Reviewerly/exHarmony huggingface dataset:
Adaptive Feature Selection for Google App Rating in Smart Urban Management: A Big Data Analysis Approach

MostUrban management feature selectionFeature selection algorithms use a similarity matrix to assign a fixed value to pairs of objects. However, if the dataset is large and includes noisy or unlabeled samples, they couldGoogle produce incorrect or inaccurate conclusions. This chapter refers to a single target prediction analysis on Google’s App ratingRating. This aspect is relevant to smart urban managementUrban management and systems and could help optimize data analysis for multiple uses. After conducting necessary experimentsExperiment, the findings from this chapter show that the adaptive feature selectionAdaptive feature selection (e.g., Random ForestRandom Forest) gives optimized resultsTraditiona comparedTraditional to traditionalTechniques feature selection techniquesFeature selection, such as ‘Linear Discriminant AnalysisLinear discriminant analysis’ and ‘Principal Component Analysis’. The results from this comparison help future research in big data analysis, particularly for urban management and smart urbanMachine learning systems.
Improve the Daily Societal Operations Using Credit Fraud Detection: A Big Data Classification Solution

Credit fraud detectionCredit fraud detection grows rapidly with the development of electronic payment. It is part of the smart management in daily societal operations. IEEE-CISIEEE_CIS Fraud DetectionFraud detection dataset was raised for technique innovation to tackle this problem. For the consideration of the size of the available fraud dataset, this chapter proposes a solution with the combination of the big data techniqueTechniques and the machine learning algorithms. Aside from the raised pipeline of the solution, other identified works include a comparison of four implemented machine learningMachine learning algorithms, three surveys into the computing time with respect to the data size, number of executors, and number of cores. Findings from this chapter help finding solutions for more efficient credit fraud detectionFraud detection.
EVOLUTION OF REGULATORY MODELS FOR PUBLIC HEALTH DATA ECOSYSTEMS FROM A LINKED DEMOCRACY PERSPECTIVE

Public healthcare is a data-intensive environment that manages ever-increasing volumes of biomedical data resulting from medical data-generating technologies. In this paper, the authors discuss strategies to regulate the collection and use of biomedical data and metadata to build sustainable public health data ecosystems; this can assist citizens to get control of dataflows by defining identity in the public domain and shaping the capacity to use the web of data: get access to healthcare services and receive benefits and appropriate care. The authors suggest that a strategy based on the linked democracy governance model and safeguards, implemented through the meta-rule of law, enables better design of regulatory tools to handle semantically driven data flows. This strategy ties well in with models of deliberative and epistemic democracy, focused on relationships between people, data, and institutions. The authors investigate privacy, security, and data protection issues, applying existing ethical and legal frameworks for public health data and the theory of justice; they discuss the implementation of strategies to articulate the public domain and propose intermediate, anchoring institutions at the meso-level by building ontologies, selecting technical functionalities and algorithms, and embedding protections of the rule of law into specific public health data ecosystems.
AutoDebias based on NeuralCF

Recommendation systems rely on user behavior data (ratings, clicks, etc.) to build personalized models. Nevertheless, the data collected was always observational rather than experimental, which can lead to a variety of biases. Data deviations will directly affect the operation results of the recommendation system. Users may not favor the items recommended by the system to them, so this is an imperative problem that must be resolved as soon as possible. The majority of current efforts to debias recommendation systems focus only on one or two specific biases and are not universally applicable. As a result, Chen et al. and their group developed a general debiasing framework (AutoDebias) containing most of the debias strategies, which overcomes the problem of low flexibility of most of today's debiasing techniques. However, the model still has room for improvement. Chen et al. chose Matrix Factorization (MF) as the benchmark model for AutoDebias. In our study, however, we found that NeuralCF model may be a better alternative to MF. Therefore, the goal of our work is to replace the MF base model in AutoDebias with the NeuralCF model, and to conduct relevant experiments in order to validate our hypothesis. According to experiments, when using NLL as evaluation metrics, NeuralCF has better performance than MF, which improved 14.3% in final results.
Quantitatively examining the interaction between cybercrime and physical crime

This study examined the differences and relationships between reported incidents of cybercrime and physical crime within U.S. society nationally. The examined period encompassed the years between 2001 and 2020. The study outcomes showed that a relationship existed between reported incidents of cybercrime and reported incidents of physical crime (p = 0.00; α = 0.05). More specifically, it appeared that relationships existed between the reported incidents of cybercrime and the reported incidents of physical crimes representing robbery rate (p = 0.01; α = 0.05), burglary rate (p = 0.00; α = 0.05), and larceny theft rate (p = 0.00; α = 0.05). It also appeared that a difference (p = 0.00; α = 0.05) existed between reported incidents of cybercrime and physical crime, wherein greater quantities of physical crime were exhibited societally during the examined period.
A Model-Based Approach for Measuring Wavefront Aberrations Using Random Ball Residual Compensation

The projection objective lens holds a pivotal role in lithography, directly influencing imaging system quality and, consequently, the lithography machine’s feature dimensions. Optical inspection methods for this lens require advancements in calibrating systematic error and enhancing alignment precision of auxiliary devices, given their impact on calibration accuracy. In the random averaging method, random ball can give rise to additional wavefront aberrations due to misalignment and numerical aperture mismatch. To mitigate these aberrations and enhance the accuracy of systematic error calibration, this paper introduces a random ball residual compensation (RBRC) model. Additionally, when combined with the random averaging technique, it elevates the calibration accuracy of the measured lens’s wavefront aberrations. The experimental results underscore the method’s effectiveness, accurately determining optical component eccentricities and numerical aperture errors. After eliminating these errors, more accurate values of lens wavefront aberrations are achieved. This research significantly contributes to enhancing error calibration of lithography objective lens systems.
The atomic properties of stress

Abstract This article posits a theory of iterative stress that separates each facet of the stress map into its constituent parts, or ‘atoms’. Through the well-defined notion of complexity provided by Formal Language Theory, it is shown that this division of the stress map results in a more restrictive characterisation of iterative stress than a single-function analysis does. While the single-function approach masks the complexity of the atomic properties present in the pattern, the compositional analysis makes it explicitly clear. It also demonstrates the degree to which, despite what appear to be significant surface differences in the patterns, the calculation of the stress function is largely the same, even between quantity-sensitive and quantity-insensitive patterns. These stress compositions are limited to one output-local function to iterate stress, and a small number of what I call edge-oriented functions to provide ‘cleanup’ when the iteration function alone fails to capture the pattern.
A Unified SoS and System Architecture Modeling Framework Based on Grid-Type MBSE Methodology

There is dense and heterogeneous information exchange between the SoS, System architecture model, and it is meaningful to build a unified SoS and system architecture modeling framework to bridge the gap between the two different architecture level modeling. The paper studied the grid-type SoS/system architecture modeling methods and the two different unified architecture framework building approaches. A unified SoS and system architecture modeling framework that aligns with ISO 15288 is proposed. The modeling framework takes full advantage of MBSoSE and MBSE to save time and cost in the engineering practice.
Monitoring Key Pair Usage through Distributed Ledgers and One-Time Signatures

Private key management is a complex obstacle arising from the traditional public key infrastructure model. However, before any related security breach can be addressed, it must first be reliably detected. Certificate Transparency (CT) is an example of a certificate issuance monitoring strategy, developed to detect the possible malfeasance of certification authorities (CAs). To the best of our knowledge, CT and other detection mechanisms do not cover digitally signed documents made by an end user, which are also susceptible to CA misbehavior. We modify the CT framework to handle signed documents via logging certificates in the blockchain to enable the secure and user-friendly monitoring of one-time signatures, backdating protection, and effective CA misbehavior detection. Moreover, to demonstrate the feasibility of our proposal, we present distinct deployment scenarios and analyze the storage, performance, and monetary costs.
Class incremental learning of remote sensing images based on class similarity distillation

When a well-trained model learns a new class, the data distribution differences between the new and old classes inevitably cause catastrophic forgetting in order to perform better in the new class. This behavior differs from human learning. In this article, we propose a class incremental object detection method for remote sensing images to address the problem of catastrophic forgetting caused by distribution differences among different classes. First, we introduce a class similarity distillation (CSD) loss based on the similarity between new and old class prototypes, ensuring the model's plasticity to learn new classes and stability to detect old classes. Second, to better extract class similarity features, we propose a global similarity distillation (GSD) loss that maximizes the mutual information between the new class feature and old class features. Additionally, we present a region proposal network (RPN)-based method that assigns positive and negative labels to prevent mislearning issues. Experiments demonstrate that our method is more accurate for class incremental learning on public DOTA and DIOR datasets and significantly improves training efficiency compared to state-of-the-art class incremental object detection methods.
Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing

Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the context semantic information, suppress redundant information, and obtain the haze density image with higher detail. Finally, our method removes haze, preserves image color, and ensures image details. The proposed method achieved a PSNR score of 33.74, SSIM scores of 0.9843 and LPIPS distance of 0.0040 on the SOTS-outdoor dataset. Compared with representative dehazing methods, it demonstrates better dehazing performance and proves the advantages of the proposed method on synthetic hazy images. Combined with dehazing experiments on real hazy images, the results show that our method can effectively improve dehazing performance while preserving more image details and achieving color fidelity.
Practices and challenges of threat modelling in agile environments

Abstract Facing the increasing annual cybersecurity costs, threat modelling (TM) is a method to consider security as early as possible in the software development life cycle (SDLC). Thereby, TM helps to identify and address security-related design flaws in information systems. As the original TM approach is based on sequential development, it is not aligned with today’s predominantly agile environments. This results in several challenges. However, TM’s implementation in an agile development approach lacks the recommendations on how to tackle these challenges. Therefore, we assess the state-of-the-art of TM challenges and practices in agile environments by conducting a literature review covering 220 papers. Thereby, we identify nine categories of challenges and six categories of practices. We propose a valuable artefact for practitioners by mapping challenges and practices to the agile SDLC and by creating a matrix highlighting how the practices address the challenges of TM in an agile environment.
An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology

Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables. The TSML method utilizes a number of techniques to create an interpretable, hypothesis-driven framework for machine learning that can handle different nowcast and forecast lengths. Some of the techniques employed include:•Feature engineering to construct interpretable features, like site-specific lead times, hypothesized to be potential predictors of COVID-19 cases.•Feature selection to identify features with the best predictive performance for the tasks of nowcasting and forecasting.•Prequential evaluation to prevent data leakage while evaluating the performance of the machine learning algorithm.
Profitable entanglement for channel discrimination

We investigate the usefulness of side entanglement in discriminating between two generic qubit channels, up to unitary pre- and post-processing, and determine exact conditions under which it does enhance (as well as conditions under which it does not) the success probability. This is done in a constructive way by first analysing the problem for channels that are extremal in the set of completely positive and trace-preserving qubit linear maps and then for channels that are inside such a set.
Logic + probabilistic programming + causal laws

Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent. Probabilistic programming has rapidly emerged as a key paradigm to integrate probabilistic concepts with programming languages, which allows one to specify complex probabilistic models using programming primitives like recursion and loops. Probabilistic logic programming aims to further ease the specification of structured probability distributions using first-order logical artefacts. In this article, we briefly discuss the modelling of probabilistic planning through the lens of probabilistic (logic) programming. Although many flavours for such an integration are possible, we focus on two representative examples. The first is an extension to the popular probabilistic logic programming language PROBLOG, which permits the decoration of probabilities on Horn clauses-that is, prolog programs. The second is an extension to the popular agent programming language GOLOG, which permits the logical specification of dynamical systems via actions, effects and observations. The probabilistic extensions thereof emphasize different strengths of probabilistic programming that are particularly useful for non-trivial modelling issues raised in probabilistic planning. Among other things, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting.
Use of Analytical Tools in Empirical Study

The purpose of this article is to study various analytical methods and techniques within the framework of empirical research, which can be used to collect, analyze and interpret data in an accurate and systematic way. Analytical tools for empirical research are various techniques and methods used to collect, analyze, and interpret data systematically and thoroughly. One of the main goals of studying analytical tools is to understand how researchers can use these methods to obtain reliable and valid results. It is also important to understand the correct choice of methodology, hypothesis setting, and forming a holistic picture of the study before applying the empirical practice of knowledge. By forming a construct of correlation of all variables within the framework of building a research philosophy and using properly selected analytical methods, it is possible to develop academic practice to the level of thorough knowledge of the research object. The authors believe that by providing a framework for data collection and analysis, analytical tools help identify patterns and relationships in complex data sets. Learning the analytical tools of empirical research is essential to conducting thorough and effective research.
Multi-view and multi-scale behavior recognition algorithm based on attention mechanism

Human behavior recognition plays a crucial role in the field of smart education. It offers a nuanced understanding of teaching and learning dynamics by revealing the behaviors of both teachers and students. In this study, to address the exigencies of teaching behavior analysis in smart education, we first constructed a teaching behavior analysis dataset called EuClass. EuClass contains 13 types of teacher/student behavior categories and provides multi-view, multi-scale video data for the research and practical applications of teacher/student behavior recognition. We also provide a teaching behavior analysis network containing an attention-based network and an intra-class differential representation learning module. The attention mechanism uses a two-level attention module encompassing spatial and channel dimensions. The intra-class differential representation learning module utilized a unified loss function to reduce the distance between features. Experiments conducted on the EuClass dataset and a widely used action/gesture recognition dataset, IsoGD, demonstrate the effectiveness of our method in comparison to current state-of-the-art methods, with the recognition accuracy increased by 1-2% on average.
Understanding the Stakeholder Perspectives on Assessing Educators’ Digital Competence

Digital competence of educators is one of the key factors affecting wide-scale digital transformation of education and is considered as one of the major aims in the European Digital Education Action Plan (2021–2027). To plan, conduct and report progress in digital competence development requires the ability to measure this competence. Hence, the importance of valid, reliable and usable digital competence assessment has been increasing among researchers, policy makers and teacher educators in recent years. While there exist a number of different instruments for assessing digital competence, there are not many comparative studies that would inform different stakeholders how to choose a suitable instrument to match specifically their context and goals. Furthermore, there is a lack of understanding of needs and expectations of various stakeholders when they consider assessment of educators’ digital competence. This paper summarizes a study that explored the stakeholder perspectives in digital competence assessment and related trade-offs.
Evaluation of a Data Structure Viewer for Educational Practice

Data structures and algorithms are fundamental subjects in the curricula of information science faculties and departments. Recently, they were also included in the curriculum as basic subjects of data science and artificial intelligence. In 2017, we developed a smartphone application, termed Data Structure Viewer(DSV), to promote the understanding of data structures and algorithms. This application has been used in our lectures. In this article, we describe the evaluation of the DSVs used in lectures from 2017 to 2021 through the use of questionnaires. The students’ evaluation and confirmation of the lecture content in class, revealed that the DSV contributed to the improvement of students’ comprehension. We also discuss important points to be considered when using the DSV in lectures.
Improving a Model-Based Software Engineering Capstone Course

Capstone projects are a common feature of software engineering bachelor’s degrees. We report on the experience and lessons learned from a decade of capstone projects at a small regional Israeli college. We first created a capstone process adapted to the department’s model-based software design philosophy, cultural aspects of the student body, and the sparse industrial environment surrounding the college. After several years, we improved the process through the introduction of mandatory fill-in report templates. Analyses of ten years of project statistics and outcomes led us to an understanding of what capstone features led to better outcomes and how the report templates affected grading outcomes. Our templates are released under the Creative Commons Attribution-ShareAlike 4.0 International License.
Curriculum Development and Practice of Application Creation Incorporating AI Functions; Learning During After-School Hours

There is an urgent need to develop human resources which can utilize the power of AI and data in order to enrich society through technological innovation and value creation. On the other hand, in school education, due to the framework of each subject and time limitation, it is difficult to implement a curriculum to work on application creation. Therefore, in this study, we attempted to develop and evaluate teaching materials for developing iOS applications for junior high and high school students in order to provide opportunities for interested students to learn independently during after-school hours. As a result, the students’ responses on programming showed no significant change for the experienced students, but “surprise” and “feeling that I can do it” were meaningful learning opportunities for the beginners. Looking at the responses of the students regarding AI, although they knew the term AI, their perceptions varied from “humanistic”, “familiar”, and “coexistence with AI”.