Reviewerly/exHarmony huggingface dataset:
Publishing Cultural Heritage Collections of Ghent with Linked Data Event Streams

Cultural heritage institutions maintain digital artefacts of their collections using Collection Management Software (CMS). In order to attract new audiences, these data should be interoperable with and reusable within other Web APIs. In this article, we explain how we applied Flemish Linked Data Standards (OSLO) to make the data within the Axiell Collections CMS interoperable, and how we applied the method of Linked Data Event Streams (LDES) for making the data reusable. The LDES has been successfully adopted by third parties to then host subject pages, a SPARQL endpoint, a substring fragmentation for autocompletion purposes, and a IIIF enriched LDES. To this end, we see LDES as the core Web API of a CMS, allowing third parties to take up other querying and processing tasks on their own machines.
ReMeDi: Secure Electronic Health Records Sharing Using Blockchain

The following paper introduces a web application using blockchain technology that put forward an effortless solution for sharing sensitive medical records in a secure way. With the fast pace life involving work and degrading environmental conditions, we are subjected to various health issues. These health issues generate a lot of health records. Health records store a lot of critical information that is private to individuals. Medical records are currently handled manually by individuals which have security and efficiency-related issues. Thus, the web application provides instantaneous tracking of medical records which improves security, increases efficiency, and reduces overall cost. It will help infrequent distribution of information among health care providers, insurance companies, pharmacists, researchers, patient families leading to ease in data sharing, improving decision-making for medical care, and maintaining accessibility and authenticity. This paper highlights this application as it works on two different levels: patient and the doctor levels. While patients can use it for uploading their records, granting access to the doctors, and viewing their records, doctors can use it to view the records of the patients who have given access to them.
Crypto-Market Network In-Formation and Bitcoin Price Dynamics and Tail Behaviour

This study explores the role of network dynamics in the Bitcoin market. We assess whether and to what extent cryptocurrency market network connectivity predict Bitcoin returns across quantiles of the return distribution. The facets of cryptocurrency market network connectivity we consider include (i) connectivity between the on- and off-chain segments of the Bitcoin market network, (ii) the intensity of network correlations between cryptocurrencies, and the (iii) intensity and synchronization of social and traditional crypto-focussed media activity (iii). We identify tail behavior determinants and predictors employing a quantile regression approach. The results demonstrate the effectiveness of the connectivity measures in dynamically explaining and predicting both price spikes and downfalls.
Data-Free Neural Architecture Search via Recursive Label Calibration

This paper aims to explore the feasibility of neural architecture search (NAS) given only a pre-trained model without using any original training data. This is an important circumstance for privacy protection, bias avoidance, etc., in real-world scenarios. To achieve this, we start by synthesizing usable data through recovering the knowledge from a pre-trained deep neural network. Then we use the synthesized data and their predicted soft labels to guide NAS. We identify that the quality of the synthesized data will substantially affect the NAS results. Particularly, we find NAS requires the synthesized images to possess enough semantics, diversity, and a minimal domain gap from the natural images. To meet these requirements, we propose recursive label calibration to encode more relative semantics in images, as well as regional update strategy to enhance the diversity. Further, we use input and feature-level regularization to mimic the original data distribution in latent space and reduce the domain gap. We instantiate our proposed framework with three popular NAS algorithms: DARTS, ProxylessNAS and SPOS. Surprisingly, our results demonstrate that the architectures discovered by searching with our synthetic data achieve accuracy that is comparable to, or even higher than, architectures discovered by searching from the original ones, for the first time, deriving the conclusion that NAS can be done effectively with no need of access to the original or called natural data if the synthesis method is well designed. Code and models are available at: https://github.com/liuzechun/Data-Free-NAS .
Leveraging Customer Reviews for E-commerce Query Generation

Abstract Customer reviews are an effective source of information about what people deem important in products (e.g. “strong zipper” for tents). These crowd-created descriptors not only highlight key product attributes, but can also complement seller-provided product descriptions. Motivated by this, we propose to leverage customer reviews to generate queries pertinent to target products in an e-commerce setting. While there has been work on automatic query generation, it often relied on proprietary user search data to generate query-document training pairs for learning supervised models. We take a different view and focus on leveraging reviews without training on search logs, making reproduction more viable by the public. Our method adopts an ensemble of the statistical properties of review terms and a zero-shot neural model trained on adapted external corpus to synthesize queries. Compared to competitive baselines, we show that the generated queries based on our method both better align with actual customer queries and can benefit retrieval effectiveness.
Analysis of Deep Learning Techniques for Handwritten Digit Recognition

The huge variations in culture, community and language have paved the path for a massive diversification in the handwriting of humans. Each one of us tends to write in a different pattern. Character or digit recognition finds humongous applications in the recent days especially in the processing of bank statements, sorting of postal mails and many more. Although many classification models exist in literature that successfully classifies the handwritten digits, yet the problem that is still persisting is which one can be termed as an optimal classification model with higher accuracy and lower computational complexity depending upon the circumstances. In this paper, the Machine Learning classification models involving the likes of K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and XGBOOST were compared with that of Deep Learning models like Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). The comparison clearly portrays how convolution operation on images plays a vital role to outperform rest of the classification models. Then the paper compares the CNN models on the basis of two different sets of Loss functions and Optimizers to delineate their role in enhancement of accuracy of the model. The only limitation lies in the fact that in spite of being a handwritten recognizing model this model only recognizes digits in the range of 0–9.
Representing Regular Languages of Infinite Words Using Mod 2 Multiplicity Automata

Abstract We explore the suitability of mod 2 multiplicity automata (M2MAs) as a representation for regular languages of infinite words. M2MAs are a deterministic representation that is known to be learnable in polynomial time with membership and equivalence queries, in contrast to many other representations. Another advantage of M2MAs compared to non-deterministic automata is that their equivalence can be decided in polynomial time and complementation incurs only an additive constant size increase. Because learning time is parameterized by the size of the representation, particular attention is focused on the relative succinctness of alternate representations, in particular, LTL formulas and Büchi automata of the types: deterministic, non-deterministic and strongly unambiguous. We supplement the theoretical results of worst case upper and lower bounds with experimental results computed for randomly generated automata and specific families of LTL formulas.
Phonon-mediated many-body quantum entanglement and logic gates in ion traps

The high-fidelity multi-ion entangled states and quantum gates are the basis for trapped-ion quantum computing. Among the developed quantum gate schemes, Mølmer-Sørensen gate is a relatively mature experimental technique to realize multi-ion entanglement and quantum logic gates. In recent years, there have also been schemes to realize ultrafast quantum entanglement and quantum logic gates that operate outside the Lamb-Dicke regime by designing ultrafast laser pulse sequences. In such a many-body quantum system, these entanglement gates couple the spin states between ions by driving either the phonon energy level or the motional state of the ion chain. To improve the fidelity of quantum gates, the modulated laser pulses or the appropriately designed pulse sequences are applied to decouple the multi-mode motional states. In this review, we summarize and analyze the essential aspects of realizing these entanglement gates from both theoretical and experimental points of view. We also reveal that the basic physical process of realizing quantum gates is to utilize nonlinear interactions in non-equilibrium processes through driving the motional states of an ion chain with laser fields.
Regexexplainer: Automatic Description Generation for Regular Expressions Via Transformer

Regular expressions can describe specific matching rules, which can be used to determine the string format or extract the string content. Until now, regular expressions have been widely used in various operating systems and have been supported by most programming languages. However, the understanding of regular expressions is challenging for end-users or developers, who may not familiar with the syntax of regular expressions. Therefore, it is urgent for researchers to propose effective methods to automatically generate natural language descriptions for regular expressions. In this study, we formalize the description generation of the regular expression as a neural machine translation problem and then propose a fully data-driven method RegexExplainer based on Transformer. Specifically, it uses Regex Encoder and AST Encoder to jointly learn the encoding information of regular expressions. Then, it additionally introduces adversarial training to improve the robustness of the trained model. To verify the effectiveness of RegexExplainer, we construct a high-quality corpus, which contains 107,851 pairs of regular expressions and corresponding natural language descriptions. Experimental results show that our proposed method can achieve better performance than state-of-the-art baselines in terms of three performance measures. Moreover, A human study can also verify the effectiveness of our proposed method. Finally, we develop a browser plug-in based on RegexExplainer to help users understand the purpose of regular expressions.
Evaluating the effectiveness of quantum leadership skills training on thinking style and knowledge sharing behavior of school principals

Evaluating the effectiveness of quantum leadership skills training on thinking style and knowledge sharing behavior of school principals
Discuss on Application of High Frequency Electro-magnetic Compatibility Test of Digital Instrument and Control Equipment in Nuclear Power Plant

With the development of computer and network communication technology, especially the deep integration of information technology and industrial automation, wireless communication and 5G equipment have been fully into the practical application of nuclear power plants. The working frequency of such equipment is getting higher and higher, which makes the electro-magnetic environment level of the nuclear power plants deteriorate. However, instrumentation and control systems usually using digital technology integrate circuits and microelectronic modules. When in such electro-magnetic environment, the relevant qualification tests and measures previously carried out may not be able to cover the actual environment and are easily affected by the high frequency interference. Therefore, additional measures such as supplementary qualification, shielding, and distance restriction are required.
Few-Shot Generative Learning by Modeling Stereoscopic Priors

Few-shot image generation, which aims to generate images from only a few images for a new category, has attracted some research interest in recent years. However, existing few-shot generation methods only focus on 2D images, ignoring 3D information. In this work, we propose a few-shot generative network which leverages 3D priors to improve the diversity and quality of generated images. Inspired by classic graphics rendering pipelines, we unravel the image generation process into three factors: shape, viewpoint and texture. This disentangled representation enables us to make the most of both 3D and 2D information in few-shot generation. To be specific, by changing the viewpoint and extracting textures from different real images, we can generate various new images even in data-scarce settings. Extensive experiments show the effectiveness of our method.
A Dynamic Bayesian Model for Causal Inference with Mediation

Many marketing research projects aim not only to estimate the causal effect of an intervention but also to understand the mechanism by which the intervention affects outcomes. However, existing causal inference methods, such as difference-in-differences and synthetic control, do not include mediation analysis, nor do existing mediation analysis methods address unobserved confounders. The authors therefore propose an integrative, scalable framework for performing mediation analysis while estimating causal treatment effects with panel data. The proposed method decomposes the causal effect of an intervention into indirect effects, transmitted through the model’s mediators, and a direct effect, not transmitted through the mediators. Simulation studies confirm that the proposed method correctly identifies the direct, indirect, and total treatment effects and outperforms existing causal inference methods in inferential quality. Two real-world applications yield further insights. For Washington state’s 2010 soda tax, despite a persistent, negative indirect effect on sales through inflated prices, the tax’s total effect is transient, due to a direct effect that was initially negative but became positive later on, offsetting the price effect. In the case of U.S. newspaper paywall adoption, paywalls decreased newspaper circulation through increased print prices, but a direct spillover effect dominated the indirect price effect. Moreover, newspaper size positively moderated the spillover effect, such that only large newspapers enjoyed the positive spillover effect and therefore larger circulation.
Prediction of Increased Mobility of Yogyakarta Residents in Controlling the Spread of COVID-19 Cases Using the Neural Network Algorithm

High mobility in D.I Yogyakarta arises from tourist visits and educational activities as well as community activities which are quite dense. High mobility and density will affect the spread of COVID-19 in D.I Yogyakarta. Early preparation is needed to predict displacement in D.I Yogyakarta so that policies can be implemented as early as possible to prevent the emergence of a new wave of spread of COVID-19 in D.I Yogyakarta. In this study, the Neural Network Algorithm with Backpropagation model is used to predict the mobility of Yogyakarta D.I data from September 2021 to January 2022. To form the Neural Network Algorithm model using Rapidminer software with prediction criteria of accuracy and kappa. The prediction accuracy value obtained is 95.49% and the kappa value is 0.908. The results of this study indicate that the predictive value of Yogyakarta D.I mobility tends to be high.
Detect and Track the Motion of Any Moving Object Using OpenCV

This paper represents the moving object that can detect and track in a computer system with the help of OpenCV and image processing. In this paper, we found that a video picture is moving and that moving object is detected using OpenCV and the detected picture has been represented in frames with the help of contour by computer vision (CV) in a computer system. Detecting and recognizing an object is the initial stage of image systems in computer vision. Therefore, is a real-time identification of tracking a large moving object system using open computer vision (CV). In future work we focus on the security surveillance system to improve the influence of moving object detection using OpenCV.
Representation Learning of World Models and Estimation of World Model of Others Using Graph2vec

To realize advanced interaction between autonomous robots and users, it is important for robots to aware the difference in their state space representations (i.e., world models). As a first step toward this goal, we propose a method to estimate user's world model based on queries. In our method, the agent learns distributed representation of world models by graph2vec and generates concept activation vectors (CAVs) that represent the meaning of queries in latent space. The experimental results show that our method can estimate user's world model more efficiently than the simple method using ``AND'' search of queries.
A Lightweight Api Recommendation Method for App Development Based on Multi-Objective Evolutionary Algorithm

Context: API is important in daily programming activities during app development, but finding appropriate APIs is time-consuming for developers. To simplify this process, many researchers pay attention to overcoming the task-API knowledge gap using semantic information mined from the large-scale dataset for API recommendation. However, only semantic information is not enough since API descriptions and developer's query may not share similar words, meanwhile, large-scale data mining brings huge time cost and causes knowledge fixed problem (i.e. data cannot be timely updated). This limits the efficiency of existing API commendation methods. Objective: In this work, we aim at proposing a lightweight API recommendation method for app development based on the data in app store. Method: We model API recommendation as a multi-objective optimization problem by considering both structural and semantic information of API, and use the genetic algorithm to gain optimal solutions for overcoming limitations of the small-scale dataset. Specially, we collect APK files of related apps and API descriptions to establish a lightweight dataset, and extract the structural and semantic information of API from them with graph embedding and NLP techniques respectively. Then, we get the recommended APIs satisfying structural and semantic objectives according to the developers’ target functionality with a genetic algorithm. Finally, we give the interpretation of our recommendation information to help developers understand the results easily. Results and Conclusion: We conduct a series of experiments based on apps in Google Play and Q&As in Stack Overflow. The results show that Precision@N of our recommendation can be up to 0.89, MAP@N is up to 0.50, and MRR reaches 0.69, and our method achieves such good performance only utilizing less than 1/19 of the project number of our compared machine learning method. Besides, we conduct a survey and most of the participants confirm the understandability and usability of our recommended APIs in practice.
Development and Test of Navigation System for Unmanned Carrier Platform in Greenhouse

In order to solve the navigation and positioning problem of unmanned carrier platform in facility greenhouse environment, aiming at the problems and shortcomings of existing schemes, this paper proposes a facility greenhouse unmanned carrier platform navigation system combining electromagnetic line and encoder. The system uses electromagnetic line arranged above the travel path of facility greenhouse for navigation, The position data of the electromagnetic line node mark and the encoder of the vehicle body are fused for accurate positioning, which realizes the autonomous navigation, real-time positioning and autonomous operation of the unmanned carrier platform in the facility greenhouse environment. The prototype is tested in the laboratory environment to test the navigation deviation and positioning accuracy at different speeds. The test shows that when the prototype runs at the speeds of 0.5 m/s, 1.0 m/s and 1.5 m/s respectively, the maximum absolute value of the horizontal deviation between the actual running track and the ideal track is 5cm, and the maximum positioning accuracy error is 7 cm. Meet the requirements of trajectory deviation and positioning accuracy.
Key Technologies of English Document Grammar Recognition System with Cloud Computing and Electronics Systems

This paper studies the key technologies of English document grammar recognition system with the cloud computing and electronics systems. The security of the platform relative to the user refers to the user’s information security guarantee on the cloud computing platform; hence, at the initial design of the proposed model, the electronics system is combined for the overall performance enhancement. Characters have a special line structure, which can basically be classified as the horizontal, vertical, and oblique line combination. Hence, we consider the text mining model and the neural networks model to finalize the framework. The experimental results compared with the latest ones have shown the robustness.
Mining Negative Sequential Rules from Negative Sequential Patterns

As an important tool for behavior informatics, negative sequential rules (NSRs, e.g., $${<}ab{>}\Rightarrow \lnot {<}cd{>}$$ ) are sometimes much more informative than positive sequential rules (PSRs, e.g., $${<}ab{>}\Rightarrow \ {<}cd{>}$$ ), as they can provide valuable decision-making information from both negative and positive sides. Very limited NSR mining algorithms are available now and most of them discover NSRs only from positive sequential patterns (PSPs, e.g., $${<}abcd{>}$$ ) rather than from negative sequential patterns (NSPs, e.g., $${<}a \lnot b c \lnot d{>}$$ ), which may result in a loss of important information. However, discovering NSRs (e.g., $${<}a \lnot b{>}\Rightarrow {<}c \lnot d{>}$$ ) from NSPs is much more difficult than mining NSRs from PSPs because NSPs do not satisfy the downward closure property. In addition, it is very difficult to find which kind of NSRs should be generated. This paper proposes a novel algorithm named nspRule to address all these difficulties. The experiment results on real-life and synthetic datasets show that nspRule can mine NSRs correctly and efficiently w.r.t. several aspects including rule number, runtime, memory usage and scalability.