Reviewerly/exHarmony huggingface dataset:
Design of a modular DSS for public health decision-making in the context of a COVID-19 pandemic landscape

The awareness of the occurrence of a new disease involves much uncertainty and the search for answers and also appropriate questions. In this paper we focus on the perspective of public health decision-makers. Typically, they would have a standard set of questions and supporting metrics that have been found in previous disease outbreaks to be useful in assessing the effectiveness of various ‘solution’ methods on the trajectory of the disease. There may be other relevant questions with which such public health domain experts may not be familiar and/or for which they are familiar but are not aware of methods for addressing such questions when there is limited data. Decision Support Systems (DSS) can be used to facilitate the exploration of established questions and some other relevant questions. Given an initial set of questions, the DSS designer should consider which sets of data analytic methods have the capabilities to adequately address. Some of these data analytic methods may also have the capability of addressing questions that could be of interest to the public health decision makers including researchers. In this paper we present a conceptual design for a relevant easy-to-construct DSS and an example of a multi-method DSS that is based on this conceptual design. Using publicly available data on the CoViD-19 pandemic, we illustrate benefits of the multi-method DSS in action.
Research on the global calibration of the multi-vision line structured light measurement system based on auxiliary camera

<p indent="0mm">To solve the problem of the absence of a common field of view between the cameras in the existing multi-vision line structured light measurement system, which leads to high requirements for calibration tools and complicated global-calibration process, a global-calibration method for the multi-vision line structured light measurement system based on an auxiliary camera is introduced to achieve a large-field-of-view measurement. First, an auxiliary camera is used as an intermediary to acquire an approximately one-half overlapped field of view with the line structured light sensor, which can constitute a binocular subsystem. Then, the spatial geometric relationship between the auxiliary camera and two adjacent line structured light sensors is established based on the different pose images of the calibration plate. Second, the position relationship between the two adjacent line structured light sensors without a common field of view is solved using coordinate transformation. Finally, the transformation matrix from a local to a global coordinate system is solved one at a time to complete the global calibration process. The requirement in terms of calibration tools of the proposed calibration method is low. Further, it does require the use of high-precision measuring equipment and complex targets but only requires a simple chessboard target to complete the global calibration of a multi-vision line structured light measurement system. Meanwhile, the calibration process can achieve high calibration accuracy. Meanwhile, the measurement experiment demonstrates that the global calibration error of this method is <sc>0.067 mm,</sc> which effectively improves the measurement accuracy and can guarantee the required accuracy for the integration of “measurement and machining” of large components using robots.
HWAuth: Handwriting-Based Socially-Inclusive Authentication

Small, local group of users who share private resources (e.g., families, university labs, business departments) usually have limited usable authentication needs. For these entities, existing authentication solutions either require excessive personal information (e.g., biometrics), do not distinguish each user (e.g., shared passwords), or lack security measures when the access key is compromised (e.g., physical keys). We propose an alternative solution by designing HWAuth: an inclusive group authentication system with a shared text that is uniquely identifiable for each user. Each user shares the same textual password, but individual handwriting styles of the text are used to distinguish each user. We evaluated the usability and security of our design through a user study with 30 participants. Our results suggest that (1) users who enter the same shared passwords are discernible from one another, and (2) that users were able to consistently login using HWAuth.
Instructional-based Gamification in Improving Knowledge in Early Childhood

This research aims to produce instructional-based gamification, which effectively improves healthy and clean-living knowledge during early childhood, in Kindergarten Group B Banten Province. A population of 60 students (4-5 years of age) were selected as participants in the study, and all were enrolled in four different kindergartens in Banten province. A purposive sampling technique was used for sample selection, as data collection before and after interventions were carried out using pre and post-test instruments. After the validation process, and field tests were carried out to determine the effectiveness of the products, with the results showing that instructional-based gaming applications were effective in improving healthy and clean-living knowledge. The game topics observed included personal &amp; environmental hygiene, sports, as well as healthy food and drink. Students considered using instructional-based gaming as motivation to learn, in order to develop healthy and clean-living behaviors, which were proposed in the cloud platform of their respective schools. This game also encouraged students' active and dynamic learning, due to the fact that it helped with various interesting activities, therefore, increasing daily healthy and clean-living behaviors. The implication of this study is that students should be diligent in maintaining cleanliness, loving exercise, and always selecting healthy food to consume. The novelty of this study is that games are observed as an early childhood team, which are responsible for improving the knowledge of clean-living and healthy behaviors.
Ho Chi Minh Ideologies on Educating Young Generation - and Training Solutions for Health in New Era with Digital Transformation

Educating the youth in developing and developed countries such as Vietnam is receiving lots of concerns from the public and government. President Ho Chi Minh, a talented leader of Vietnam and the world has emphasized so much on the youth education as they are the future generation and the owner of the nation. In the relationship between individuals and society, Ho Chi Minh requires young people to ask themselves what they have done for the country, not what the country has given them? Must educate young people to have love and responsibility for everyone. Young people must regularly practice self-criticism and serious criticism to help each other progress. Authors main use qualitative analysis, synthesis and inductive methods. We also use experiment test methods and experiences. Beside, physical exercise and sport training for students also are in a main concern, as Tuan, N.A (2017) also mentioned in Quang Binh University, many students prefer soccer and volley ball as their favorite sports to train. Through study, authors indicated traditional and modern values need to be mixed in educating young generation and need to keep good traditional values, morality with physical health training via physical exercises and sport games.
Reasoning with the THOG Problem: A Forty-Year Retrospective

Being able to create new information from already existing premises is the essence of human reasoning.This paper focuses on one of the most important experimental tasks that have been used to study how people make inferences: the THOG problem (Wason, 1977(Wason, , 1978;;Wason & Brooks, 1979).It is a hypothetico-deductive reasoning problem in which subjects must formulate and test hypotheses from the comprehension of an exclusive disjunctive statement.Research on this task has shown that it is a difficult problem to solve and few people reach the logically correct answer.This paper presents some of the main theoretical explanations about people's inferences with this task.From a general perspective, the Dual Process and the Hypothetical Thinking Theories and the Mental Models Theory are found.Some of the more specific proposals have focused on analysing the underlying mechanisms of the cognitive biases such as the Confusion Theory or the Non-Consequential Thinking.Moreover, a review of the empirical investigations on this meta-inference task is presented.Finally, some research on the THOG problem that provides important new clues on broader topics in the study of human reasoning is analyzed.
Optimization of Multimedia English Teaching Computer-Aided System based on Internet of Things

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.
A Drop-in Replacement for LR(1) Table-Driven Parsing

This paper presents a construction method for a deterministic one-symbol look-ahead LR parser which allows non-terminals in the parser look-ahead. This effectively relaxes the requirement of parsing the reverse of the right-most derivation of a string/sentence. This is achieved by replacing the deterministic push down automata of LR parsing by a two-stack automata. The class of grammars accepted by the two-stack parser properly contains the LR( k ) grammars. Since the modification to the table-driven LR parsing process is relatively minor and mostly impacts the creation of the goto and action tables, a parser modified to adopt the two-stack process should be comparable in size and performance to LR parsers.
Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects

Neuro-symbolic approaches combine neural and symbolic methods. This paper explores aspects regarding the reasoning mechanisms of two neuro-symbolic approaches, that is, neurules and connectionist expert systems. Both provide reasoning and explanation facilities. Neurules are a type of neuro-symbolic rules tightly integrating the neural and symbolic components, giving pre-eminence to the symbolic component. Connectionist expert systems give pre-eminence to the connectionist component. This paper explores reasoning aspects about neurules and connectionist expert systems that have not been previously addressed. As far as neurules are concerned, an aspect playing a role in conflict resolution (i.e., order of neurules) is explored. Experimental results show an improvement in reasoning efficiency. As far as connectionist expert systems are concerned, variations of the reasoning mechanism are explored. Experimental results are presented for them as well showing that one of the variations generally performs better than the others.
Semantic Term-Term Coupling-Based Feature Enhancement of User Profiles in Recommendation Systems

Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.
IoT Security Issues and Solutions with Blockchain

A sensor has basically three components namely, the sensor itself which performs sensing action and is based on some technology fit for a particular application. Cloud can be considered as a high-performance network, which interfaces various servers together and streamlines the processing in real time by many devices simultaneously. Sensors help in collecting data from different devices and applications. The traffic between various protocols and networks can be easily managed through gateways. It also ensures the proper connections among devices and sensors. Gateway also preprocesses the sensor data and provides encryption before sending it to the next level. Internet of Things (IoT) architype incorporates a very wide range of devices and apparatus oscillating from the high-end computer server to very small size embedded chips. A categorization of different security issues at different level is shown in Figure 8.5. The figure shows three different levels according to IoT development.
A Reference Model Based on Agile Values, Principles, and Aspects of Scrum, XP and Kanban to Foster the Adoption of Agility in Industry

<p class='IJASEITAbtract'><span lang='EN-GB'>Currently, there is a broad range of software development agile approaches mainly based on the values and principles defined in the agile manifesto (AM). However, in many cases, their implementation is carried out informally and without being aligned with the values and principles stated. In practice, practitioners and consultants may lose sight of the AM recommendations, which could jeopardize the companies’ agility, therefore, applying an agile approach does not make a company agile. In this article, we present a reference model called AgilityRef, which allows practitioners to support the understanding and implementation of the values and principles of the agile manifesto in the software development processes of a company, this, through twenty-two aspects defined from relations established between principles and values described in the AM and processes elements described in Scrum, XP, and Kanban. The evaluation of AgilityRef was carried out through a focus group where its completeness, understandability, and suitability were evaluated. Our findings suggest good enough acceptance by professionals and consultants who evaluated the proposal. The proposed reference model seems to allow professionals and companies to improve the understanding and implementation in practice of the concept of agility in their companies’ software development processes, thus, minimizing the subjectivity and error of their process adoption, implementation, evaluation, and articulation with the principles and values of the agile manifesto.</span>
Introduction to Visual Effects (VFX)

Visual effects play a vital role in every aspect of the entertainment industry. They have extended their purview into e-learning and are an emerging technology in the field of digital education. Creative visual effects design saves designers, manufacturers, and customers time, money, and effort. This book dives into the nuances of visual effects design, from planning to execution. Visual effects give viewers an immersive experience by creating realistic CGI (Computer-Generated Imagery) effects.
Balancing Sparsity and Power: Likelihoods, Priors, and Misspecification

Occam's razor is a classical argument for Bayesian model selection (BMS). Penalizing complexity leads to choosing a parsimonious model, whereas in frequentist tests one cannot prove the simpler model. This argument is correct but care is needed. Good model selection requires balancing parsimony and power, which depends on three elements: the priors, model, and whether it approximates well the data-generating mechanism (misspecification). Standard finite-dimensional BMS does not penalize complexity sufficiently, whereas high-dimensional formulations impose stronger penalties that ensure consistency, but may reduce power. Regarding misspecification, BMS still targets a reasonable problem when (inevitably) assuming the wrong model, and the effect on false positives vanishes asymptotically, but power is exponentially reduced. To increase power one may enrich the model, but having more parameters can reduce power. We discuss these issues, practical strategies to improve sparsity-power tradeoffs, and offer our views on a long-standing debate on BMS sensitivity to prior dispersion.
Interoperability model between heterogeneous MAS platforms based on mobile agent and reinforcement learning

A model of interoperability between heterogeneous multi-agent systems (MAS) platforms is presented in this article. This model is based on a mobile agent and reinforcement learning (RL) to provide semantic interoperability and message-oriented middleware for technology interoperability. The latter concerns the technical problems of linking systems, the definition of interfaces, the data format, and the communication protocols needed to exchange messages. In this context, we propose an architecture based on the AMQP protocol. On the other hand, semantic interoperability depends on the application domain of each system and refers to the ability of two agents to exchange data while preserving their semantics, and to the ability of the receiving agent to translate or convert the information received to ensure efficient collaborative exchanges. In this context, our technique is as follows: when an agent of MAS1 receives a message from another agent of MAS2 and needs information to interpret this message, it requests the help of the mobile agent. The latter must migrate to the MAS2 equipped with a technique allowing it to extract the information necessary for the interpretation of the message. After this step, the mobile agent offers information to the first agent. If this information helps the agent to interpret the message, the mobile agent receives a positive reward from MAS1, otherwise, it receives a negative reward. The goal of the mobile agent is to maximize their rewards. The experimental results have shown the effectiveness of our approach. Indeed, according to the results, the experience of the mobile agent who has already explored the environment makes it possible to accelerate the learning process of other agents.
Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference

AbstractSparse topic modeling under the probabilistic latent semantic indexing (pLSI) model is studied. Novel and computationally fast algorithms for estimation and inference of both the word-topic matrix and the topic-document matrix are proposed and their theoretical properties are investigated. Both minimax upper and lower bounds are established and the results show that the proposed algorithms are rate-optimal, up to a logarithmic factor. Moreover, a refitting algorithm is proposed to establish asymptotic normality and construct valid confidence intervals for the individual entries of the word-topic and topic-document matrices. Simulation studies are carried out to investigate the numerical performance of the proposed algorithms. The results show that the proposed algorithms perform well numerically and are more accurate in a range of simulation settings comparing to the existing literature. In addition, the methods are illustrated through an analysis of the COVID-19 Open Research Dataset (CORD-19).Keywords: Confidence intervalsHigh-dimensional statisticsMatrix factorizationSparsityTopic modeling Supplementary MaterialsIn the supplemental materials, we prove all the theorems and technical lemmas, and also present additional numerical results.Notes1 https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html2 https://www.covid19treatmentguidelines.nih.gov/Additional informationFundingThis work was supported, in part, by NSF (grant nos. DMS-2015259, DMS-2015378) and NIH (grant nos. R01-GM129781 and R01-GM123056).
Variable Selection and Interaction Detection with Bayesian Additive Regression Trees

Bayesian Additive Regression Trees (BART) has emerged as a highly effective Bayesian approach to ensemble modeling with many binary trees. The BART Markov Chain Monte Carlo (MCMC) algorithm provides effective stochastic search in a complex model space and Bayesian uncertainty. As is the case with many modern approaches, the overall complexity of the model makes interpretation difficult. In practice, investigators often wish to know what predictor variables are important or, more generally, which roles variables play in the model. In this chapter we review some approaches for understanding how variables enter the BART model. We present simple ways to find out which variables are most important, which pairs of variables interact in the model, and which subsets of variables allow us to approximate the full information inference according to a user defined metric. In all cases, our approaches are based on post processing the output from basic BART modeling, naturally capturing the uncertainty by the usual MCMC variation in a straightforward way.
API Development

Of all the phases in the software development lifecycle, Development is my favorite. This is when life is breathed into designs and postulations and hypotheses can finally be tested. Unlike other software domains, the complexity of implementation, in an API context, is shielded by the interface. As any seasoned integration engineer will confirm, the interface allows the team to maintain a poker face regarding the level of completion of development. On many occasions, across different projects, I have sometimes published an API with a wafer-thin implementation to either get off the critical path of a maniacal project manager or to shift pressure to the consumer by bluffing with a show of readiness for integration testing.
Development of Qube: A Low-Cost Internet of Things Device for On-Site and Regional Earthquake Warning

Abstract Earthquakes are a major global risk. The current earthquake early warning systems based on public seismic stations face challenges such as high cost, low density, high latency, no alert zone, and difficulty in predicting ground motions at the location of the user. This article pursues an alternative consumer-based approach. An Internet of Things consumer device, called a “Qube,” was built for a cost below $100 and is about the size of a Rubik’s cube. The Qube successfully detected earthquakes and issued earthquake warnings through sounding the onboard alarm for on-site warning and sending text messages to local subscribers for regional warning. The Qube is highly sensitive. During nine months of testing from September 2020 to May 2021, it detected all earthquakes over M 3.0 magnitude around Los Angeles, as well as nearby earthquakes down to M 2.3. The Qube uses a geophone for ground-motion velocity sensing and captures earthquake waveforms consistent with a nearby broadband seismometer in the Southern California Seismic Network. By analyzing data of the earthquakes detected by the Qube, an empirical logarithmic formula that is used to estimate local earthquake magnitude based on detected ground-motion amplitude in digital counts was developed. Although the Qube’s response in digital counts to ground-motion velocity in μm/s has not been determined, the empirical formula between Qube’s output and local earthquake magnitude suggests the Qube’s consistency in ground-motion measurement. The Qube has Wi-Fi connectivity and is controllable via a smartphone or computer. The combination of low cost, high sensitivity, and integrated alarm function of the Qube is intended to enable a consumer-based approach with the potential for mass adoption and use in dense networks, creating new opportunities for seismic network, earthquake warning, and educational applications.