solving complicated optimization problems, and preserves a good scalability to deal with optimization problems. Besides, experiments have also been conducted on the popularly adopted CEC'2011 real-world optimization problem suite and the experimental results have substantiated that FVRADE is very promising to solve real-world optimization problems.
A Comparative Analysis of Story Representations for Interactive Narrative Systems

Interactive narrative is a blossoming field, with new systems and approaches being developed each year. However, despite this burgeoning array of designs, little work has been done in empirically evaluating and comparing them (Weyhrauch 1997; Nelson and Mateas 2005; Magerko 2007). Such a comparison is complicated by the fact that different systems are used to create different narratives; it is difficult to differentiate between the authored content and the system capabilities in an empirical setting. This paper proposes a comparative analysis approach to determining the strengths and weaknesses of interactive narrative systems. Such a comparison is a positive first step towards understanding and evaluating current and future interactive narrative systems.
Knowledge graph representation learning with simplifying hierarchical feature propagation

Graph neural networks (GNN) have emerged as a new state-of-the-art for learning knowledge graph representations. Although they have shown impressive performance in recent studies, how to efficiently and effectively aggregate neighboring features is not well designed. To tackle this challenge, we propose the simplifying heterogeneous graph neural network (SHGNet), a generic framework that discards the two standard operations in GNN, including the transformation matrix and nonlinear activation. SHGNet, in particular, adopts only the essential component of neighborhood aggregation in GNN and incorporates relation features into feature propagation. Furthermore, to capture complex structures, SHGNet utilizes a hierarchical aggregation architecture, including node aggregation and relation weighting. Thus, the proposed model can treat each relation differently and selectively aggregate informative features. SHGNet has been evaluated for link prediction tasks on three real-world benchmark datasets. The experimental results show that SHGNet significantly promotes efficiency while maintaining superior performance, outperforming all the existing models in 3 out of 4 metrics on NELL-995 and in 4 out of 4 metrics on FB15k-237 dataset.
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to train strong GNN models in a distributed manner without sharing their private data. A core challenge in federated systems is the non-IID problem, which also widely exists in real-world graph data. For example, local data of clients may come from diverse datasets or even domains, e.g., social networks and molecules, increasing the difficulty for FGL methods to capture commonly shared knowledge and learn a generalized encoder. From real-world graph datasets, we observe that some structural properties are shared by various domains, presenting great potential for sharing structural knowledge in FGL. Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks. To explicitly extract the structure information rather than encoding them along with the node features, we define structure embeddings and encode them with an independent structure encoder. Then, the structure encoder is shared across clients while the feature-based knowledge is learned in a personalized way, making FedStar capable of capturing more structure-based domain-invariant information and avoiding feature misalignment issues. We perform extensive experiments over both cross-dataset and cross-domain non-IID FGL settings, demonstrating the superiority of FedStar.
Adaptive cloud orchestration resource selection using rough set theory

The recent research is developing in a vast speed to develop the cloud orchestration system. In cloud system the remotely managed servers are storing, finding, removing, replacing and retrieving the various services in an adaptive optimized manner. The lot of services are provided by the vast number of providers in the market with the help of approximation theory by the rough set system (RST). RST finds in helping in getting the efficient cloud resources as a service to the users. The proposed OCRS (Optimized Cost Resource System) approach is being simulated and compared with the existing cloud simulator. The simulator gives the approximate results in many parameters of cloud services. In all aspects our algorithm is performing better.
Penny for Your Thoughts: Searching for the 50 Cent Party on Sina Weibo

Evidence suggests that the Chinese government employs “Internet Commentators” to post propaganda on social media. This group is pejoratively nicknamed the “50 cent party” or Wumao. In this study, we make the first attempt to quantify the size and behavior of the Wumao. Our study leverages a large corpus of data from Sina Weibo (Twitter in China) that includes 26M tweets and comments from 2.7M users over the span of one year. Unfortunately, detecting the Wumao is difficult because there is no ground truth information about them. To overcome this challenge, we apply a series of unsupervised techniques to filter our dataset and isolate suspicious users who exhibit characteristics indicative of being Wumao.
A Study of the Impact of Transformational Leadership, Organizational Learning, and Knowledge Management on Organizational Innovation

Purpose: This study aims at finding the potential impact on organizational innovations and corporate performance through the influence of transformational leadership on knowledge management and organizational learning.Methodology: Stratified random sampling was used to collect 273 usable responses from 500 respondents for using Structural Equation Modeling (SEM).The deductive approach was adopted under positivism philosophy. Findings:The Result shows that transformational leadership, organizational learning, and knowledge management significantly predict corporate innovation and those, in effect, foster organizational performance through organizational innovation.Implication: One of the implications of this study is the role of transformational leadership is worth evident through empirical findings to transform knowledge management and organizational learning into organizational innovation. Limitation:The size of the sample prevents the result to generalize the results in the home and abroad, and on the contrary, random sampling could have improved universal acceptance of the result which was not used in this study.
Probabilistic Logic Programming with Beta-Distributed Random Variables

We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains. Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.
Roadmap to Talking Quantum Movies: a Contingent Inquiry

Quantum computation and quantum information science have transmuted from classroom conjectures and demonstrations usually confined to the laboratory into exciting sub-disciplines with potential for profound impacts in information and communication security, device miniaturization, cosmology, Internet, and so on. Recent developments in both areas have further changed the landscape of future computing and information processing technologies. Encouraged by this, both fields have witnessed interests from professional bodies, notably, the Institute of Electrical and Electronics Engineers (IEEE) via the IEEE P7130 working group on standards for quantum computation as well as similar interests from big technology companies (Google, Microsoft, IBM, etc.) as well as those from national and regional (China, European Union, United Kingdom, Australia, Japan, and so on) governments. With all these activities, presently, interest in and funding for quantum computation and quantum information science are at their highest points ever, thus heralding the inevitable feat of physical realization of quantum computing hardware. Considering the ubiquity and indispensability of imaging and video technologies in our lives today, it is paramount that the utility of any future computing paradigm be measured in terms of its ability to advance and guarantee perpetuity in terms of the efficacy of such technologies. Quantum image processing (QIP), a sub-discipline mainly focused on using quantum computing hardware for the attainment of the aforementioned objectives, has emerged as one of the most promising fields in the quantum computing and quantum information science domains. Its extension to realize quantum movies (or videos), which is the main subject of this disquisition, is an even newer prospect. Consequently, the main objectives of this review are twofold. First, targeting enthusiasts and beginners in the quantum computing field, the treatise provides both an essential background on the rudiments of QIP as well as an exhaustive overview of all literature related to quantum movies and/or videos. Second, for the benefit of those already pursuing research in the QIP sub-discipline, the commentary provides useful insights, allegories, and conjectures to support the integration of other media, such as quantum audio and so on, for the realization of colored talking quantum movies (or video). On the whole, the review is intended to serve as an avenue to invigorate activities tailored toward accelerating the realization of QIP-based quantum technologies.
Towards Gradient Free and Projection Free Stochastic Optimization

This paper focuses on the problem of \emph{constrained} \emph{stochastic} optimization. A zeroth order Frank-Wolfe algorithm is proposed, which in addition to the projection-free nature of the vanilla Frank-Wolfe algorithm makes it gradient free. Under convexity and smoothness assumption, we show that the proposed algorithm converges to the optimal objective function at a rate $O\left(1/T^{1/3}\right)$, where $T$ denotes the iteration count. In particular, the primal sub-optimality gap is shown to have a dimension dependence of $O\left(d^{1/3}\right)$, which is the best known dimension dependence among all zeroth order optimization algorithms with one directional derivative per iteration. For non-convex functions, we obtain the \emph{Frank-Wolfe} gap to be $O\left(d^{1/3}T^{-1/4}\right)$. Experiments on black-box optimization setups demonstrate the efficacy of the proposed algorithm.
Geometric calibration of markerless optical surgical navigation system

Abstract Background Patient‐to‐image registration is required for image‐guided surgical navigation, but marker‐based registration is time consuming and is subject to manual error. Markerless registration is an alternative solution to avoid these issues. Methods This study designs a calibration board and proposes a geometric calibration method to calibrate the near‐infrared tracking and structured light components of the proposed optical surgical navigation system simultaneously. Results A planar board and a cylinder are used to evaluate the accuracy of calibration. The mean error for the board experiment is 0.035 mm, and the diameter error for the cylinder experiment is 0.119 mm. A calibration board is reconstructed to evaluate the accuracy of the calibration, and the measured mean error is 0.012 mm. A head phantom is reconstructed and tracked by the proposed optical surgical navigation system. The tracking error is less than 0.3 mm. Conclusions Experimental results show that the proposed method obtains high accessibility and accuracy and satisfies application requirements.
Learning deep discriminative face features by customized weighted constraint

Different convolutional neural networks (CNNs) may learn different levels of discriminative features to represent the raw face data. To enhance the discrimination of deeply learned face features, we propose a customized weighted discriminative loss (CWD loss) to seek a customized constraint for mitigating the large perturbations caused by imbalanced distribution of correctly-classified features and misclassified features. It focuses on mapping the raw data into a feature space such that deeply learned face features can achieve a high discrimination for representation, by restraining the intra-class variations and the inter-class variations, simultaneously. Extensive experiments carried out on several famous face recognition benchmarks, including LFW, YTF, FGLFW and BLUFR, demonstrate that the proposed approach can achieve superior performance over the related approaches.