Reviewerly/exHarmony huggingface dataset:
Research of College English Online Course Based on Cloud Computing and Exploitation for Multimedia Asian Information Processing

In today's techno world,teachers worldwide can learn the world-famous masterclasses and access all sorts of courses for school teachers. Learners have more learning opportunities based on the excellent sharing of resources. Technical support is provided in developing the Fuzzy Integrated Cloud Computing Framework (FICCF) to develop Online Courses. This paper explores the educational capabilities of cloud computing, the real-time contact between teachers and students, studying, and heterogeneous terminal access to track evaluation-based fuzzy variables. It addresses the benefits of open-ended modes, creating an English course design system in a cloud-based environment, modifying lecture mode, and reforming the English course design mode. The experimental result shows that the proposed cloud computing framework has better efficiency in developing and promoting English online courses.The self-learning systems make it possible to provide the best possible learning environment. It ensures that sensitive data generated by an application is deleted, and this feature ensures accuracy.
A Proposal of a New Chaotic Map for Application in the Image Encryption Domain

Several chaos-based image encryption schemes have been proposed in the last decade. Each encryption scheme has pros and cons regarding its speed, complexity, and security. This paper proposes a new chaotic map called Power-Chaotic Map (PCM). Characteristics of the proposed PCM, such as chaotic behaviour, randomness, sensitivity, and s-unimodality, are investigated. As an application of the proposed chaotic map, an image encryption scheme is proposed to encrypt greyscale and text images. The proposed three-phase image encryption scheme performs a series of substitution and permutation operations. The Pixel-Level phase utilises the PCM’s generated keystreams to perform the substitution operation of image pixels. The Row-Level phase permutates, via a proposed pseudorandom number generator, pixel locations of each row and then shuffles row locations. Finally, the Column-Level phase performs a substitution operation on pixels of each column. Performance of the proposed PCM-based image encryption scheme is investigated through histogram analysis, statistical correlation analysis, key sensitivity, encryption performance of text images, and permutation and substitution properties. Experimental results indicate that the PCM has a wider range of chaotic behaviour than well-known one-dimensional maps, meets the s-unimodality property, has high sensitivity, and generates keystreams with random-like behaviour. Furthermore, results indicate that the PCM-based image encryption scheme provides high encryption security for text images, high key sensitivity, immunity against brute-force attacks, strong statistical correlation results, strong encryption performance, and low computational complexity.
Website-Based Outpatient Registration Information System at Dokter Bersama Clinic Bojong Gede.

&lt;p&gt;&lt;em&gt;Dokter Bersama Clinic is one of the individual health business entities in Bojong Gede District. However, since the outbreak of the COVID-19 pandemic, the clinic has experienced a decline in income and has temporarily stopped operating. Some time later this pandemic began to decrease and then entered the new normal. The purpose of this study is to reduce the queue for registration crowds of patients who will seek treatment at the clinic and build a website-based outpatient registration system without having to crowd in queues either during the COVID-19 pandemic or after the new normal period. The writing method used is the waterfall method which is classic and easy to use so that it fits with the system design created. The expected results in the Website-Based Outpatient Registration Information System at the Dokter Bersama Clinic Bojong Gede are to help register treatment without having to queue, provide registration information for patients how many queues are running and patients can also choose a treatment schedule according to doctor's practice hours via online registration and Helping admins and doctors make it easier to manage patient registration with an online system.&lt;/em&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
Multiframe Track-Before-Detect of Weak Fluctuating Targets in a Complex-Valued RD Plane

The use of track-before-detect (TBD) of weak fluctuating targets is significant in radar systems. Conventional TBD methods may suffer from model mismatch and information waste, and the target fluctuation may not be considered. In this paper, to improve the detection and tracking performance of weak fluctuating targets using complex-valued range-Doppler (RD) data, a multiframe TBD method is proposed in a complex-valued RD plane. The target state evolution in the RD plane is formulated for multiframe accumulation accuracy. Considering the additional phase of raw complex-valued RD measurements, matched filtering based TBD using both amplitude and phase information in the raw RD plane is presented. Five common fluctuation models, i.e., Rayleigh distributed model, x2 distributed model, Log-Normal distributed model, Weibull distributed model, and K distributed model are used to describe the target fluctuation in RD plane. The procedure for multiframe accumulation of fluctuating targets in RD plane is presented in detail. The multiframe accumulated envelope of the fluctuating target in RD plane is derived theoretically, and the computational complexity of the proposed method is also analyzed. Both numerical simulations and real data are used to demonstrate the effectiveness of the proposed method.
A two-stage Bayesian learning-based probabilistic fuzzy interpreter for uncertainty modeling

In practical applications, there are usually complex stochastic and vague uncertainties caused by inherent randomness and unknown dynamics. How to model uncertainty and interpret the modeling result is of significance for practical applications. To address these issues, a new probabilistic fuzzy logic system (PFLS) is proposed based on two-stage Bayesian learning. On the basis of the structure of traditional FLS, the proposed PFLS can be used as probabilistic fuzzy interpreter for complex uncertainty modeling. Specifically, in the training stage, expectation–maximization-based Gaussian mixture model is used to classify given data and learn the corresponding probability distributions, which are further used to generate samples to compensate for the limited given data. Based on Bayesian learning, fuzzy rules are learned by maximizing joint probability distributions of given data and rules. In the later inference stage, Bayesian learning-based probabilistic fuzzy inference is developed to make the activated fuzzy rules best match the input and output data pairs. Through integration with the two-stage Bayesian learning, PFLS can properly handle both vague and stochastic uncertainties, and interpret modeling results with fuzzy rules under maximum likelihood. Experiments based on temperature predictions in complex industrial processes demonstrate the effectiveness of the proposed method.
Online Survey on Novel Designs for Supporting Self-Reflection and Emotion Regulation in Online News Commenting

Uncivil commenting on online news is regarded as a persistent and complex sociotechnical issue. Because commenting behavior is inherently conditioned by user interfaces (UIs) on news sites, HCI scholars may approach the issue by proposing alternative UI solutions and thereby potentially mitigating incivility. This paper explores eight novel UI design proposals that aim to support emotion regulation and self-reflection during commenting and reports how the designs are evaluated in an international online survey (N=439) among online news commenters. This exploratory study advances our understanding of what kind of UI solutions, from the end-user’s perspective, appear desirable—and why—in terms of improving the quality of online news commenting. For example, desire for moderation was found to predict more favorable ratings of the design proposals in general.
A Hybrid Board Game by Using Daily Activity Data of Users as Game Mechanics

Motivating people for activities that will benefit their physical and mental health through games and gamification has been popular, and prevalent especially throughout the pandemic. While many studies produce tailored gameful apps for this purpose, studies that investigate if the existing games can be used for such purposes by modifying their game mechanics are scarce. Thus, in this research, we customized an existing board game by using the game mechanics to facilitate better habits for physical activity, sleep time and social interaction. We added mechanics granting players permanent skills, bonus moves and in-game currency based on their daily activities with a research through design process which went through paper mockups, game design, development of a web-based platform and iterative user tests. Our preliminary findings show that the social component of hybrid board games can help motivate players to adopt a healthier lifestyle by creating an environment to discuss health issues with others.
Imagining Emerging Technologies for Promoting Intercultural Meaningful Social Interactions: A Participatory Futures Approach

Intercultural Meaningful Social Interactions (IMSI) are positive and impactful interactions held between intercultural strangers. Previously, studies have explored these interactions characterizing them, identifying factors that influence them, and tactics and strategies to promote them. Still, what kind of technologies could encourage IMSI is underexplored. Hence, we took a participatory futures approach to understand participants' imaginaries and perceptions of potential technologies to promote IMSI. We conducted participatory futures workshops with locals and migrants living in Istanbul, an exemplary multicultural city between the east and west, who envisioned and discussed 15 concepts of emerging technologies that would enable IMSI in Istanbul of 2050. By analyzing this work, our contribution is first to present six visions of alternative futures of IMSI and, second, to introduce the tools and implications of the methodology followed to enable participants' futures thinking of IMSI in an intercultural context.
An investigation into the effect of cluster-based preprocessing on software migration

The problem of language obsolescence has been addressed in multiple ways, including by rule-based transformation techniques. In such scenarios, a set of rules is executed against an artefact from a source language, generating a consistent or compatible artefact in a target language. For large and complex artefacts, and large and complex languages, this rule set may also be large and complex. Techniques that can make the execution or expression of these rule sets more efficient and effective would be helpful. In this paper we present a preliminary investigation on the use of preprocessing in software migration. In particular, we describe an approach that makes use of a novel cluster-based preprocessing algorithm, based on machine learning, to partition the input space, with the consequence of making rule-based transformation more efficient and effective. We present preliminary data evidencing this claim.
CPSA <sub>ML</sub>

Cyber-physical systems (CPS) are finding increasing use, whether in factories, autonomous vehicles, or smart buildings. Monitoring the execution of CPSs is crucial since CPSs directly influence their physical environment. Like the actual system, the monitoring application must be designed, developed, and tested. Mobile CPSs, in contrast to stationary CPSs, bring the additional requirement that instances can dynamically join, leave, or fail during execution time. This dynamic behavior must also be considered in the monitoring application. This paper presents CPSAML, a language and code generation framework for the model-driven development of mobile CPS systems, including a cockpit application for monitoring and interacting with such a system. The pipeline starts with the formulation of the system and the CPSs it contains at an abstract level by the system architect using a domain-specific modeling language. Next, this model is transformed into SysML 2 for further extension and richer specificity by system engineers on a more technical level. In the last step of the pipeline, the SysML 2 model is used to generate code for the CPS devices, a system-wide digital twin, and the cockpit application mentioned above. This cockpit enables the operator to configure and apply the monitoring and interaction with the system during runtime. We evaluate our CPSAML language and code generation framework on an Indoor Transport System case study with Roomba vacuum cleaner robots.
Dynamic adaptation for distributed systems in model-driven engineering

Modern-day software systems operate within complex, uncertain, and highly dynamic environments. Managing such systems is a significant challenge; developing self-managing autonomic systems is one way to reduce development and maintenance efforts. In the context of distributed systems, achieving this autonomy through dynamic adaptation is particularly challenging due to the volatile host environment. Model-Driven Engineering (MDE) is a software development paradigm that advocates the use of models as the primary artifacts rather than source code. MDE promises higher-quality software at a lower cost through abstractions, automation, and analyses.
Towards blended modeling and simulation of DevOps processes

DevOps and Model Driven Engineering (MDE) provide differently skilled IT stakeholders with methodologies and tools for organizing and automating continuous software engineering activities and using models as key engineering artifacts. JSON is a popular data format, and JSON Schema provides a general-purpose schema language for JSON. This paper presents our work in progress on blended modeling and scenario simulation of continuous delivery pipelines as executable JSON-based models. For this purpose, we show a case study based on Keptn, an open source tool for DevOps automation of cloud-native applications, and its language, Shipyard, a JSON-based process language for continuous delivery pipeline specification.
Mediation-based MLM in FOModeLer

MLM has attracted much attention over the last two decades. MLM activities include philosophical discussions about ontologies, requirements and relevant services, and development of theories, languages, and tools. Approaches differ in their support for MLM concepts on the levels of syntax, semantics and pragmatics.
Participatory agent-based modelling in healthcare

Management of infectious diseases is a critical issue in hospital emergency departments (ED). Participatory agent-based modelling (ABM) is an effective method of capturing and evaluating potential infection control interventions without incurring real-world costs. However, typical participatory modelling approaches use general-purpose programming languages or technically complex agent-oriented DSMLs during implementation. These languages are often inaccessible to ED staff, making communication difficult during domain analysis and reducing the traceability of model design. We argue that high-abstraction domain-specific modelling languages (DSMLs) can help improve the accessibility of ABM development to ED stakeholders, allowing them to engage more effectively in model design and experimentation. Rather than developing a DSML at the platform-level (i.e. using ABM concepts) we present a DSML that uses a highly-abstract domain-level syntax, specific to the ED domain. We present the current iteration of this DSML, and the vision for a participatory modelling methodology to be used during model and DSML development. We discuss ongoing modelling case studies in collaboration with St Thomas' Hospital in London and present an initial DSML empirical evaluation.
Automating the design of recommender systems

Recommender systems (RSs) are complex software systems that come in handy in many application domains. Being built on top of various components, these systems can ease the curse of information overload, enabling developers to approach the most relevant artifacts while coding. Nevertheless, existing approaches resemble black-box systems, where the end-user is not allowed to customize the overall process. This poses difficulties for those who want to tailor the design to meet their requirements when implementing and evaluating a recommender system.
EvolveDB

Requirements for software applications change almost continuously in all phases of the product life cycle, which leads to a constant adaption of software systems. These changes affect the domain model of the application and usually lead to necessary changes of the underlying database. As a result, the data stored in this database must also be adapted accordingly. This well-known problem is called schema evolution. Manual schema evolution with low-level SQL scripts contains both time-consuming and error-prone routine tasks and complicated operations that require expert knowledge. Automation promises significant leverage for cost savings and quality improvements. To date, the most advanced solutions for this problem have focused on operator-based approaches. However, these approaches do not allow free editing of the database schema, but require the database administrator to know in advance the exact sequence of all required operations. In this paper, we present EvolveDB, an approach to model-driven schema evolution in relational databases, where the user specifies the evolution steps by freely editing a database model extracted by reverse engineering. EvolveDB analyzes the differences between the status quo and the evolved model structures and generates a data migration script. An initial evaluation with experts shows promising results. A screencast of the demo is available at https://youtu.be/ieXmrDd2nw4.
ViMoTest

Automated testing for software is a fundamental part of achieving high-quality software systems. In particular, the user interface (UI) is one suitable abstraction layer for automated tests. However, software test developers usually spend considerable effort writing automated UI tests in a maintainable way. Therefore, this work proposes a concept to design a low code approach using the ViewModel architectural pattern as an abstraction layer to develop automated UI tests. We use the Design Science Research method to design a projectional DSL as an artifact to refine our approach. As planned results, we aim to (1) develop a projectional DSL to specify ViewModel-based tests, (2) apply this novel approach in an industrial and an open-source project, and (3) perform empirical studies to get feedback for improving this approach w.r.t. creation, maintenance, and understandability of test suites. We conclude that a low code approach using a projectional DSL to specify automated tests based on ViewModels could have several benefits compared to similar approaches.
Design Recommendations for Historical Cemeteries Using Speculative Design

Historical cemeteries are interesting design spaces within Human-Computer Interaction (HCI), due to their cultural and personal significance, and that they also serve multi-purposes within an urban community. However, contemporary usage of such places and technological developments partly interfere with preserving such places’ significance. To evaluate how to counteract this challenge, we conducted an iterative design approach including a) five expert interviews, b) a design workshop using speculative design artifacts (n=4), followed by c) semi-structured interviews that evaluated the derived design concepts (n=6). We made an exemplary study of a European historical cemetery, and consolidate ten design recommendations for interactive historical cemeteries. Eight align with prior work, two are new. Furthermore, we identified design opportunities for supportive technology to maintain such places’ atmosphere and regulate users’ behavior to comply with the local code of conduct. Our work supports other HCI researchers, aiming to preserve cultural heritage sites such as historical cemeteries.
Effect of time patterns in mining process invariants for industrial control systems: an experimental study

Machine Learning is playing a crucial role in the design of intrusion detectors for Industrial Control Systems (ICS). Intrusion Detection Systems (IDS) rely on data obtained from an operational ICS. Such datasets contain multiple time series, one for each process variable. In this work, we explore how such time series can be exploited to understand the effect of time patterns in mining the process invariants, i.e., conditions on process state variables. We use the knowledge gained through the time patterns to determine the optimal data collection size for generating the invariants. The study reported here was conducted using the operational data obtained from a water treatment plant.
ORM ontologies with executable derivation rules to support semantic search in large-scale data applications

A semantic layer maps complex enterprise data into an ontology with abstract business concepts that are well-known to business users. Chief data officers invest significant effort to create and update these ontologies, while data scientists do feature engineering by combining already existing concepts and features of the domain. However, it is a significant challenge to catalogue and maintain the numerous features pertaining to an ontology, which leads to duplicated features and unnecessary complexity. In this work, we propose to combine ontologies captured using the Object-Role Modeling notation with derivation rules defined in a datalog-like language called Rel, which allows the creation of a semantic layer with feature search capability. Our prototype framework uses the RAI Knowledge Graph Management System, which provides automated and incremental derivation rule evaluation.
Multi-scale model-based explanations for cyber-physical systems

Automated control in Cyber-Physical Systems (CPS) generates behaviours that may surprise non-expert users. Relevant explanations are required to maintain user trust. Large CPS (e.g., autonomous car networks and smart grids) raise additional scaleability issues for the explanatory processes and complexity issues for generated explanations. We propose a multi-scale system modelling and explanation technique to address these concerns. The idea is to increase the scale, or abstraction level, of the modelled CPS, whenever possible without loss of salient information, so as to produce smaller system representations and hence to reduce the complexity of the explanatory process and of the generated explanations. We illustrate our proposal via an urban traffic case study, modelling traffic at two different scales (i.e., modelling individual cars at a lower-scale; and traffic jams at a higher-scale). We show how a multi-scale explanatory process can use the lower- and higher-scale models to generate either longer (more detailed) explanations, or shorter (more abstract) explanations, respectively. This proof-of-concept illustration offers a basis for further research towards a comprehensive multi-scale explanatory solution for CPS.