Reviewerly/exHarmony huggingface dataset:
Adaptive query compilation in graph databases

Abstract Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and exploit characteristics of modern hardware. Particularly for graph database queries, which often execute the exact instructions for processing, this technique can lead to an improvement. Furthermore, compilation frameworks like LLVM provide powerful optimization techniques and support different backends. However, the time for generating and optimizing machine code becomes an issue for short-running queries or queries which could produce early results quickly. In this work, we present an adaptive approach integrating graph query interpretation and compilation. While query compilation and code generation are running in the background, the query execution starts using the interpreter. When the code generation is finished, the execution switches to the compiled code. Our evaluation of the approach using short-running and complex queries show that autonomously switching execution modes helps to improve the runtime of all types of queries and additionally to hide compilation times and the additional latencies of the underlying storage.
A prior knowledge-driven reference signal extraction network towards measured data in DRM-based passive radar

We propose a novel intelligent reference signal extraction system for DRM-based passive radar. By fully using prior knowledge of the received signal, a pilot conditional reference signal extraction network (PCRSENet) is designed to replace both conventional channel estimation and signal equalization. The PCRSENet estimates the channel state information (CSI) implicitly and outputs reference signals directly. Meanwhile, what makes it challenging to utilize deep learning in reference signal extraction of passive radar is not only the limited measured data but also the truth transmission signals and CSI being unknown. So, the least square (LS) estimator-aided training sets generation method is employed, which obtains the CSI from measured data by LS estimator as prior knowledge and then simulates DRM signals as accurate labels of training sets. We evaluate the proposed method on measured data, and the experimental results show that the proposed method works effectively for target detection in DRM-based passive radar with different working parameters when the training and test sets are in the same transmission mode.
Remote arts therapy in collaborative virtual environment: A pilot case study

Motivated by the unique experience of creating three-dimensional artworks in virtual reality (VR) and the need for teletherapy due to the global pandemic, we conducted this pilot case study to explore the feasibility and effectiveness of using a custom-designed collaborative virtual environment (CVE) to enable remote arts therapy. Three participants (two females and one male) experiencing moderate to high stress as measured by the Perceived Stress Scale (PSS) joined this study. Each participant had eight 45-minute one-on-one sessions with the therapist for eight consecutive weeks. These eight sessions covered eight art creation themes and were delivered following pre-designed protocols. The CVE was the only medium to facilitate the sessions, during which the therapist and the participants were physically separated into two rooms. The quantitative and qualitative results suggested that the CVE-enabled approach was generally feasible and was welcomed by both the participants and the therapist. However, more evidence of the approach’s effectiveness in enhancing the participants’ mental wellbeing is needed because the results of the pilot case study were affected by the pandemic. The advantages and disadvantages of this approach and the CVE were investigated from practicality and technological affordance perspectives. Potential improvements to the CVE are also proposed to better facilitate the practice of remote arts therapy in CVE. We encourage future studies to cautiously investigate CVE-enabled remote arts therapy in clinical settings and collect more evidence regarding its effectiveness in addressing clinically diagnosed mental disorders and other complications.
Simulation and experiment on obstacle avoidance control of concrete pump truck boom based on improved danger field and gradient projection method

Aiming at the shortcomings of the traditional robot obstacle avoidance algorithm applied directly to the leader–follower task transformation of concrete pump truck boom, an algorithm combined with improved danger field and improved gradient projection method for obstacle avoidance control of the boom is proposed. In the method, a joint limit avoidance function is used to avoid the angle overrun of the boom joints. And the danger field expression is modified to improve its engineering suitability. Moreover, a smoothing adjustment factor is introduced to improve the smoothness of the leader–follower task transformation of the boom. The influence of key control parameters on the safety and accuracy of boom movement is discussed through the simulation, and the reasonable ranges of the parameters are given. Furthermore, experiments are carried out to verify the effectiveness of the algorithm. The method provides a solution for obstacle avoidance control of construction machinery with multijoint series boom.
Prologica and the National Informatics Policy

The purpose of this article is to talk about Prologica Computadores, one of the largest microcomputer and peripheral manufacturing companies in the 1980s, which had its peak during the Brazilian Market Reserve (Federal Law 7232/84), its main products, R&amp;D, reverse engineering, and the entire structure of several complementary companies.
Linear-space S-table algorithms for the longest common subsequence problem

Given two sequences A and B of lengths m and n, respectively, the consecutive suffix alignment (CSA) problem is to compute the longest common subsequence (LCS) between A and each suffix of B. The data structure of two-dimensional matrix for solving the CSA problem is named S-table. The S-table would be infeasible due to its quadratic-space requirement for long sequences. The linear-space S-table, proposed by Alves et al. (2005), consists of the first row of the S-table and the changes between every two consecutive rows. However, there is no further discussions and practical applications for the linear-space S-table. This paper proposes algorithms for improving three problems, related to LCS and S-table, by using the linear-space S-table. Suppose that A = A(1)A(2) (concatenation of two substrings), and we are given the S-table of A(2) and B, as well as the alignment result (LCS length) of A(1) to each prefix of B. The concatenated LCS (CoLCS) problem is to find the alignment result of A and B. For the CoLCS problem, this paper proposes an O(n)-time algorithm with the technique of set union-find to achieve the linear space. Next, for merging two linear S-tables, we propose an O(nlog⁡n)-time algorithm with the technique of range query and update to improve the quadratic-time algorithm using quadratic-space S-tables. Then, we propose an O(n)-time algorithm to update the linear-space S-table of A and B to obtain the linear-space S-table of Aσ and B, where σ denotes a new character appended to the tail of A. For further applications to long sequences by using the linear-space S-table, the algorithms proposed in this paper are essential and necessary.
Characterization of continuous experimentation in software engineering: Expressions, models, and strategies

Continuous Experimentation (CE) has become increasingly popular across industry and academic communities. Major software organizations use CE to increase their revenue by adding value to end-users, and researchers are investigating the CE adoption process and usage to expand its success. Given this rapid evolution, observing a shared understanding of CE definitions, processes, and experiment strategies is difficult, potentially jeopardizing new implementations and focused research efforts. To characterize CE from the perspective of its definitions, processes, and strategies for experimentation available in the technical literature and to evolve the understanding perspectives for "continuous experimentation" and "data-driven development" definitions. To select and analyze sources of information in the technical literature dealing with different aspects of continuous experimentation through a Literature Study using an ad hoc search improved with snowballing (backward and forward). Organize the findings into new perspectives for CE definitions, processes, and experiment strategies. It was possible to identify many different definitions, processes, and experimental strategies used to describe CE in the 72 analyzed empirical papers, making it difficult to decide on their combination to be applied in a real software development project. Therefore, it has been proposed to evolve the CE understanding perspective, to categorize its experiment strategies, and to offer a combined development process for CE combining parts of other processes. Besides, conjectural requirements have been identified, which can contribute to better differentiating requirements and hypotheses in the CE context. Likely, a better understanding of CE is still missing. It can contribute towards organizing a common taxonomy to facilitate the possible choices for the experiment strategies. Therefore, there is space for more investigations on its applicability and value in different categories of software systems, despite all the advancements of CE and its promotion in developing modern software systems.
Using Machine Learning Algorithms to Alleviate the Shilling Attack in a Recommendation System

Abstract: Every e-commerce site must have a referral system. Shilling attacks, One of the biggest issues with recommendation systems is those that involve the creation of fake profiles in the system and biased ratings of things. They reduce the accuracy and improve the performance of the recommender system when making suggestions to users. The goal of attackers is to change the order of materials or objects that match their interests. Shilling attacks threaten the reliability of RS. Therefore, to maintain their validity and fairness, recommender systems must be able to detect shilling attacks. So far, suitable algorithms and methods have been presented for the detection of shilling attacks. Some of these approaches, though, either use low-order interactions or higher-order interactions, or they examine the rating matrix from a single point of view. This study aimed to propose a mechanism using users' rating matrix, rating time, and social network analysis output of users' profiles by Gaussian-Rough neural network to simultaneously use low-order and high-order interactions to detect shilling attacks. Finally, several experiments were conducted with three models: CNN, DNN, and RNN compared with the Proposed Model. The results indicated that the proposed method is more effective than the comparison methods regarding attack detection and overall detection, which proves the effectiveness of the proposed model.
Human Action Recognition using Deep Learning

Abstract: The aim of this project is to recognize human actions for monitoring and security purposes. This project is mainly focused on building a system that is helpful for doctors to monitor patients .Human Action Recognition is required to recognize a set of human activities by training a supervised learning model and displaying the activity/action result as per the input action received. It has wide range of applications such as patient monitoring system, ATM/ Bank security system, etc. Human Action Recognition model can be mainly used for security and monitoring purposes. We can use various machine learning and deep learning algorithms for this project . One of the best approach is using CNN .
Fraud Detection in Credit Card Automated System using ML with AWS SageMaker

Abstract: The article discusses the need for the widespread adoption of payment systems, which has been driven by advances in technology. However, the issue of fraud remains a major concern for financial institutions, as there is no one-size-fits-all approach to detecting and preventing fraudulent transactions. Machine learning has been identified as a potential solution to this problem, but it requires the development of a reliable automated system capable of handling large volumes of data in realtime. In the article, the author details the structure and setup of an automated fraud detection system for payment systems that relies on a web service hosted on the cloud. The deployment of this system is justified by utilizing Amazon Web Services as the platform, which includes Amazon Fraud Detector and Amazon A2I task type to authenticate and validate forecasts that are deemed high-risk. One instance of developing a system for detecting anomalies on Amazon DynamoDB streams is presented by utilizing AWS SageMaker, AWS Glue, and AWS Lambda. The software product aims to prevent and detect fraud in payment systems, with a rapid detection time and integration with various business institutions. The article also highlights the importance of developing a specific methodology for implementing the software product for fraud detection in payment systems.
Design of security management model for communication networks in digital cultural consumption under Metaverse – The case of mobile game

Under Metaverse, security management of communication networks in digital cultural consumption has attracted more and more attention. As we know, security risks of communication networks caused by non-human factors are becoming weaker and weaker due to technology optimization. Meanwhile, players play significant and increasing role in communication networks. Taking mobile games as an example, this paper designed and applied the security management model to improve security management of communication networks in digital cultural consumption under Metaverse. The results show that: (1) The physiological, psychological, pathological, pharmacological, physical and psychosocial factors exert tremendous influence on communication networks. The psychosocial factors are more prominent. (2) There is security threshold in communication networks. It means that the security state can be entered if security threshold is crossed. This paper designs security threshold as the III level (5.5 ≤ P < 6.5). (3) This paper proposes 3E rules (That is, engineering, education, enforcement). It is beneficial to deal with security risks in communication networks. (4) After two security evaluations, the result is G2 = 6.70 > G1 = 5.01. This confirms the effectiveness of the security management model.
Deep Learning-Based Adaptive Online Intelligent Framework for a Blockchain Application in Risk Control of Asset Securitization

Blockchain and distributed ledger technologies have attracted massive attention from both legal communities and businesses. Asset securitization is the procedure in which an issuer designs a financial instrument that is marketable by combining or merging different financial assets into one group. However, most securitization occurs with loans and other assets that generate receivables, such as consumer or business debt of various types. This article discusses the possible benefits of blockchain during the securitization process using the deep learning-based adaptive online intelligent framework (DLAOIF). The benefits can be significant, from reduced costs, time, and fraud risks to increased safety, trust, and accuracy. Tracking financial assets on a blockchain can reduce dependence on credit rating organizations and allow investors to monitor asset performance and the associated risk more carefully. It should improve investor confidence and increase secondary market interest.
Cross-Project Transfer Learning on Lightweight Code Semantic Graphs for Defect Prediction

A deep learning system (DLS) developed based on one software project for defect prediction may well be applied to the related code on the same project but is usually difficult to be applied to new or unknown software projects. To address this problem, we propose a Transferable Graph Convolutional Neural Network (TGCNN) that can learn defects from the lightweight semantic graphs of code and transfer the learned knowledge from the source project to the target project. We discuss how the semantic graph is constructed from code; how the TGCNN can learn from the graph; and how the learned knowledge can be transferred to a new or unknown project. We also conduct a controlled experiment to evaluate our method. The result shows that despite some limitations, our method performs considerably better than existing methods.
Teachers' Technology-Enhanced Remote Teaching Competency in an Arctic Ubiquitous Pedagogical Context

This article presents the findings of a survey (N = 35) conducted among teachers working above the Arctic Circle in Northern Finland, specifically in the Sámi Domicile Area educational institutions (the Utsjoki, Enontekiö, Inari, and Sodankylä municipalities). The survey's goal was to determine what types of premises instructors worked in when working in online environments, as well as what types of developmental needs they indicated. The survey was designed around a technology integration framework that focuses on and combines technological, pedagogical, and content knowledge, and technological-pedagogical content knowledge (TPACK) by Mishra and Koehler for the effective and meaningful integration of technology in teaching and learning activities. The findings suggest that digital education is still being implemented at a variety of levels in Northern schools and that greater efforts should be made to ensure that appropriate techniques and equal learning opportunities are available to all students.
The Influence of Digital Currency Popularization and Application in Electronic Payment Based on Data Mining Technology

The rapid development of science and technology has provided us with great convenience, and the payment methods are gradually enriched. On the basis of traditional electronic payment, digital currency has emerged as the times require. As a strategic currency, it is very necessary to supervise the market status during its whole life. In this paper, the authors propose a scenario payment identification model based on RNN network to identify different scenarios of electronic payment for the intelligent analysis of digital currency. On this basis, PSO is adopted to optimize the initial parameters of the model to improve the accuracy of model identification. Finally, the high-precision identification of different electronic payment means in multiple scenarios was completed, providing detailed data reference for the development of electronic payment market and digital currency, thus providing an objective basis for policy formulation and adjustment in the next stage.
Privacy-preserving Amalgamated Machine Learning for Virtual Metrology

Standard machine learning approaches rely on having access to all of the data relevant to a problem, but this does not map well to real-world situations comprising different entities that each have access to a subset of the information, but who do not wish to make all of their data available to each other due to commercial, legal or regulatory issues. Such cases can occur in fabs. Using data from the MADEin4 project, we show how a privacy-preserving augmented machine learning (PAML) approach can be applied to building virtual metrology models by combining data from two collaborating entities that have access to different aspects of the relevant data, and who wish to build the virtual metrology model whilst retaining control of their own data. The results show that the privacy-preserving PAML model nearly matches the performance of a model built from the pooled data, and significantly outperforms the models built using only the data available to individual entities.
A domain-region based evaluation of ML performance robustness to covariate shift

Abstract Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpected performance of the learned model in deployment. The issue in which the training and test data inputs follow different probability distributions while the input–output relationship remains unchanged is referred to as covariate shift. In this paper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariate shift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function of the input data to assess the classifier’s performance per domain region. Distributional changes were simulated in a two-dimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on the experimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing the lowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the results reveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that the models exhibit high bias toward the region with high density in the input space domain of the training samples.
Constructing a Demand-Driven College English Learning Environment in Higher Education Institutions

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Construction of A Multi-Dimensional Achievement Evaluation System for Students in Higher Vocational Colleges

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.