Reviewerly/exHarmony huggingface dataset:
An Adaptive Contagion Mapping Methodology

In this paper we augment the CoMap methodology with an advanced set of regulatory distress and default constraints and banks’ behavioural rules which allows us to model and capture the implications for financial stability of dynamic banks’ responses. The result is an adaptive contagion mapping methodology (A-CoMap) - a network-based contagion model where banks dynamically adapt their short-term exposures to news about counterparty risk. The model is calibrated and tested on a unique dataset composed of granular bank and exposure level information on 2,800 consolidated banking groups worldwide in which less-significant institutions (network periphery) play a relevant role. First, we show how banks’ behavioural responses may either increase or decrease the degree of stress to some specific banks. Second, thanks to the multi-complex regulatory environment made of solvency/liquidity and distress/default constraints we shed light upon the effectiveness and calibration of minima and capital buffer requirements, and their trade-off. Third we provide new evidence of the too-many-too fail problem and we compare it with the too-big-too-fail issue. In the end to foster research in the field, which is often limited by the availability of confidential exposure-level data, we provide detailed statistics about the actual network of EA banks’ exposures and the set of calibrated parameters.
Corrigendum to “Asymptotic behavior of Bayesian learners with misspecified models” [J. Econ. Theory 195 (2021) 105260]

This note corrects errors in Esponda et al. (2021).
The Influence of Think Talk Write (TTW) Learning Model on Critical Thinking Ability of Class VII Students of MTs Al-Ikhlas Mowewe

This experimental study aims to determine the effect of the application of the TTW learning model on the critical thinking skills of seventh grade students of MTs. Al Ikhlas Mowewe. The population in this study were all students of class VII MTs. Al Ikhlas Mowewe Academic Year 2020/2021. Sampling using a saturated sample technique. Learning outcomes data on students' mathematics were collected by using the test instrument given at the posttest. The results showed that the average value of critical thinking skills of student in class VIIA (31 students) and class VIIB (31 students) were 75 and 65. So, it’s indicates that the critical thinking ability of students who were taught using TTW learning was higher than using conventional learning. Furthermore, the results of inferential statistics analysis on hypothesis testing using the t-test have obtained the value of , indicating that is rejected and is accepted, it means, there is a significant difference of critical thinking skills of students by TTW learning and conventional learning. So, the conclusion of this study is that the critical thinking ability of students who are taught with TTW learning has a more positive effect than the critical thinking ability of students who are taught by conventional learning in class VII MTs. Al Ikhlas Mowewe.
A Systematic Review of the Use of Blockchain in Higher Education

The purpose of this paper is to present a systematic review in order to identify and analyse applicable blockchain technology research associated with the field of higher education. Terms including ‘college’ and ‘blockchain’ were used to assist in narrowing the scope of the research. In addition, well-known scientific databases and search engines have been used to conduct the search, these include scopes, springer, science direct, ResearchGate and science open. In the field of information technology and education, these chosen scientific databases and search engines index high quality and high impact articles. The focus of the research was only related to high quality and influential journal publications that had been found in the books, magazines, international journals, seminars and proceedings of conferences. This paper presents a vital review of blockchain technology and delves into its value in the of education and its related aspects. This study will help researchers and practitioners in identifying the areas where blockchain can be adopted and implemented effectively. A summary of possible challenges faced when implementing the technology of blockchain in higher education especially in universities. Also, this study summarises the application of blockchain technology in education.
Write Recount Text Learning Using a Genre-Based Approach in German Literature Class

Writing is a complex and challenging language skill because writing involves cognitive, affective, and creative elements. Rich vocabulary, understanding of grammatical features, and other aspects of writing also really determine someone's success in writing. Writing skills must be taught in foreign language learning because through writing skills, students can express emotions, thoughts, desires, and events with appropriate symbols. One approach that can help students improve their writing skills is the genre-based approach. Through this approach, students learn to write using specific text genres, such as recount text. Recount text is a type of text that tells the past. This study recounts text because students had just learned one of the past tenses in German, namely preterite. By using a genre-based approach in the study, it was found that students were able to 1) Improve their ability to identify characteristics and differences between several types of recount text; 2) Pay more attention to the text's structural elements and language style in the process of reading and writing; 3) Find essential information in the text according to the elements of the genre; 4) Increase self-confidence in writing.
Landscape Fusion Method Based on Augmented Reality and Multiview Reconstruction

This paper proposes a fused landscape augmented reality method based on 3D model multiview reconstruction. Based on the principles related to augmented reality technology, the proposed method uses natural features of images for training and extraction, which solves the problems of convenience and aesthetics caused by artificial signs. By extracting and training natural features at different scales of the acquired images, Harris and FREAK algorithms are used to extract features and create binary descriptors for real-time acquired images. Feature matching is performed on the above two features to estimate the location where the reconstructed model will appear. At the same time, for the difficulties of 3D model reconstruction requiring relevant expertise and the defects of poor reconstruction effect, the SFM algorithm is used for multiview reconstruction of landscape models to realize the augmented reality fusion method of natural scenes and landscape models. After the experiments, the fusion achieved by this method works well, which proves that the method is feasible and has potential.
Scalable Distributed Target Tracking with Partially Observation and Limited Communication

Tracking dynamic targets with several autonomous agents is an essential issue for active information acquisition. There exist some solutions for unlimited observation and perfect communication capabilities, but these requirements are difficult to meet in physical world. We propose a distributed target tracking algorithm, which describes historical information with graphs, coordinates agents with communication, and balances tracking and searching with delicate rewards. Benefiting from these technologies, the algorithm can work under limited observations and poor communication conditions. In addition, the method can generalize to environments with various numbers of agents or targets without retraining. The effectiveness and generalization performance of the algorithm are verified in a distributed target tracking environment.
Ultra-Fast Approximate Inference Using Variational Functional Mixed Models

While Bayesian functional mixed models have been shown effective to model functional data with various complex structures, their application to extremely high-dimensional data is limited due to computational challenges involved in posterior sampling. We introduce a new computational framework that enables ultra-fast approximate inference for high-dimensional data in functional form. This framework adopts parsimonious basis to represent functional observations, which facilitates efficient compression and parallel computing in basis space. Instead of performing expensive Markov chain Monte Carlo sampling, we approximate the posterior distribution using variational Bayes and adopt a fast iterative algorithm to estimate parameters of the approximate distribution. Our approach facilitates a fast multiple testing procedure in basis space, which can be used to identify significant local regions that reflect differences across groups of samples. We perform two simulation studies to assess the performance of approximate inference, and demonstrate applications of the proposed approach by using a proteomic mass spectrometry dataset and a brain imaging dataset. Supplementary materials are available online.
Distributed Cluster Fusion Estimation Under Correlated Noise Conditions

In this paper, considering the problem of distributed cluster fusion estimation, a two-layer fusion structure is proposed for discrete time-varying linear systems with multiple sensors. In the first layer of the fusion structure, considering the noises correlation between process noise and measurement noises, as well as the mutual interference between measurement noises in the cluster, a centralized information filtering method is designed, and local state fusion estimations are obtained. In the second layer of the fusion structure, a distributed cluster fusion estimation algorithm is presented for the clusters, where the weight matrix is obtained under the minimum variance by the least square method. Finally, a numerical simulation of target tracking proves effectiveness of the proposed algorithm.
JointConf: Jointly autotuning configuration parameters for modularized graph databases

Abstract To support different application scenarios, graph databases (GDBs) usually provide a large number of performance‐related parameters for developers. Since manually configuring is both time‐consuming and cost‐intensive, automatically tuning configurations parameters to achieve a better performance has been an urgent need. Besides, considering various graph management requirements, GDBs begin to utilize the modular architecture to interoperate with a wide range of storage and index backends. Due to the complicated interactions among different modules, sequentially tuning each software with previous solutions may fall into a local optimal and it is necessary to jointly autotune the cross‐module configuration parameters. Toward this challenging target, we propose JointConf —a new black‐box approach of jointly autotuning configuration parameters for modularized GDBs. To address the formulated high‐dimensional black‐box optimization problem, JointConf utilizes the recently proposed BO_dropout algorithm. Inspired by the dropout algorithm in neural networks, BO_dropout explores efficient dimension dropout to achieve a high‐dimensional Bayesian optimization. We evaluate the effectiveness of JointConf on a local distributed JanusGraph cluster with three different graph query benchmark applications and experimental results show its advantages over the four baseline search‐based approaches. The necessity of jointly tuning for modularized GDBs is also verified in our experiments.
Autonomous Flight for Multi-UAV in GPS-Denied Environment

Autonomous Flight in GPS-denied Environment is a challenging task for multiple unmanned aerial vehicles (UAVs). That needs the UAVs perform simultaneous localization and mapping (SLAM) in GPS-denied environment and motion planning in environment populated with obstacles. To solve this problem, this paper proposes a framework for multi-UAV relative localization and motion planning. Specifically, the relative state estimation system is proposed for relative localization and mapping of multi-UAV in GPS-denied environments. And the motion planning system is proposed for multi-UAV trajectory planning, collision avoidance and improving the safety and clearance of the trajectory. A set of experiments show that the method proposed in this paper can perform efficient and accurate localization and motion planning for autonomous flight in GPS-denied environment.
Differentially private resilient distributed cooperative online estimation over digraphs

Abstract This article investigates resilient distributed online estimation (DOE) in unreliable directed networks with differential privacy requirements. In the network considered, some agents are subject to Byzantine attacks and thus could send arbitrary incorrect messages to their neighbors. The remaining agents aim to collaboratively estimate the value of an unknown vector parameter while protecting their private data. In this article, by adding private noises to mask the estimate, a stochastic approximation‐type resilient differentially private DOE algorithm is proposed to protect the privacy of sensitive information. A time‐varying step size is introduced to attenuate the divergence caused by the private noise, and furthermore, guarantees the convergence of the algorithm. When the directed graph is +1)‐robust, the algorithm is shown to be both mean square and almost sure convergence in the sense of ‐differential privacy. A simulation example is given to verify the effectiveness and superiority of the algorithm.
Multi-UAV Cooperative Moving Target Search Based on Improved Pigeon-Inspired Optimization

Aiming at the problem of multi-UAV cooperative moving target search, a cooperative search decision based on improved pigeon-inspired optimization is designed. Firstly, based on the independence of moving targets, a target probability information graph model with normal distribution is established. In order to improve the certainty of the presence of the target in the environment, the information graph of the certainty of the search environment is established. Secondly, the attractant and repulsive digital pheromone graphs are established to guide the UAV to fly to the unsearched area, reducing the probability of repeated search and improving the efficiency of collaborative target search. Based on the traditional pigeon-inspired optimization, it is improved by adding speed update and correction mechanism and elite generation mechanism. Then, combining with the existence probability information of the target in the environment and the detection information of the UAV searching for the target, the improved pigeon-inspired optimization is used to determine the optimal searching flight path of the UAV. Finally, the feasibility and effectiveness of the improved pigeon-inspired optimization for the collaborative search of moving targets are verified by setting different search flight step sizes and using different search algorithms in simulation experiments.
Design of Blended Teaching Model Based on Emotion Recognition and Language Learning

With the vigorous development of Internet technology, great changes have taken place in all aspects of human society. This change is also having an increasingly significant impact on the education sector. It is even a trend to subvert the tradition. This also makes the student's identity a passive recipient of knowledge. At the same time, the final orientation of China's education model is the result of examinations, and there is little guidance for students' interest. Of course, traditional teaching also has the ability to enable students' subject knowledge to be systematically established, and to communicate with teachers in the teaching process, which can improve students' learning efficiency in the classroom. In the face of the information explosion in today's society and the rapid development of the above-mentioned technologies, the change of students' learning mode of knowledge has also ushered in an opportunity to change. The purpose of this paper is to study the establishment of a blended teaching model that combines traditional classrooms with network applications. With the help of the characteristics of network big data, it transforms students' passive learning identities, and combines offline traditional learning classrooms with online learning. In addition to the advantages mentioned above, the advantage of online learning is that some network science and technology can be used for the online learning platform to serve the entire teaching process. Therefore, this paper proposes a blended teaching model based on the network platform for students' emotion recognition and language learning result analysis. And from the experimental results in this paper, it can be seen that the recognition evaluation rate of HTMC, the feature of emotion recognition, is 71.52% and the recognition frequency of ETMC is 73.89%. The above two recognition parameters can better reflect the emotional changes in the mixed teaching process.
Exploring Combinatorics and Graph Theory with Simple Blokus

SummaryIn this article, we examine a simplified version of the game Blokus. We count the number of completed boards in this game, discussing how the count can be examined in terms of both combinatorics and graph theory, and provide takeaways for undergraduate mathematics.
A Cooperative Search Method for Multiple UAVs in a Complex Environment with No-Fly Zone Areas

In conditions of the unknown generation time and positions of the search targets, a multi-UAV cooperative search algorithm with obstacle avoidance capability is proposed for the dynamic environment with no-fly zones. First, an improved artificial potential field method is presented for UAVs to effectively avoid no-fly zones or obstacles and falling into local minimum points during path planning process. Second, inspired from the idea of territory division among animal population in nature, centroidal Voronoi partition method is employed to divide target search area for each UAV. Then, by integrating the improved artificial potential field method with the centroidal Voronoi partition method, multi-UAV system can complete the collaborative search task in the dynamic environment with no-fly zones. The numerical simulation verifies the feasibility and effectiveness of the proposed method.
Research on Target and Firepower Allocation of Multi-platform Anti-ship Missiles Under Constraints

According to the possible new situation in the future sea battlefield, under the cooperative operation of multi-platform anti-ship missiles. A route planning method based on geometric method is used for route planning, and a firepower distribution scheme that can maximize firepower as much as possible is put forward, and a model of firepower distribution optimization problem is established. Aiming at this model. An improved genetic algorithm is adopted to solve the problem that genetic algorithm is easy to fall into local optimal solution. Simulation results verify the effectiveness of this method.
Dominating the Wild: How Have Companies Been Leveraging Cryptoeconomics to Shape Markets Through Business Model Innovation?

A recent emerging market that has impacting companies, customers, and society is the one embedded in Cryptoeconomics. However, although promising, management research has been scarce in drawing the Business Model Innovation (BMI) based on Cryptoeconomics as a new market, including its theoretical underpinnings. Acknowledging both the research gap and the practical relevance of market representations and business models in the market shaping phenomenon, we conduct a pioneering empirical study to investigate how forward-looking firms have practiced Cryptoeconomics to shape new markets through BMI. As significant contributions, (i) we provide a conceptual framework to clarify the market shaping process led by companies that devise Cryptoeconomics as a strategic tool for BMI and, finally, (ii) we unfold the market practices associated with the market shaping process enacted by multiple case studies, including how these practices are interlinked as socio-material phenomena in the light of BMI constructs.
Polynomial Fuzzy Control of an Underwater Robot Based on Gaussian Processes

This paper proposes a novel hybrid control strategy for underwater robots whose system dynamics are partially unknown and possibly changing. Given multiple prior system matrices at various operating points, the sparse online Gaussian process (SOGP) technique is deployed to predict the residual unknown system dynamics from input-output data. The posterior means of Gaussian processs (GPs) are utilized to compensate unknown system dynamics. The sum-of-squares (SOS) technique is applied to construct a state feedback controller and derives the related sufficient stability condition using a polynomial fuzzy control framework. The simulation result indicates the effectiveness of our GP-based polynomial fuzzy control approach for underwater robot trajectory tracking.
Sophisticated methods for noise filtering, subgroup discovery, and classification in big data analysis

Big Data is a popular research area where a vast amount of data is created, replicated, and consumed by society. The quality of the data used directly influences big data knowledge discovery. The existence of noise is the most prevalent problem influencing data quality. The following techniques were developed to reduce noise in data with a distributed setting: Homogenous Ensemble for Big Data (HME-BD) and Heterogeneous Ensemble for Big Data (HTE-BD). In this article, the performance of HTE-BD is improved further by developing Enhanced HTE-BD (EHTE-BD), which combines Logistic Regression based Support Vector Machine (LR-SVM) in conjunction with RF, LR, and KNN to reduce noisy data. Furthermore, the Multi-Objective Evolutionary Fuzzy Method for Subgroup Discovery throughout Big Data (MEFASD-BD) was used to resolve the multi-objective optimization challenge, and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was utilized to handle the rising dimensionality issue through subgroup discovery. To address the NSGA-II’s slow convergence rate, an Improved Multi-Objective Meta-Heuristic Fuzzy approach for discovering subgroups in big data is described, that contains a meta-heuristic method for subgroup discovery known as the Multi-Objective Differential Search Algorithm (MODSA). It selects the most relevant subgroups from vast amounts of data, reducing the data’s dimensionality. The Fuzzy Deep Neural Network (FDNN) classifier assesses the main subgroups. By removing noisy data and selecting the most relevant subgroups, the performance of FDNN in classifying vast amounts of data is improved.
Research on the Multi-Screen Connection Interaction Method Based on Regular Octagon K-Value Template Matching

Manufacturing companies using CPSS integrate multiple data in organizational production and present the data in the form of data visualization with the help of a big screen data visualization. In order to help the manufacturing enterprises using the data visualization screen to understand the data trend of the whole production process more conveniently, this paper proposes a method to establish the connection and interaction between the visualization screen and the mobile terminal based on the positive octagon K-value template matching algorithm, match the pre-processed mobile phone pictures with the visual large screen image, determine the area where the captured image is located in the large screen image, and return the detailed information of chart components contained in the visual large screen area to the mobile phone for users’ observation and analysis. This matching algorithm has good matching accuracy and time efficiency. Application cases and related investigations in enterprises also prove the practicability of this method to a certain extent.