Reviewerly/exHarmony huggingface dataset:
The Impact of Electronic Money on Monetary Policy: Based on DSGE Model Simulations

Starting with the interactive relationship between electronic money and household consumption stimuli, this paper deeply analyzes the changes in the behavior of each monetary subject under the impact of electronic money, and establishes a DSGE model based on the three economic sectors of family, commercial bank and central bank under the New Keynesian framework. On this basis, the impact of electronic money on savings, loans, output and the interest rate, and its impact on monetary policy, are described by numerical simulation. The simulation results show that: (1) electronic money has asymmetric effects on savings and loans, but an irrational deviation on households; (2) the influence of electronic money on the interest rate has a reverse effect, and the “inverse adjustment” of the interest rate increases the management difficulty of the micro subject to a certain extent, and affects the effectiveness of monetary policy; (3) the regulatory effect of price monetary policy is better than that of quantitative monetary policy, and electronic money has the effect of its risk restraining impact. Finally, based on the analysis, this paper gives policy recommendations.
Decision Support System for Real-Time People Counting in a Crowded Environment

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Improving Weakly Supervised Object Localization via Causal Intervention

The recently emerged weakly-supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels. Previous works endeavor to perceive the interval objects from the small and sparse discriminative attention map, yet ignoring the co-occurrence confounder (e.g., duck and water), which makes the model inspection (e.g., CAM) hard to distinguish between the object and context. In this paper, we make an early attempt to tackle this challenge via causal intervention (CI). Our proposed method, dubbed CI-CAM, explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps thus improving the accuracy of object localization. Extensive experiments on several benchmarks demonstrate the effectiveness of CI-CAM in learning the clear object boundary from confounding contexts. Particularly, on the CUB-200-2011 which severely suffers from the co-occurrence confounder, CI-CAM significantly outperforms the traditional CAM-based baseline (58.39% vs 52.4% in Top-1 localization accuracy). While in more general scenarios such as ILSVRC 2016, CI-CAM can also perform on par with the state of the arts.
The EFIS Framework for Leveraging Agile Organizations Within Large Enterprises

Abstract This article presents the design and application of the EFIS framework that combines four pillars to foster agile and lean working in organizations within large enterprises. These pillars constitute the empowerment of teams, the focus on products, the integration of processes, and the scaling of knowledge. The framework is designed to systematically address typical large enterprise challenges such as governance of regulation requirements and product risks. By design, EFIS is lean and nimble to make it easily adaptable to domain-specific demands within large organizations. It can be used as a stand-alone approach to establish and continuously improve lean and agile organizations, as well as in combination with existing approaches like SAFe®.
An Adaptive Memetic Algorithm for the Joint Allocation of Heterogeneous Stochastic Resources

This article investigates the joint allocation problem of stochastic resources (JASRs), which is widely found in complex systems. A general mathematical model for joint allocation of multiple heterogeneous stochastic resources is built, capturing the interdependencies between resources, quantity constraints, capability constraints, and strategy constraints of resources. An adaptive memetic algorithm (MA) is proposed for JASR, and the multipermutation encoding method is developed to denote assignment schemes of different resources. Multiple permutation-based operators are employed in the mutation and local search process under the genetic evolution framework and learning framework, respectively. Besides, a hybrid initialization method and an adaptive replacement strategy are put forward. Moreover, a restart strategy is developed to rebuild the population to maintain the genetic diversity. Twenty-five random test instances are produced to validate the effectiveness of the proposed MA. The results of computational experiments and the Wilcoxon rank-sum test demonstrate that these JASR instances can be well handled by the proposed adaptive MA, and the proposed MA is able to provide remarkably better decision schemes for the majority of the test instances than the prevailing solution methods.
Fault-tolerant adaptive tracking control of Euler-Lagrange systems – An echo state network approach driven by reinforcement learning

Reinforcement learning (RL) has enjoyed considerable success in application to nonlinear systems. However, very few RL-based works that explicitly address the control problem of MIMO nonlinear systems with subject to actuator failures. In this work, we develop a fault-tolerant adaptive tracking control method fused with an echo state network (ESN) driven by reinforcement learning for Euler-Lagrange systems subject to actuation faults. The proposed control includes an associative search network (ASN), a control gain network (CGN), and an adaptive critic network (ACN), with ASN to estimate the unknown items of the control system, CGN to deal with the time-varying and unknown control gains matrix, and ACN to generate the reinforcement signal, all together ensuring stable tracking and accommodate modeling uncertainties and actuation failures. Different from traditional reinforcement learning controllers that utilizes radial basis function neural networks (RBFNN) or fuzzy systems, the proposed one adopts an echo state network, a paradigm of recurrent neural networks, to implement the ASN, ACN and CGN, resulting in enhanced learning capabilities and stronger robustness against external uncertainties and disturbances, thus better control performance.
Efficient Search Over Encrypted Medical Data With Known-Plaintext/Background Models and Unlinkability

In advanced health care systems, the patients’ medical data can be outsourced to cloud servers to enable remote healthcare service providers to access and analyze patients’ data from any location to provide better treatment. However, outsourcing sensitive medical data makes data owners, i.e., patients, concerned about their privacy because private companies run the cloud service and the data can be accessed by them. Therefore, it is important to encrypt the data in the form of documents before outsourcing them to the cloud in a way that enables a data user, i.e., a doctor, to search over these documents without allowing the cloud provider to learn any private information about patients. Several schemes have been proposed to enable search over encrypted medical cloud data to preserve patient privacy, but the existing schemes suffer from high communication/computation overhead because they are designed for a single-data-owner setting. Moreover, they are not secure against known-plaintext/background and linkability attacks, and do not allow doctors to customize their search to avoid downloading irrelevant documents. In this paper, we develop an efficient search scheme over encrypted data for multi-data-owner setting. To secure our scheme, the cloud server obtains noisy similarity scores and doctors de-noise them to download the most relevant documents. Our scheme enables doctors to prescribe search conditions to customize the search without revealing the conditions to the server. Our formal proof and analysis indicate that our scheme can preserve privacy and is secure against known plaintext/background and linkability attacks, and the results of extensive experiments demonstrate the efficiency of our scheme compared to the existing works.
History and nature of the Jeffreys–Lindley paradox

Abstract The Jeffreys–Lindley paradox exposes a rift between Bayesian and frequentist hypothesis testing that strikes at the heart of statistical inference. Contrary to what most current literature suggests, the paradox was central to the Bayesian testing methodology developed by Sir Harold Jeffreys in the late 1930s. Jeffreys showed that the evidence for a point-null hypothesis $${\mathcal {H}}_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> scales with $$\sqrt{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>n</mml:mi> </mml:msqrt> </mml:math> and repeatedly argued that it would, therefore, be mistaken to set a threshold for rejecting $${\mathcal {H}}_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> at a constant multiple of the standard error. Here, we summarize Jeffreys’s early work on the paradox and clarify his reasons for including the $$\sqrt{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>n</mml:mi> </mml:msqrt> </mml:math> term. The prior distribution is seen to play a crucial role; by implicitly correcting for selection, small parameter values are identified as relatively surprising under $${\mathcal {H}}_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> . We highlight the general nature of the paradox by presenting both a fully frequentist and a fully Bayesian version. We also demonstrate that the paradox does not depend on assigning prior mass to a point hypothesis, as is commonly believed.
Double paths network with residual information distillation for improving lung CT image super resolution

Medical image analysis is particularly important for doctors to differential diagnosis of diseases. Due to the outbreak of COVID-19, how to diagnose COVID-19 accurately has become a key issue. High-resolution lung CT images can provide more diagnostic information, so there is an urgent need to develop a super-resolution method to improve the resolution of medical images.In this paper, a method based on double paths with residual information distillation for medical images super resolution (DRIDSR) is established. In the low-frequency path, shallow convolutional network is used to get low-frequency features, while in the high-frequency path, a residual information distillation module (RIDM) is designed to obtain clearer high-frequency features. RIDM cascades multiple residual blocks, and uses the output of each residual block as the input of IDB for further information distillation. Finally, it merges the information left by multiple IDBs as output.The proposed method is tested on the public dataset COVID-CT. The DRIDSR reconstruction quality of the algorithm is higher than that of the SRCNN, ESPCN, VDSR, IMDN and PAN method (+2.21 dB, +2.41 dB, +1.42 dB, +0.43 dB, +0.54 dB improvement, respectively) at × 3 upscale factor and (+2.35 dB, +2.17 dB, +1.59 dB, +0.48 dB, +0.56 dB increase, respectively) at ×4 upscale factor. While the number of parameters and analysis time of our model are reduced.It is demonstrated that DRIDSR network can obtain better performance and better HR medical images than several state-of-the-art SR methods in terms of objective indicators and subjective evaluation.
Fuzzy-PID Strategy Based on PSO Optimization for pH Control in Water and Fertilizer Integration

In the process of crop growth, irrational fertilizer application methods have caused waste of fertilizer resources and water, as well as damaged the soil's structure.To puzzle the above problems out, the research constructs the model of water-fertilizer machine by gathering relevant parameters in the field.Considering the system's plenty of defects and combining it with the MATLAB/simulink system, such as non-linearity, time-varying, large inertia, uncertain mathematical model and severe lag, a fuzzy proportional integral differential control based on particle swarm optimization is proposed in this paper for water and fertilizer integration system.Primarily, the research is done for precision fertilization control of fertilizer integration system and water, and the parameters of fuzzy proportional integral differential gain that schedules controllers are optimized through a particle swarm algorithm.The effectiveness of the suggested controller has been validated by comparing with the control algorithms (proportional integral differential control, fuzzy proportional integral differential control) commonly applied in current fertilizer application systems.Simulation experiments for this research are devised through MATLAB/Simulink simulation platform.Significant improvement in the system's tuning capabilities by incorporating particle swarm algorithm in the hysteretic non-linear system.Eventually, four control algorithms are experimentally validated in this research at different pH values through Experiments čáre designed in the experimental field.The outcomes demonstrate that the control algorithm in this paper possesses better regulation effect, smaller overshoot, excellent stability and more inadequate rising steady state time compared with the previous controls, which can enables precise control of the fertiliser system.
DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task.
Smart, Commodified and Encoded: Blockchain Technology for Environmental Sustainability and Nature Conservation

We explore the implications of blockchain technology for conservation and environmental policy. Drawing on an analysis of 27 initiatives, we examine their goals, assumptions, visions and workings. We find that these initiatives do not yet form a coherent approach, there is too much variety in their environmental focus, and the role of blockchain technology in achieving their goals. However, they share a faith in environmental-commodity markets, a penchant for surveillance and upward accountability, and lack a critical analysis of the main causes of environmental problems. Blockchain initiatives are forming a growing community of praxis and deepen ongoing trends in neoliberal environmental governance, characterised by the increased commodification and global accounting, surveillance and marketisation of environmental goods, services and outcomes. We suggest these services and outcomes fail to challenge the actual root causes of environmental degradation. At the same time, they are not all necessarily flawed by these characteristics. They can render information held by communities financially valuable in ways those communities may find useful. Future research should focus on exploring whether blockchain initiatives may at least translate in concrete environmental outcomes and contribute to the well-being of natural resource managers.
Programming dynamic reconfigurable systems

DR-BIP is an extension of the BIP component framework intended for programming reconfigurable systems encompassing various aspects of dynamism. It relies on architectural motifs to structure the architecture of a system and to coordinate its reconfiguration at runtime. An architectural motif defines a set of interacting components that evolve according to reconfiguration rules. With DR-BIP, the dynamism can be captured as the inter-play of dynamic changes in three independent directions: (1) the organization of interactions between instances of components in a given configuration; (2) the reconfiguration mechanisms allowing creation/deletion of components and management of their interaction according to a given architectural motif; and (3) the migration of components between predefined architectural motifs which characterizes dynamic execution environments. The paper lays down the formal foundation of DR-BIP, illustrates its expressiveness on few examples and discusses avenues for dynamic reconfigurable system design.
Adaptive learning in university students’ opinions: Cross-border research

Contemporary education is often based on using e-learning courses, which have become a popular means of delivering didactic material to students. Among the main advantages mentioned is the potential possibility of creating individual ways of learning and teaching. The purpose of this article is to provide a description of the various approaches to adaptive learning, to present the comparative research results of a Polish-Ukrainian study, as well as to highlight the options offered by LMS Moodle (Modular object-oriented dynamic learning environment Learning Management System) for the implementation of adaptive learning and the possibility of taking into account the expectation of students regardless of the country. During the study, an online survey was administered to 59 students at the University of Silesia in Katowice (US), Poland and 121 students at the Borys Grinchenko Kyiv University (BGKU), Ukraine between March-June 2020. All of the students were studying online due to the pandemic caused by COVID-19. The classes were conducted in synchronous and in asynchronous modes using e-learning courses in the Moodle system, as well as MS Teams for Polish students and Google Suite for Ukrainian students. At the same time, the majority of the surveyed students declared that they lack personalization, both in terms of materials and the learning process, which was limited in terms of fulfilment and they would like to have a choice of the level of study.
AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation

In this letter, we present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments that uses a team of autonomous unmanned aerial vehicles (UAVs) with on-board RGB cameras and computation. Existing methods are limited by calibrated cameras and off-line processing. Thus, we present the first method (AirPose) to estimate human pose and shape using images captured by multiple extrinsically uncalibrated flying cameras. AirPose itself calibrates the cameras relative to the person instead of relying on any pre-calibration. It uses distributed neural networks running on each UAV that communicate viewpoint-independent information with each other about the person (i.e., their 3D shape and articulated pose). The person's shape and pose are parameterized using the SMPL-X body model, resulting in a compact representation, that minimizes communication between the UAVs. The network is trained using synthetic images of realistic virtual environments, and fine-tuned on a small set of real images. We also introduce an optimization-based post-processing method (AirPose$^{+}$) for offline applications that require higher MoCap quality. We make our method's code and data available for research at https://github.com/robot-perception-group/AirPose. A video describing the approach and results is available at https://youtu.be/xLYe1TNHsfs.
Data Preprocessing Combination to Improve the Performance of Quality Classification in the Manufacturing Process

The recent introduction of smart manufacturing, also called the ‘smart factory’, has made it possible to collect a significant number of multi-variate data from Internet of Things devices or sensors. Quality control using these data in the manufacturing process can play a major role in preventing unexpected time and economic losses. However, the extraction of information about the manufacturing process is limited when there are missing values in the data and a data imbalance set. In this study, we improve the quality classification performance by solving the problem of missing values and data imbalances that can occur in the manufacturing process. This study proceeds with data cleansing, data substitution, data scaling, a data balancing model methodology, and evaluation. Five data balancing methods and a generative adversarial network (GAN) were used to proceed with data imbalance processing. The proposed schemes achieved an F1 score that was 0.5 higher than the F1 score of previous studies that used the same data. The data preprocessing combination proposed in this study is intended to be used to solve the problem of missing values and imbalances that occur in the manufacturing process.
A Blockchain-Applied Personal Health Record Application: Development and User Experience

This study aims to introduce a novel blockchain-applied personal health records (PHR) application and validate its user experience. The system transmits the part corresponding to the patient’s personal information off-chain and prevents data forgery and falsification by storing encrypted data on-chain. Patients may easily trace the opt-in and opt-out history of their consent data and dynamically store the consent system for data exchange on the blockchain. A mixed-method study using a questionnaire, in-depth interviews, and usability evaluation were conducted for 30 participants. The system usability score was 74.0, indicating the high usability of the application. Those who were familiar with blockchain showed confidence in the application, but those unfamiliar wanted their data to be safe using another way. Most of the participants were interested in exchanging and using their medical data and considered security important but those unfamiliar wanted their data to be safe using another way. We found that participants were concerned about data security and considered a blockchain-based PHR as a novel way to store and exchange their medical information securely. Blockchain is not a visible technology. However, a blockchain-applied PHR must be able to win user trust through visualizations, certificates, and system descriptions.