Reviewerly/exHarmony huggingface dataset:
Central Bank Digital Currency: One, Two or None?

This paper considers the motives, modalities, and possible consequences of central bank digital currency (CBDC) issuance. It starts by drawing a distinction between a wholesale CBDC (WCBDC), accessible only to financial intermediaries, and a retail CBDC (RCBDC), accessible to the general public. The issuance of one could be dissociated from the other, implying the possibility of one, two, or no CBDC(s). The main motive for issuing a WCBDC could be to promote financial innovation and to lower transactions costs using a blockchain. The motives for issuing a RCBDC could be to supply the public with a digitalised monetary instrument without any liquidity or credit risks, easy to access and cheap to use. A CBDC would be created or destroyed only by the central bank and would be issued and exchanged at par with other forms of central bank money (banknotes and reserves). A WCBDC would have to be issued on a permissioned rather than a public blockchain. It would also have to be remunerated to keep reserves and the WCBDC at par. A RCBDC would not necessarily use the blockchain and would most likely involve intermediaries. The issuance of a CBDC would represent a supply shock, which would support economic growth in the medium to long run and could transitorily weigh on prices. One consequence of issuing a WCBDC could be the development of an intraday market, which could in turn lead to the adoption of a real-time monetary policy. Furthermore, the issuance of a RCBDC could put a floor to bank deposit rates and, if it is remunerated, raise them. If the RCBDC were not remunerated, the effective lower bound would be raised to zero and the effectiveness of asset purchases by the central bank could be diminished. If it were, the interest and exchange rate channels should be strengthened. The remuneration of the RCBDC would thus seem to create a trade-off between the effectiveness of monetary policy and the cost of bank intermediation.
A Distributional Approach to Controlled Text Generation

We propose a Distributional Approach to address Controlled Text Generation from pre-trained Language Models (LMs). This view permits to define, in a single formal framework, “pointwise” and “distributional” constraints over the target LM --- to our knowledge, this is the first approach with such generality --- while minimizing KL divergence with the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-Based Model) representation. From that optimal representation, we then train the target controlled autoregressive LM through an adaptive distributional variant of Policy Gradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the initial LM (GPT-2). We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study, we show the effectiveness of our adaptive technique for obtaining faster convergence.
Language-Agnostic Representation Learning of Source Code from Structure and Context

Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine learning models have relied predominantly either on Structure or Context. We propose a new model, which jointly learns on Context and Structure of source code. In contrast to previous approaches, our model uses only language-agnostic features, i.e., source code and features that can be computed directly from the AST. Besides obtaining state-of-the-art on monolingual code summarization on all five programming languages considered in this work, we propose the first multilingual code summarization model. We show that jointly training on non-parallel data from multiple programming languages improves results on all individual languages, where the strongest gains are on low-resource languages. Remarkably, multilingual training only from Context does not lead to the same improvements, highlighting the benefits of combining Structure and Context for representation learning on code.
Development of Temperature and Humidity Control System in Internet-of-Things based Oyster Mushroom Cultivation

Oyster mushrohoms are a kind of mushrooms that have high nutritional content and medicinal properties. This plant can be cultivated using planting media with the proper composition and the certain temperature as well as humidity. This paper describes the design and implementation of a prototype of temperature and humidity control system in the oyster mushroom cultivation based on the internet-of-things framework to obtain the good quality mushrooms. Controlling is carried out by utilizing DHT-11 sensor, Arduino Uno microcontroller, MCU ESP8266, and the internet-of-things (IoT) platforms which are called Cayenne. The temperature is maintained between 22°C-28°C and humidity of 60%-80% through continuous monitoring remotely. The Cayenne application installed on the desktop computer or an Android-based mobile phone provides data on temperature and humidity at all times. Experimental results show that monitoring and controlling temperature and humidity can be done well through the Cayenne application so that the whole system realizes the concept of IoT.
Letter-Level Online Writer Identification

Writer identification (writer-id), an important field in biometrics, aims to identify a writer by their handwriting. Identification in existing writer-id studies requires a complete document or text, limiting the scalability and flexibility of writer-id in realistic applications. To make the application of writer-id more practical (e.g., on mobile devices), we focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues. Unlike text- $$\backslash $$ document-based writer-id which has rich context for identification, there are much fewer clues to recognize an author from only a few single letters. A main challenge is that a person often writes a letter in different styles from time to time. We refer to this problem as the variance of online writing styles (Var-O-Styles). We address the Var-O-Styles in a capture-normalize-aggregate fashion: Firstly, we extract different features of a letter trajectory by a carefully designed multi-branch encoder, in an attempt to capture different online writing styles. Then we convert all these style features to a reference style feature domain by a novel normalization layer. Finally, we aggregate the normalized features by a hierarchical attention pooling (HAP), which fuses all the input letters with multiple writing styles into a compact feature vector. In addition, we also contribute a large-scale LEtter-level online wRiter IDentification dataset (LERID) for evaluation. Extensive comparative experiments demonstrate the effectiveness of the proposed framework.
Filter bubbles and digital echo chambers 1

Filter bubbles and echo chambers are often named as key drivers of political polarisation and societal fragmentation. The conceptualisation of echo chambers and filter bubbles and their relationship is still subject to an ongoing academic debate. For echo chambers, the constituting affordances of online communication mainly pertain to online social networks. The large-scale, multi-platform studies all show that the prevalence of filter bubble and echo chamber effects among the majority of the population is low. In the review of literature on filter bubbles and echo chambers, it became clear that both concepts are frequently operationalised as ideological bubbles. The mismatch of theoretical work and empirical work on echo chambers and filters bubbles might also be a manifestation of a lack of explicit normative research in the field. It can be argued that ideas and attitudes expressed in fringe bubbles hold higher news value because of their novelty and extremity.
Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.
Supply Chain Inventory Collaborative Management and Information Sharing Mechanism Based on Cloud Computing and 5G Internet of Things

With the development of economic globalization, the competition among enterprises is increasingly fierce. Therefore, companies need close information sharing to realize the integration of supply chain. This article aims to study the collaborative management and information sharing mechanism of supply chain inventory based on cloud computing and 5G Internet of Things. This article first introduces the theory and methods of collaborative supply chain management and the information exchange mechanism and then discusses the problem of information sharing in the supply chain, that is, the bullwhip phenomenon, and then from the demand forecast, supply chain structure, time lag, and shortage game, six aspects are analyzed. The cause of the bullwhip phenomenon is analyzed. Secondly, this article proposes a quantitative analysis of the bullwhip effect, establishes a mathematical model of the bullwhip effect in the supply chain, and uses quantitative analysis to analyze the value of information sharing in the supply chain. Finally, this article uses cloud computing technology to build a supply chain information collaboration system architecture and uses EPC Internet of Things to build a supply chain information sharing model and describes the entire operation process of the supply chain. The experimental results of this paper show that the application of cloud computing technology to supply chain management establishes a system platform for supply chain information sharing, improves the overall operational efficiency of supply chain management, and realizes supply chain information sharing and business collaboration. In addition, the operating costs and risks of each node enterprise in the supply chain are reduced by 12% compared with the nonsharing situation, which also shows that the overall benefits of the supply chain have been correspondingly improved and market competitiveness has been enhanced.
Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network

In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural networks (GNN). GCN, namely, graph convolution networks, are neural network models in GNN. GCN extends the convolution operation from traditional data (such as images) to graph data, and it is essentially a feature extractor, which aggregates the features of neighborhood nodes into those of target nodes. In the process of aggregating features, GCN uses the Laplacian matrix to assign different importance to the nodes in the neighborhood of the target nodes. Since graph-structured data are inherently non-Euclidean, we seek to use a non-Euclidean mathematical tool, namely, Riemannian geometry, to analyze graphs (networks). In this paper, we present a novel model for semi-supervised learning called the Ricci curvature-based graph convolutional neural network, i.e., RCGCN. The aggregation pattern of RCGCN is inspired by that of GCN. We regard the network as a discrete manifold, and then use Ricci curvature to assign different importance to the nodes within the neighborhood of the target nodes. Ricci curvature is related to the optimal transport distance, which can well reflect the geometric structure of the underlying space of the network. The node importance given by Ricci curvature can better reflect the relationships between the target node and the nodes in the neighborhood. The proposed model scales linearly with the number of edges in the network. Experiments demonstrated that RCGCN achieves a significant performance gain over baseline methods on benchmark datasets.
RETRACTED: Application of artificial intelligence in physical education

The development of artificial intelligence is accompanied by the progress of human society. Purpose of the article is to using artificial intelligence in coordination of physical education teaching in middle and higher schools to solve the problems of traditional physical education models and improve modern physical education of overall operating efficiency. In this paper, the research methods of artificial intelligence are applied to physical education teaching. In order to understand the advantages and application methods of artificial intelligence, the application prospect and development trend of artificial intelligence in modern physical education technology are further explored. And the experiment is studied by the conventional research method, taking 400 students in 11 colleges and universities as the research object, and using decision tree algorithm to analyze the overall, systematic and development perspective. A comprehensive and in-depth study of influencing factors of practical teaching of public physical education. The result shown that for the use of public ROTOWIRE data, the practical application proposed in this article can significantly increase the actual attention of physical education and teaching to 90%, while increasing the participation of 70% of students.
Deciphering Bitcoin Blockchain Data by Cohort Analysis

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.
Mitigating Psychological Trauma on Adult Burn Patients Based on Virtual Reality Technology of Smart Medical Treatment

Virtual reality (VR) is one of the hot spots in the computer network world in recent years, which has attracted more and more people’s attention. This study mainly explores the effect of mitigating the psychological trauma of adult burn patients based on the VR technology of smart medical treatment. First, the EEG data are sent to the data processing module through a wireless protocol; then, the data processing module denoises the EEG data and performs feature extraction and feedback parameter calculation; after that, these parameters will be sent to the VR interaction engine; based on this, these parameters change the VR scene to capture and reflect the physiological activities of the patient’s brain in real time; finally, the patient uses the VR scene content presented by the real-time feedback of the captured EEG signal as a guide to making self-adjustment in time, and the electrical signal of captured brain at this time is again transmitted to the next work cycle and continues to feed back and present new VR interactive scenes to guide and intervene in the patient’s self-regulation behavior. The VR feedback training module is responsible for receiving the characteristic data calculated from the EEG acquisition and processing module and converts it into parameter variables that control the VR intervention system. The system user adjusts the state according to the feedback information displayed in the VR scene and generates new EEG signals to promote the realization of self-adjustment. The biofeedback training based on EEG feeds back the intuitive EEG state to the patient, prompting them to learn how to realize self-regulation and achieve the purpose of adjusting the level of mental health. The degree of itching and pain in the VR treatment group was alleviated, and compared with the normal training treatment results, it was statistically significant ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&gt;</mo> <mn>0.05</mn> </math> ). This study has a positive effect on the psychological intervention of patients with facial injuries.
Shading-based absolute phase unwrapping

Absolute phase unwrapping in the phase-shifting profilometry (PSP) is significant for dynamic 3-D measurements over a large depth range. Among traditional phase unwrapping methods, spatial phase unwrapping can only retrieve a relative phase map, and temporal phase unwrapping requires auxiliary projection sequences. We propose a shading-based absolute phase unwrapping (SAPU) framework for in situ 3-D measurements without additional projection patterns. First, the wrapped phase map is calculated from three captured images. Then, the continuous relative phase map is obtained using the phase histogram check (PHC), from which the absolute phase map candidates are derived with different fringe orders. Finally, the correct absolute phase map candidate can be determined without additional patterns or spatial references by applying the shading matching check (SMC). The experimental results demonstrate the validity of the proposed method.
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning

Few-Shot Learning (FSL) is a challenging task, i.e., how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then predicting novel classes via a cosine nearest neighbor classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usually biased. In this paper, we attempt to diminish the prototype bias by regarding it as a prototype optimization problem. To this end, we propose a novel meta-learning based prototype optimization framework to rectify prototypes, i.e., introducing a meta-optimizer to optimize prototypes. Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, i.e., the mean-based gradient estimation is also biased on sparse data. To address the issue, we regard the gradient and its flow as meta-knowledge and then propose a novel Neural Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes, called MetaNODE. In this meta-optimizer, we first view the mean-based prototypes as initial prototypes, and then model the process of prototype optimization as continuous-time dynamics specified by a Neural ODE. A gradient flow inference network is carefully designed to learn to estimate the continuous gradient flow for prototype dynamics. Finally, the optimal prototypes can be obtained by solving the Neural ODE. Extensive experiments on miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our method.
An adaptive chaotic sparrow search optimization algorithm

Sparrow Search Algorithm (SSA) is a new type of swarm intelligence optimization algorithm recently proposed. Compared with other intelligent algorithms, this algorithm has better effect, but still has the problems of slow convergence speed, insufficient solution accuracy and easy to fall into local optimum. In response to these problems, an improved sparrow search algorithm (ISSA) is proposed. Based on the sparrow search algorithm, the improved Tent chaotic map is used to initialize the population to increase the diversity of the population and improve the global search ability; then an adaptive local search strategy is introduced to update the location of the discoverer and select the best location to enhance Local search ability to avoid the population from falling into the local optimum; next, the Cauchy mutation method is used to update the optimal individual position and find feasible solutions to improve the search accuracy and optimization ability; finally, through the simulation experiment of 8 benchmark functions and compare with three basic Comparing the algorithm with an improved sparrow algorithm, the results show that the ISSA algorithm has better results in convergence speed, solution accuracy and global optimization ability than the other four algorithms.
SimFaaS: A Performance Simulator for Serverless Computing Platforms

Developing accurate and extendable performance models for serverless platforms, aka Function-as-a-Service (FaaS) platforms, is a very challenging task. Also, implementation and experimentation on real serverless platforms is both costly and time-consuming. However, at the moment, there is no comprehensive simulation tool or framework to be used instead of the real platform. As a result, in this paper, we fill this gap by proposing a simulation platform, called SimFaaS, which assists serverless application developers to develop optimized Function-as-a-Service applications in terms of cost and performance. On the other hand, SimFaaS can be leveraged by FaaS providers to tailor their platforms to be workload-aware so that they can increase profit and quality of service at the same time. Also, serverless platform providers can evaluate new designs, implementations, and deployments on SimFaaS in a timely and cost-efficient manner. SimFaaS is open-source, well-documented, and publicly available, making it easily usable and extendable to incorporate more use case scenarios in the future. Besides, it provides performance engineers with a set of tools that can calculate several characteristics of serverless platform internal states, which is otherwise hard (mostly impossible) to extract from real platforms. We show how SimFaaS facilitates the prediction of essential performance metrics such as average response time, probability of cold start, and the average number of instances reflecting the infrastructure cost incurred by the serverless computing provider. We evaluate the accuracy and applicability of SimFaaS by comparing the prediction results with real-world traces from Amazon AWS Lambda.