Reviewerly/exHarmony huggingface dataset:
Enhanced Local Stabilization of Constrained N-TS Fuzzy Systems With Lighter Computational Burden

The constrained Takagi–Sugeno fuzzy systems with local nonlinear models (N-TS fuzzy systems) are much more practical than the conventional T–S fuzzy models, but their applications are seriously affected by either the great conservatism or the heavy computational burden. In this short article, an enhanced local stabilization of constrained N-TS fuzzy systems is investigated with the purpose of not only reducing conservatism but also relieving computational burden. To do this, a new class of threshold functions are established in order to achieve real-time online assessment of the variation of the normalized fuzzy weighting functions (NFWFs) at different sampling instants, i.e., the one-step-past one and the current one. Thus, a key alternative is proposed for eliminating all the involved terms containing the one-step-past NFWFs while their main information is retained. Since our obtained design criteria are merely dependent on the current NFWFs, the conservatism can be reduced at the expense of lighter computational burden. The benefits of our proposed results are validated by implementing several objective comparisons with those recent results.
External magnetic effect for the security of practical quantum key distribution

Abstract Quantum key distribution (QKD) allows remote parties to share secret keys with proven security. To guarantee the security of practical QKD, the imperfections in realistic devices need to be characterized and considered in practical security analysis. Particularly, a standard QKD system normally uses optical isolator or optical circulator in the transmitter to prevent the injection from external light. Here we find that the optical isolators and circulators, commonly based on the Faraday effect, are subject to the perturbation from external magnetic fields. With this, we provide a comprehensive analysis of the security for practical QKD due to external magnetic effect. We experimentally demonstrate the influence of magnetic fields on both the transmittance and the isolation of standard optical isolators and circulators. We analyze the security risks caused by the potential magnetic attacks, together with the corresponding countermeasures.
Asset Tokenization of Real Estate in Europe

The real estate industry is one of the most important and oldest asset classes in the world. It is also one of the most inefficient and inflexible ones. This paper explores a disruptive technology that could solve fundamental problems within the real estate market. With the advent of blockchain technology, and therefore the concept of tokenization, two major possibilities have arisen: first, the fractionalization of assets or any claims; second, the digital representation of asset ownership. The former feature has the potential to enable access to any investor to realize real estate investment opportunities. This could lead to a more democratized investment environment and greater liquidity. The latter aspect opens up room for real innovation by displaying asset ownership on a digital platform. This paper presents current use cases of blockchain application in the real estate industry. To further verify the business feasibility, it will provide insights gained through industry experts and real estate tokenization company representatives. According to the results, the concept will most probably disrupt the current real estate market and lead to major shifts in revenue and asset ownership in a digital environment. The paper identifies current challenges and hurdles that are yet to be overcome in order for blockchain technology to fully mature in this particular market.
Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?

Abstract Data visualization is powerful in persuading an audience. However, when it is done poorly or maliciously, a visualization may become misleading or even deceiving. Visualizations give further strength to the dissemination of misinformation on the Internet. The visualization research community has long been aware of visualizations that misinform the audience, mostly associated with the terms “lie” and “deceptive.” Still, these discussions have focused only on a handful of cases. To better understand the landscape of misleading visualizations, we open‐coded over one thousand real‐world visualizations that have been reported as misleading. From these examples, we discovered 74 types of issues and formed a taxonomy of misleading elements in visualizations. We found four directions that the research community can follow to widen the discussion on misleading visualizations: (1) informal fallacies in visualizations, (2) exploiting conventions and data literacy, (3) deceptive tricks in uncommon charts, and (4) understanding the designers' dilemma. This work lays the groundwork for these research directions, especially in understanding, detecting, and preventing them.
Cross-modal Knowledge Graph Contrastive Learning for Machine Learning Method Recommendation

The explosive growth of machine learning (ML) methods is overloading users with choices for learning tasks. Method recommendation aims to alleviate this problem by selecting the most appropriate ML methods for given learning tasks. Recent research shows that the descriptive and structural information of the knowledge graphs (KGs) can significantly enhance the performance of ML method recommendation. However, existing studies have not fully explored the descriptive information in KGs, nor have they effectively exploited the descriptive and structural information to provide the necessary supervision. To address these limitations, we distinguish descriptive attributes from the traditional relationships in KGs with the rest as structural connections to expand the scope of KG descriptive information. Based on this insight, we propose the Cross-modal Knowledge Graph Contrastive learning (CKGC) approach, which regards information from descriptive attributes and structural connections as two modalities, learning informative node representations by maximizing the agreement between the descriptive view and the structural view. Through extensive experiments, we demonstrate that CKGC significantly outperforms the state-of-the-art baselines, achieving around 2% higher accurate click-through-rate (CTR) prediction, over 30% more accurate top-10 recommendation, and over 50% more accurate top-20 recommendation compared to the best performing existing approach.
Public Willingness to Engage With COVID-19 Contact Tracing, Quarantine, and Exposure Notification

We conducted a survey to understand how people's willingness to share information with contact tracers, quarantine after a COVID-19 exposure, or activate and use a smartphone exposure notification (EN) application (app) differed by the person or organization making the request or recommendation.We analyzed data from a nationally representative survey with hypothetical scenarios asking participants (N = 2157) to engage in a public health action by health care providers, public health departments, employers, and others. We used Likert scales and ordered logistic regression to compare willingness to take action based on which person or organization made the request, and we summarized findings by race and ethnicity.The highest levels of willingness to engage in contact tracing (adjusted odds ratio [aOR] = 1.74; 95% CI, 1.55-1.96), quarantine (aOR = 1.91; 95% CI, 1.69-2.15), download/activate an EN app (aOR = 1.30; 95% CI, 1.16-1.46), and notify other EN users (aOR = 1.43; 95% CI, 1.27-1.60) were reported when the request came from the participant's personal health care provider rather than from federal public health authorities. When compared with non-Hispanic White participants, non-Hispanic Black participants reported significantly higher levels of willingness to engage in contact tracing (aOR = 1.32; 95% CI, 1.18-1.48), quarantine (aOR = 1.49; 95% CI, 1.37-1.63), download/activate an EN app (aOR = 2.19; 95% CI, 2.01-2.38), and notify other EN users (aOR = 1.63; 95% CI, 1.49-1.79).Partnering with individuals and organizations perceived as trustworthy may help influence people expressing a lower level of willingness to engage in each activity, while those expressing a higher level of willingness to engage in each activity may benefit from targeted communications.
Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.
Towards corpora creation from social web in Brazilian Portuguese to support public security analyses and decisions

Purpose This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions. Design/methodology/approach The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling. Findings Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security. Research limitations/implications Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses. Practical implications The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities. Originality/value The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.
New energy industry financial technology based on machine learning to help rural revitalization

Rural economic development plays an important role in China’s economic development, which can not only directly drive the development of the primary industry, but also indirectly drive the development of China’s secondary and tertiary industries. With the development of the times and the advancement of science and technology, the application of intelligent technology in rural areas has become increasingly difficult in the context of social digital transformation, and the digital divide in large rural areas has widened, the energy industry financial technology is not satisfactory. In this context, in order to fully implement the rural revitalization strategy, bridge the digital divide between urban and rural areas, accelerate the pace of urban and rural financial technology, revitalize rural industries as soon as possible, and revitalize rural talents, this paper used machine learning algorithms to apply new energy industry fintech technology to rural revitalization. It can not only reduce the problem of rural information asymmetry, but also improve the rural economic level by 13.7%, which laid the foundation for the implementation of the strategy of technology-assisted financial services for rural revitalization.
Multimodal deep learning: An improvement in prognostication or a reflection of batch effect?

Chen et al. recently reported a novel artificial intelligence (AI) approach for the multimodal fusion (MMF) of genomic and histologic data to more accurately predict the prognosis of 14 cancer types in The Cancer Genome Atlas (TCGA). 1 Chen R.J. Lu M.Y. Williamson D.F. Chen T.Y. Lipkova J. Noor Z. Shaban M. Shady M. Williams M. Joo B. Mahmood F. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell. 2022; 40: 865-878.e6https://doi.org/10.1016/j.ccell.2022.07.004 Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar This is the largest study to date which combines image data and genomic data for outcome prediction in cancer. The authors found that in 12 of the 14 cancer types tested, MMF outperformed attention-based multiple-instance learning (MIL) on histology alone and self-normalized network (SSN) on genomic data alone. They also compared the combined histology/genomic models to Cox models trained on age, sex, and grade, demonstrating the superiority of MMF in prognostication. Such an approach may better characterize cancer biology and prognosis by accurately integrating features derived from both gene expression data and digital histology. Pan-cancer integrative histology-genomic analysis via multimodal deep learningChen et al.Cancer CellAugust 08, 2022In BriefChen et al. present a pan-cancer analysis that uses deep learning to integrate whole-slide pathology images and molecular features to predict cancer prognosis, with multimodal interpretability used to elucidate morphologic and molecular correlates of prognosis. Full-Text PDF Open Access
Error Propagation in NISQ Devices for Solving Classical Optimization Problems

We propose a random circuit model that attempts to capture the behavior of noisy intermediate-scale quantum devices when used for variationally solving classical optimization problems. Our model accounts for the propagation of arbitrary single-qubit errors through the circuit. We find that, even with a small noise rate, the quality of the obtained optima implies that a single-qubit error rate of 1/(nD) (where n is the number of qubits and D is the circuit depth) is needed for the possibility of a quantum advantage. We estimate that this translates to an error rate lower than 10−6 using the quantum approximate optimization algorithm for classical optimization problems with two-dimensional circuits.Received 13 April 2022Accepted 13 October 2022DOI:https://doi.org/10.1103/PRXQuantum.3.040326Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum Information
Robot Process Automation in Blockchain

Blockchain is being referred to as the 5th innovative computer invention. In simple words, blockchain is a block of records. In a nutshell, it is a shared ledger of unchangeable and demonstrable data. Blockchains are digital ledgers resistant to tampering and are often deployed in a decentralized form with no central authority. At its most basic, they permit the public to track record operations in a shared ledger within that community, such that no transaction can be modified once published under regular blockchain network functioning. Blockchain technology is evolving at a rapid pace, and it is feasible to combine it with a variety of systems, including robotics and AI applications. A blockchain is simply a distributed database of records or a public ledger of all transactions or digital events that have been conducted and shared among participants. Each transaction in the public ledger is validated by most of the system’s members. Furthermore, once information is input, it cannot be erased. The blockchain includes a precise and verifiable record of every single transaction that has ever occurred. Analyze the next obstacles and commercial possibilities in this foundational technology, which is poised to alter our digital world. This chapter will explore how robotic process automation (RPA) implement in blockchain technology and its impact, and where future developments are possible.
Entrepreneurial competence and information technology capability as indicators of business success

The importance of entrepreneurial competencies and information technology capabilities has increased in the last few decades because of the strategic role played by the entrepreneurs. Therefore, this study aims to investigate the impact of entrepreneur competency and information technology capability on business success. A survey questionnaire was conducted to test the influence of entrepreneur competency and information technology capability. This questionnaire was filled by 403 participants who were recruited through Jordan Chamber of Commerce so as to identify the business owners who had started their companies in the recent years. The findings of the study showed that entrepreneurial competencies have a positive effect on business success and information technology which was used as a mediating role has a positive impact on both entrepreneurial competencies and business success. The findings of this research will be helpful to entrepreneurs and policy makers.
CoMER: Modeling Coverage for Transformer-Based Handwritten Mathematical Expression Recognition

The Transformer-based encoder-decoder architecture has recently made significant advances in recognizing handwritten mathematical expressions. However, the transformer model still suffers from the lack of coverage problem, making its expression recognition rate (ExpRate) inferior to its RNN counterpart. Coverage information, which records the alignment information of the past steps, has proven effective in the RNN models. In this paper, we propose CoMER, a model that adopts the coverage information in the transformer decoder. Specifically, we propose a novel Attention Refinement Module (ARM) to refine the attention weights with past alignment information without hurting its parallelism. Furthermore, we take coverage information to the extreme by proposing self-coverage and cross-coverage, which utilize the past alignment information from the current and previous layers. Experiments show that CoMER improves the ExpRate by 0.61%/2.09%/1.59% compared to the current state-of-the-art model, and reaches 59.33%/59.81%/62.97% on the CROHME 2014/2016/2019 test sets. (Source code is available at https://github.com/Green-Wood/CoMER )
Adversarial Feature Augmentation for Cross-domain Few-Shot Classification

Few-shot classification is a promising approach to solving the problem of classifying novel classes with only limited annotated data for training. Existing methods based on meta-learning predict novel-class labels for (target domain) testing tasks via meta knowledge learned from (source domain) training tasks of base classes. However, most existing works may fail to generalize to novel classes due to the probably large domain discrepancy across domains. To address this issue, we propose a novel adversarial feature augmentation (AFA) method to bridge the domain gap in few-shot learning. The feature augmentation is designed to simulate distribution variations by maximizing the domain discrepancy . During adversarial training, the domain discriminator is learned by distinguishing the augmented features (unseen domain) from the original ones (seen domain), while the domain discrepancy is minimized to obtain the optimal feature encoder. The proposed method is a plug-and-play module that can be easily integrated into existing few-shot learning methods based on meta-learning. Extensive experiments on nine datasets demonstrate the superiority of our method for cross-domain few-shot classification compared with the state of the art. Code is available at https://github.com/youthhoo/AFA_For_Few_shot_learning .
RVEA-based multi-objective workflow scheduling in cloud environments

Cloud computing is a major heterogeneous distributed system that can obtain the required resources for the different needs of customers through the network. With the advancement of technology, cloud workflow scheduling has become a widely studied area aiming to utilise cloud resources efficiently. In general, the workflow scheduling problem in a cloud environment is parallel, dependent, and complex. So far, there are many algorithms in the field of workflow resource scheduling in the cloud environment. However, most of these algorithms only consider makespan or cost, and research on multiple targets is still relatively scarce. Considering the characteristics of tasks and users, this paper constructs a workflow scheduling model targeting makespan, cost, and load in the cloud environment. To better address the multi-objective cloud workflow scheduling model, a reference vector-guided evolutionary algorithm (RVEA) is used in this paper. The results show that the algorithm can effectively improve the performance of the proposed model and obtain a suitable workflow scheduling policy compared with existing multi-objective evolutionary algorithms.