Reviewerly/exHarmony huggingface dataset:
Populist discourse and entrepreneurship: The role of political ideology and institutions

Abstract Using institutional economic theory as our guiding framework, we develop a model to describe how populist discourse by a nation’s political leader influences entrepreneurship. We hypothesize that populist discourse reduces entrepreneurship by creating regime uncertainty concerning the future stability of the institutional environment, resulting in entrepreneurs anticipating higher future transaction costs. Our model highlights two important factors that moderate the relationship. First is the strength of political checks and balances, which we hypothesize weakens the negative relationship between populist discourse and entrepreneurship by providing entrepreneurs with greater confidence that the actions of a populist will be constrained. Second, the political ideology of the leader moderates the relationship between populist discourse and entrepreneurship. The anti-capitalistic rhetoric of left-wing populism will create greater regime uncertainty than right-wing populism, which is often accompanied by rhetoric critical of free trade and foreigners, but also supportive of business interests. The effect of centrist populism, which is often accompanied by a mix of contradictory and often moderate ideas that make it difficult to discern future transaction costs, will have a weaker negative effect on entrepreneurship than either left-wing or right-wing populism. We empirically test our model using a multi-level design and a dataset comprised of more than 780,000 individuals in 33 countries over the period 2002–2016. Our analysis largely supports our theory regarding the moderating role of ideology. However, surprisingly, our findings suggest that the negative effect of populism on entrepreneurship is greater in nations with stronger checks and balances.
Example of Integrating e-Learning Platforms with Social Network for Create Effective Training Courses

Pedagogical design technologies are focused on the use of various e-learning tools based on electronic platforms and social networks for the development of training courses as well as the delivery of educational materials in the format of interactive social communication. The article discusses an example of organizing training in the Moodle environment and methods of integration with the social network VKontakte
A novel algorithm for distance measurement using stereo camera

Abstract Distance estimation can be achieved by using active sensors or with the help of passive sensors such as cameras. The stereo vision system is generally composed of two cameras to mimic the human binocular vision. In this paper, a Python-based algorithm is proposed to find the parameters of each camera, rectify the images, create the disparity maps and finally use these maps for distance measurements. Experiments using real-time images, which were captured from our stereo vision system, of different obstacles positioned at multiple distances (60–200 cm) prove the effectiveness of the proposed program and show that the calculated distance to the obstacle is relatively accurate. The accuracy of distance measurement is up to 99.83%. The processing time needed to calculate the distance between the obstacle and the cameras is less than 0.355 s.
Towards Secure Searchable Electronic Health Records Using Consortium Blockchain

There are significant data privacy implications associated with Electronic Health Records (EHRs) sharing among various untrusted healthcare entities. Recently, a blockchain-based EHRs sharing system has provided many benefits. Decentralization, anonymity, unforgeability, and verifiability are all unique properties of blockchain technology. In this paper, we propose a secure, blockchain-based EHR sharing system. After receiving the data owner’s authorization, the data requester can use the data provider’s keyword search to discover relevant EHRs on the EHR consortium blockchain and obtain the re-encryption ciphertext from the proxy server. To attain privacy, access control and data security, the proposed technique uses asymmetric searchable encryption and conditional proxy re-encryption. Likewise, proof of permission serves in consortium blockchains as the consensus method to ensure the system’s availability. The proposed protocol can achieve the specified security goals, according to the security analysis. In addition, we simulate basic cryptography and put the developed protocol into practice on the Ethereum platform. The analysis results suggest that the developed protocol is computationally efficient.
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation

Effectively representing users lie at the core of modern recommender systems. Since users’ interests naturally exhibit multiple aspects, it is of increasing interest to develop multi-interest frameworks for recommendation, rather than represent each user with an overall embedding. Despite their effectiveness, existing methods solely exploit the encoder (the forward flow) to represent multiple aspects of interests. However, without explicit regularization, the interest embeddings may not be distinct from each other nor semantically reflect representative historical items. Towards this end, we propose the Re4 framework, which leverages the backward flow to reexamine each interest embedding. Specifically, Re4 encapsulates three backward flows, i.e., 1) Re-contrast, which drives each interest embedding to be distinct from other interests using contrastive learning; 2) Re-attend, which ensures the interest-item correlation estimation in the forward flow to be consistent with the criterion used in final recommendation; and 3) Re-construct, which ensures that each interest embedding can semantically reflect the information of representative items that relate to the corresponding interest. We demonstrate the novel forward-backward multi-interest paradigm on ComiRec, and perform extensive experiments on three real-world datasets. Empirical studies validate that Re4 helps to learn learning distinct and effective multi-interest representations.
Am I a Real or Fake Celebrity? Evaluating Face Recognition and Verification APIs under Deepfake Impersonation Attack

Recent advancements in web-based multimedia technologies, such as face recognition web services powered by deep learning, have been significant. As a result, companies such as Microsoft, Amazon, and Naver provide highly accurate commercial face recognition web services for a variety of multimedia applications. Naturally, such technologies face persistent threats, as virtually anyone with access to deepfakes can quickly launch impersonation attacks. These attacks pose a serious threat to authentication services, which rely heavily on the performance of their underlying face recognition technologies. Despite its gravity, deepfake abuse involving commercial web services and their robustness have not been thoroughly measured and investigated. By conducting a case study on celebrity face recognition, we examine the robustness of black-box commercial face recognition web APIs and open-source tools against Deepfake Impersonation (DI) attacks. While the majority of APIs do not make specific claims of deepfake robustness, we find that authentication mechanisms may get built one top of them, nonetheless. We demonstrate the vulnerability of face recognition technologies to DI attacks, achieving respective success rates of 78.0% for targeted (TA) attacks; we also propose mitigation strategies, lowering respective attack success rates to as low as 1.26% for TA attacks with adversarial training.
Intelligent garbage classification system based on improve MobileNetV3-Large

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Clinical target segmentation using a novel deep neural network: double attention Res-U-Net

We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest object modeling, presence of noise, and signal dropout throughout the measurement. The base line image segmentation approaches are not sufficient for complex target segmentation throughout the various medical image types. To overcome the issues, a novel U-Net-based model proposed that consists of two consecutive networks with five and four encoding and decoding levels respectively. In each of networks, there are four residual blocks between the encoder-decoder path and skip connections that help the networks to tackle the vanishing gradient problem, followed by the multi-scale attention gates to generate richer contextual information. To evaluate our architecture, we investigated three distinct data-sets, (i.e., CVC-ClinicDB dataset, Multi-site MRI dataset, and a collected ultrasound dataset). The proposed algorithm achieved Dice and Jaccard coefficients of 95.79%, 91.62%, respectively for CRL, and 93.84% and 89.08% for fetal foot segmentation. Moreover, the proposed model outperformed the state-of-the-art U-Net based model on the external CVC-ClinicDB, and multi-site MRI datasets with Dice and Jaccard coefficients of 83%, 75.31% for CVC-ClinicDB, and 92.07% and 87.14% for multi-site MRI dataset, respectively.
Research on the Blue Carbon Trading Market System under Blockchain Technology

To combat global warming, “carbon neutrality” has gradually become a consensus. Some countries have constructed domestic and regional carbon trading markets, and links to global carbon markets are receiving increasing attention. Blue carbon, an important component of nature-based solutions (NbS), has not received the attention it deserves and is still isolated from the carbon trading market. Based on summarizing the existing achievements of blockchain application in carbon trading, this paper analyzes how to make each participating body provide services in the process of blue carbon production, circulation and trading, and designs the architectural diagram of the blue carbon system under peer-to-peer (P2P) transactions based on the theoretical framework of blockchain 3.0. Utilizing the advantages of decentralization, high transparency and non-tamperability of blockchain, we can realize a highly efficient, low-cost and intelligent blue carbon trading management system. Thus, we can make the world pay more attention to the development and utilization of marine resources and increase investment in blue carbon sink projects; and promote the development of blue carbon market, enrich the carbon trading market, and help achieve “emission reduction without reduction in production”.
Research on the driving factors and carbon emission reduction pathways of China's iron and steel industry under the vision of carbon neutrality

Promoting low-carbon development in the iron and steel industry (ISI) is essential for China to achieve the carbon neutrality targets. This paper separately adopts the Logarithmic Mean Divisia Index (LMDI) technique and the Mean Impact Value (MIV) method to analyze the impact of driving factors on the CO2 emissions of ISI in the historical and future dimensions. Furthermore, this paper establishes the extreme learning machine model optimized by the bat algorithm (BA-BP) to explore the carbon emission reduction pathways of ISI in the business-as-usual (BAU) scenario, the low-speed, medium-speed and high-speed development scenarios considering the constraints of carbon neutrality targets. The results reveal that: (1) Production capacity and energy efficiency are important drivers of CO2 emissions in ISI; (2) The emission reduction situation is not optimistic under the BAU scenario, and it is difficult to accomplish the carbon neutrality goals by 2060; (3) Under the most ideal emission reduction pathway (corresponding to the high-speed development scenario), ISI will reach its peak in 2022 with the peak value of 2143.42 MtCO2. Compared to the peak year, the CO2 emissions will be reduced by 654.69 MtCO2 and 1558.61 MtCO2 in 2030 and 2050, respectively. Moreover, the achievement of short-term and long-term emission reduction targets depends on production capacity decline and technological progress, respectively. The optimal emission reduction pathway provides a reference for ISI to formulate periodic emission reduction targets.
GCRec: Graph-Augmented Capsule Network for Next-Item Recommendation

Next-item recommendation has been a hot research, which aims at predicting the next action by modeling users' behavior sequences. While previous efforts toward this task have been made in capturing complex item transition patterns, we argue that they still suffer from three limitations: 1) they have difficulty in explicitly capturing the impact of inherent order of item transition patterns; 2) only a simple and crude embedding is insufficient to yield satisfactory long-term users' representations from limited training sequences; and 3) they are incapable of dynamically integrating long-term and short-term user interest modeling. In this work, we propose a novel solution named graph-augmented capsule network (GCRec), which exploits sequential user behaviors in a more fine-grained manner. Specifically, we employ a linear graph convolution module to learn informative long-term representations of users. Furthermore, we devise a user-specific capsule module and a position-aware gating module, which are sensitive to the relative sequential order of the recently interacted items, to capture sequential patterns at union-level and point-level. To aggregate the long-term and short-term user interests as a representative vector, we design a dual-gating mechanism, which could decide the contribution ratio of each module given different contextual information. Through extensive experiments on four benchmarks, we validate the rationality and effectiveness of GCRec on the next-item recommendation task.
Explainable Fairness in Recommendation

Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model disparity in recommendation. This information is critical for recommender system designers to understand the intrinsic recommendation mechanism and provides insights on how to improve model fairness to decision makers. Fortunately, with the rapid development of Explainable AI, we can use model explainability to gain insights into model (un)fairness. In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology. Particularly, we focus on a common setting with feature-aware recommendation and exposure unfairness, but the proposed explainable fairness framework is general and can be applied to other recommendation settings and fairness definitions. We propose a Counterfactual Explainable Fairness framework, called CEF, which generates explanations about model fairness that can improve the fairness without significantly hurting the performance.The CEF framework formulates an optimization problem to learn the "minimal" change of the input features that changes the recommendation results to a certain level of fairness. Based on the counterfactual recommendation result of each feature, we calculate an explainability score in terms of the fairness-utility trade-off to rank all the feature-based explanations, and select the top ones as fairness explanations.
Towards Modeling of Virtual Reality Welding Simulators to Promote Accessible and Scalable Training

The US manufacturing industry is currently facing a welding workforce shortage which is largely due to inadequacy of widespread welding training. To address this challenge, we present a Virtual Reality (VR)-based training system aimed at transforming state-of-the-art-welding simulations and in-person instruction into a widely accessible and engaging platform. We applied backward design principles to design a low-cost welding simulator in the form of modularized units through active consulting with welding training experts. Using a minimum viable prototype, we conducted a user study with 24 novices to test the system’s usability. Our findings show (1) greater effectiveness of the system in transferring skills to real-world environments as compared to accessible video-based alternatives and, (2) the visuo-haptic guidance during virtual welding enhances performance and provides a realistic learning experience to users. Using the solution, we expect inexperienced users to achieve competencies faster and be better prepared to enter actual work environments.
Exploring Spatial UI Transition Mechanisms with Head-Worn Augmented Reality

Imagine in the future people comfortably wear augmented reality (AR) displays all day, how do we design interfaces that adapt to the contextual changes as people move around? In current operating systems, the majority of AR content defaults to staying at a fixed location until being manually moved by the users. However, this approach puts the burden of user interface (UI) transition solely on users. In this paper, we first ran a bodystorming design workshop to capture the limitations of existing manual UI transition approaches in spatially diverse tasks. Then we addressed these limitations by designing and evaluating three UI transition mechanisms with different levels of automation and controllability (low-effort manual, semi-automated, fully-automated). Furthermore, we simulated imperfect contextual awareness by introducing prediction errors with different costs to correct them. Our results provide valuable lessons about the trade-offs between UI automation levels, controllability, user agency, and the impact of prediction errors.
How Will VR Enter University Classrooms? Multi-stakeholders Investigation of VR in Higher Education

VR has received increased attention as an educational tool and many argue it is destined to influence educational practices, especially with the emergence of the Metaverse. Most prior research on educational VR reports on applications or systems designed for specified educational or training objectives. However, it is also crucial to understand current practices and attitudes across disciplines, having a holistic view to extend the body of knowledge in terms of VR adoption in an authentic setting. Taking a higher-level perception of people in different roles, we conducted a qualitative analysis based on 23 interviews with major stakeholders and a series of participatory design workshops with instructors and students. We identified the stakeholders who need to be considered for using VR in higher education, and highlighted the challenges and opportunities critical for VR current and potential practices in the university classroom. Finally, we discussed the design implications based on our findings. This study contributes a detailed description of current perceptions and considerations from a multi-stakeholder perspective, providing new empirical insights for designing novel VR and HCI technologies in higher education.
RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.
New Chaotic System: M-Map and Its Application in Chaos-Based Cryptography

One of the applications of dynamical systems with chaotic behavior is data encryption. Chaos-based cryptography uses chaotic dynamical systems as the basis for creating algorithms. The present article discusses a new dynamical system called M-map with its analysis: fixed points, bifurcation diagram, Lyapunov exponent, and invariant density. The obtained bifurcation diagram and the plot of the Lyapunov exponent (with a minimum value of ln2 and a maximum value of ln4) suggest that the so-called robust chaos characterizes this map. Moreover, the obtained results are compared with other dynamical systems used in cryptography. Additionally, the article proposes a new image encryption algorithm. It uses, among others, cyclically shifted S-box or saving encrypted pixels on the first or last free space in the cipher-image. The conducted analysis shows that the cipher-images are characterized by an entropy value close to 8, a correlation of adjacent pixels value close to 0, or values of Number of Pixel of Change Rate (NPCR) and Unified Average Changing Intensity (UACI) measures close to 100% and 33%, respectively.
Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition

Recent works have achieved remarkable performance for action recognition with human skeletal data by utilizing graph convolutional models. Existing models mainly focus on developing graph convolutional operations to encode structural properties of a skeletal graph, whose topology is manually predefined and fixed over all action samples. Some recent works further take sample-dependent relationships among joints into consideration. However, the complex relationships between arbitrary pairwise joints are difficult to learn and the temporal features between frames are not fully exploited by simply using traditional convolutions with small local kernels. In this paper, we propose a motif-based graph convolution method, which makes use of sample-dependent latent relations among non-physically connected joints to impose a high-order locality and assigns different semantic roles to physical neighbors of a joint to encode hierarchical structures. Furthermore, we propose a sparsity-promoting loss function to learn a sparse motif adjacency matrix for latent dependencies in non-physical connections. For extracting effective temporal information, we propose an efficient local temporal block. It adopts partial dense connections to reuse temporal features in local time windows, and enrich a variety of information flow by gradient combination. In addition, we introduce a non-local temporal block to capture global dependencies among frames. Our model can capture local and non-local relationships both spatially and temporally, by integrating the local and non-local temporal blocks into the sparse motif-based graph convolutional networks (SMotif-GCNs). Comprehensive experiments on four large-scale datasets show that our model outperforms the state-of-the-art methods. Our code is publicly available at https://github.com/wenyh1616/SAMotif-GCN.