Reviewerly/exHarmony huggingface dataset:
GAMEBC Model: Gamification in Health Awareness Campaigns to Drive Behaviour Change in Defeating COVID-19 Pandemic

Game designers need a precise model to design and develop a gamification application significantly when it drives to behaviour change.This situation is vital when we debate about implementing the gamification approach.However, there is a lack of guidelines or models regarding the gamified model to drive behaviour change in health awareness campaigns.This condition is real, especially the health awareness campaigns in defeating Coronavirus (COVID-19) pandemic.Concurrently, past research still lacks the theoretical foundations of gamification specifically to drive behaviour change in health awareness campaigns.Therefore, this concept paper reviews the frameworks and theoretical foundations, issues, and trends in the gamification models related to behaviour change.Finally, in this present paper, the researchers proposed a gamification model in health awareness campaigns to drive behaviour change named the GAMEBC Model.As Malaysia is very keen on promoting health awareness campaigns in facing the 'new normal,' this research significantly will contribute to Malaysia's six-pronged strategy in COVID-19's fight -resolve, resilience, restart, recovery, revitalize, and reform.
Relating starter cultures to volatile profile and potential markers in green Spanish-style table olives by compositional data analysis

This work relates native lactic acid bacteria (LAB) (Lactobacillus pentosus LPG1, L. pentosus Lp13, and Lactobacillus plantarum Lpl15) and yeast (Wickerhamomyces anomalus Y12) starters to the volatile components (VOCs) produced in green Spanish-style table olives. For this aim, the VOC profile was considered as compositional data (CoDa). The CoDa analysis generated new information on the relationship among inocula and VOCs through the tetrahedral plot, CoDa-biplot, variation array matrix, and CoDa dendrogram. The ilr (which includes pivot) coordinates (Euclidean space) from VOCs produced more reliable starters’ clustering than the original data. The potential VOC markers, identified by a test based on the pairwise comparison of the logratio variation arrays from the whole data set and the individual groups, were (starters in the parenthesis): 2-phenylethyl acetate (LPG1, Y12, Y12 + LAB), methanol (Lpl15), cis-2-penten-1-ol (LPG1, Y12, Y12 + LAB), 2-methyl-3-hexanol (LPG1, Y12), U (non-identified) C (m/z 83-112-97) (Y12) and UF (m/z 95-154-110) (LPG1, Y12 + LAB). Besides, some VOCs were partial/totally inhibited by specific starters: 2-methyl-1-propanol (Lp13, Y12 + LAB), 2-phenyl ethanol (Lp13), furfuryl methyl ether (Y12 + LAB), purpurocatechol (Y12, Y12 + LAB), 4-ethyl guaiacol (Lp13, Lpl15), 4-ethyl phenol (Lpl15), 5-tert-butylpyrogallol (Lp13, Lpl15), and UE (m/z 111–198) (Lp13). A better understanding of the relationship between starters and their VOC may facilitate modelling the flavour and quality of Spanish-style green table olive fermentations.
Redesigning Face-to-face into Online Learning for Speaking Competence during COVID-19: ESP for Higher Education in Indonesia

During the time of COVID-19, students should study at home and class is conducted fully online. However, classes in higher education are mostly planned for Face-to-Face (F2F) learning. To deal with the situation, educators seek an alternative learning design to be implemented and it is expected to have either comparable or improved learning outcome. This study describes Online Learning (OL) design which assesses speaking performance in ESP classes. The participants of the study were students year 2019 (N=118) and students year 2020 (N=96) joining English for Electrical Engineering (EEE) which has the same syllabus and learning goal. A proposed Gagneâ€™s Nine Events of Instruction is analyzed, planned, implemented, and assessed its effectiveness of speaking activities which were done in the former-year-class (using F2F method) then applied in current class (using OL method). The result of F2F and Online Learning (OL) outcome were compared. To complete the analysis, a sequence of questionnaire was given to the students. The results indicates that using Gagneâ€™s Nine Events of Instruction, speaking activity in online learning classroom, achieved satisfactory result. Designing OL both synchronous and asynchronous method for speaking is as effective as F2F. This study may open another research prospect on barriers in OL, how to assess speaking performance via online platform, or others.
Towards a European Health Data Ecosystem

The digital health ecosystem is rife with opportunities to improve healthcare through data-driven services in the European Union (EU). The value of health data, in the multiple forms in which they come (from health records to lifestyle data collected by smartphones or wearables), can truly emerge when they are allowed to flow in the ecosystem within a governance framework supported by all relevant stakeholders, with trust as the common and clear thread underpinning it. The coronavirus pandemic has provided an additional impetus for change, showing the importance of coordination and adequate communication between Member States and quality data to inform decision-making. Significant challenges remain, however, in order to achieve a fully integrated European digital health ecosystem, including: (1) the potentially diverging rules set by Member States at the national level and the need for an EU framework for the secondary use of health data; (2) the need for transparency and accountability for sustaining a data-sharing framework involving public authorities, research organisations, industry and citizens; and (3) the need for interoperable data and processes. Taking stock of these challenges, this article puts forward policy recommendations to enable the provision of pan-European data-driven healthcare services and to build a transparent and trustworthy framework for citizens.
Microaggregation heuristic applied to statistical disclosure control

The dissemination of microdata by public institutions, especially National Statistical Offices (NSOs), and data sharing between public and private organizations and the academia are of undeniable importance today. However, very important ethical and legal aspects related to privacy protection, whether for an individual or a company, often restrict this cooperation. In this sense, the methods of Statistical Disclosure Control (SDC), or, more specifically, microaggregation techniques, offer an alternative to enable the dissemination and sharing of microdata through the control of disclosure risk. In this article, we propose a new heuristic method that adopts concepts from Biased Random Key Genetic Algorithm (BRKGA) metaheuristic and present a comparative study with well-known microaggregation methods. Computational simulations show that the proposed method consistently outperforms other methods in terms of simultaneously reducing information loss and disclosure risk. In addition, the proposed method allows the adopting or even combining of any information loss and disclosure risk metrics. This feature delivers great flexibility and much more control to the privacy protection process.
Diagonal unitary and orthogonal symmetries in quantum theory

We analyze bipartite matrices and linear maps between matrix algebras, which are respectively, invariant and covariant, under the diagonal unitary and orthogonal groups' actions. By presenting an expansive list of examples from the literature, which includes notable entries like the Diagonal Symmetric states and the Choi-type maps, we show that this class of matrices (and maps) encompasses a wide variety of scenarios, thereby unifying their study. We examine their linear algebraic structure and investigate different notions of positivity through their convex conic manifestations. In particular, we generalize the well-known cone of completely positive matrices to that of triplewise completely positive matrices and connect it to the separability of the relevant invariant states (or the entanglement breaking property of the corresponding quantum channels). For linear maps, we provide explicit characterizations of the stated covariance in terms of their Kraus, Stinespring, and Choi representations, and systematically analyze the usual properties of positivity, decomposability, complete positivity, and the like. We also describe the invariant subspaces of these maps and use their structure to provide necessary and sufficient conditions for separability of the associated invariant bipartite states.
Violence detection explanation via semantic roles embeddings

Abstract Background Emergency room reports pose specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descriptions as containing violence-related injuries (V) vs . non-violence-related injuries (NV) is thus a relevant task to the ends of devising alerting mechanisms to track (and prevent) violence episodes. Methods We present ViDeS (so dubbed after Violence Detection System ), a system to detect episodes of violence from narrative texts in emergency room reports. It employs a deep neural network for categorizing textual ER reports data, and complements such output by making explicit which elements corroborate the interpretation of the record as reporting about violence-related injuries. To these ends we designed a novel hybrid technique for filling semantic frames that employs distributed representations of terms herein, along with syntactic and semantic information. The system has been validated on real data annotated with two sorts of information: about the presence vs. absence of violence-related injuries, and about some semantic roles that can be interpreted as major cues for violent episodes, such as the agent that committed violence, the victim, the body district involved, etc.. The employed dataset contains over 150K records annotated with class (V,NV) information, and 200 records with finer-grained information on the aforementioned semantic roles. Results We used data coming from an Italian branch of the EU-Injury Database (EU-IDB) project, compiled by hospital staff. Categorization figures approach full precision and recall for negative cases and.97 precision and.94 recall on positive cases. As regards as the recognition of semantic roles, we recorded an accuracy varying from.28 to.90 according to the semantic roles involved. Moreover, the system allowed unveiling annotation errors committed by hospital staff. Conclusions Explaining systems’ results, so to make their output more comprehensible and convincing, is today necessary for AI systems. Our proposal is to combine distributed and symbolic (frame-like) representations as a possible answer to such pressing request for interpretability. Although presently focused on the medical domain, the proposed methodology is general and, in principle, it can be extended to further application areas and categorization tasks.
Recording and analyzing kinematic data in children and adults with SOLLAR: Sonographic &amp; Optical Linguo-Labial Articulation Recording system

Understanding the development of spoken language in young children has become increasingly important for advancing basic theories of language acquisition and for clinical practice. However, such a goal requires refined measurements of speech articulation (e.g., from the tongue), which are difficult to obtain from young children. In recent years though, technological advances have allowed developmental researchers to make significant steps in that direction. For instance, movements of the tongue, an articulator that is essential for spoken language, can now be tracked and recorded in children with ultrasound imaging. This technique has opened novel research avenues in (a)typical language acquisition, enabling researchers to reliably capture what has long remained invisible in the speech of young children. Within this context, we have designed an experimental platform for the recording and the processing of kinematic data: SOLLAR (Sonographic and Optical Linguo-Labial Articulatory Recording system). The method has been tailored for children, but it is suitable for adults. In the present article, we introduce the recording environment developed to record over 100 children and 30 adults within SOLLAR. We then describe SOLLAR's data processing framework, providing examples of data visualization and a summary of strengths and limitations.
Drawing together control landscape and tomography principles

The ability to control quantum systems using shaped fields as well as to infer the states of such controlled systems from measurement data are key tasks in the design and operation of quantum devices. Here we associate the success of performing both tasks to the structure of the underlying control landscape. We relate the ability to control and reconstruct the full state of the system to the absence of singular controls and show that for sufficiently long evolution times, singular controls rarely occur. Based on these findings, we describe a learning algorithm for finding optimal controls that makes use of measurement data obtained from partially accessing the system. Open challenges stemming from the concentration of measure phenomenon in high-dimensional systems are discussed.
Computing and classifying reduplication with 2-way finite-state transducers

This article describes a novel approach to the computational modeling of reduplication. Reduplication is often treated as a stumbling block within finite-state treatments of morphology because they cannot adequately capture the productivity of unbounded copying (total reduplication) and because they cannot describe bounded copying (partial reduplication) without a large increase in the number of states. We provide a comprehensive typology of reduplicative processes and show that an understudied type of finite-state machine, 2-way deterministic finite-state transducers (2-way D-FSTs), captures virtually all of them. Furthermore, the 2-way D-FSTs have few states, are in practice easy to design and debug, and are linguistically motivated in terms of the transducer’s origin semantics or segment alignment. Most of these processes, and their corresponding 2-way D-FSTs, are available in an online database of reduplication (RedTyp). We classify these 2- way D-FSTs according to the concatenation of known subclasses of regular relations and show that the majority fall into the Concatenated Output Strictly Local (C-OSL) class. Other cases require higher subclasses but are still definable by 2-way D-FSTs.
Peer Consonance in Blockchain based Healthcare Application using AI-based Consensus Mechanism

The term `Blockchain', commonly referred to as the brain behind the Bitcoin network, works on the simple principle of the presence of a distributed and decentralized ledger in a public or private network. Since blockchain is decentralized, it is the duty of the Consensus Algorithm to substantiate the details in the blockchain. Traditional consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS), although widely used, are a matter of concern due to computationally expensive operations and convergence towards a monopolized system respectively. Though optimizations of PoW and PoS algorithms were subsequently introduced, their features precincts. This paper aims to provide a solution by presenting a consensus algorithm based on Artificial Intelligence (AI) technology while maintaining the fairness of the system. A Healthcare based system was set up on top of the blockchain network to generate the dataset about the miners in order to train our neural network. On the whole, it incorporated the advantages of the state of the art consensus models which can increase the efficiency of the healthcare industry while diminishing their demerits.
Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

Unmanned Aircraft Systems (UASs) have been recognized as an important resource in search-and-rescue (SAR) missions and, as such, have been used by the Croatian Mountain Search and Rescue (CMRS) service for over seven years. The UAS scans and photographs the terrain. The high-resolution images are afterwards analyzed by SAR members to detect missing persons or to find some usable trace. It is a drawn out, tiresome process prone to human error. To facilitate and speed up mission image processing and increase detection accuracy, we have developed several image-processing algorithms. The latest are convolutional neural network (CNN)-based. CNNs were trained on a specially developed image database, named HERIDAL. Although these algorithms achieve excellent recall, the efficiency of the algorithm in actual SAR missions and its comparison with expert detection must be investigated. A series of mission simulations are planned and recorded for this purpose. They are processed and labelled by a developed algorithm. A web application was developed by which experts analyzed raw and processed mission images. The algorithm achieved better recall compared to an expert, but the experts achieved better accuracy when they analyzed images that were already processed and labelled.
Application of Smartphone Qrcode Scanner as a means of Authenticating Student Identity Card

The smartphone scanner is the best tool to authenticate any object with embedded Qr codes anytime and anywhere.This smartphone scanner will make it easier and simpler to encode the generated code without any difficulty.The need for this research is to enhance the quality time of authentication which can now be done anywhere and anytime without the problem of location and connectivity issue.However, smart card technology has not been fully embraced, particularly in developing countries like Nigeria because of our lack of innovation in information technology.As a solution, this work presents a real time student identity card authentication system with the aid of android phone camera scanner in built function.The developed system accepts as input, a matriculation number which is unique for every student and encrypts it into a two-dimensional bar code using a QR generator.The generated QR code is then embedded in the identity card alongside other details of the cardholder which produce a dynamic authentication output.A software application that functions as a QR scanner is pre-installed on the smartphone.This makes it possible to reverse this process during authentication for a reconciliation of the decrypted code with the student matriculation number.The result of the experiment reveals that the smartphone scanner is effective and faster to authenticate the embedded qr code trademark than any other means of authentication.
Video-Based Human Motion Capture Data Retrieval via MotionSet Network

Content-based human motion capture (MoCap) data retrieval facilitates reusing motion data that have already been captured and stored in a database. For a MoCap data retrieval system to get practically deployed, both high precision and natural interface are demanded. Targeting both, we propose a video-based human MoCap data retrieval solution in this work. It lets users to specify a query via a video clip, addresses the representational gap between video and MoCap clips and extracts discriminative motion features for precise retrieval. Specifically, the proposed scheme firstly converts each video clip or MoCap clip at a certain viewpoint to a binary silhouette sequence. Regarding a video or MoCap clip as a set of silhouette images, the proposed scheme uses a convolutional neural network, named MotionSet, to extract the discriminative motion feature of the clip. The extracted motion features are used to match a query to repository MoCap clips for the retrieval. Besides the algorithmic solution, we also contribute a human MoCap dataset and a human motion video dataset in couple that contain various action classes. Experiments show that our proposed scheme achieves an increase of around 0.25 in average MAP and costs about 1/26 time for online retrieval, when compared with the benchmark algorithm.
Artificial intelligence as a component of innovative content of general education: analysis of world experience and domestic prospects

Problem and goal. The article discusses the world experience of introducing artificial intelligence into the educational system of schoolchildren. The purpose of this study is to identify the features of the consideration of artificial intelligence as a component of the content of educational systems in individual countries of the world and possible approaches to teaching the basics of artificial intelligence in Russian schools. Methodology. In the research process, a set of methods was used: analysis of information resources and regulatory documents; generalization and systematization of professional and pedagogical experience; reflection of the content of the generated knowledge. Results. The features of the inclusion of artificial intelligence in the educational systems of individual countries of the world are revealed and differences in approaches to the process of teaching elements of artificial intelligence at the substantive and at the organizational and methodological levels are indicated. The results of the study made it possible to find approaches to teaching students of Russian schools the basics of artificial intelligence. Conclusion. It is shown that the issue of including artificial intelligence in the educational systems of various states is at the stage of development and design, which is based on various approaches. It was revealed that today, artificial intelligence is most often regarded as a means of teaching schoolchildren, and not as their object of study. The results of the study made it possible to propose approaches to teaching the basics of artificial intelligence in a general school.
Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson–Gaussian Likelihood

Mixed Poisson–Gaussian noise exists in the star images and is difficult to be effectively suppressed via maximum likelihood estimation (MLE) method due to its complicated likelihood function. In this article, the MLE method is incorporated with a state-of-the-art machine learning algorithm in order to achieve accurate restoration results. By applying the mixed Poisson–Gaussian likelihood function as the reward function of a reinforcement learning algorithm, an agent is able to form the restored image that achieves the maximum value of the complex likelihood function through the Markov Decision Process (MDP). In order to provide the appropriate parameter settings of the denoising model, the key hyperparameters of the model and their influences on denoising results are tested through simulated experiments. The model is then compared with two existing star image denoising methods so as to verify its performance. The experiment results indicate that this algorithm based on reinforcement learning is able to suppress the mixed Poisson–Gaussian noise in the star image more accurately than the traditional MLE method, as well as the method based on the deep convolutional neural network (DCNN).
Attribute Revocable Data Sharing Scheme Based on Blockchain and CP-ABE

Data sharing and exchange is an effective way to release the vitality and value of data resources, but it also brings security problems, especially in the distributed environment. In this paper, a data sharing scheme based on blockchain, smart contract and attribute-based encryption is proposed, which supports attribute level user authority revocation, and is suitable for the privilege management of data sharing in distributed environment. Firstly, the model of data sharing and exchange is given; secondly, the operation of data sharing and exchange is defined; finally, implementation by smart contract is given and an experiment is carried out. The results show that the scheme can effectively solve the problem of data access authorization and revocation in distributed environment. In this scheme, we introduce the trusted third party as the key management center to generate, encrypt and decrypt the key, thus reducing the pressure on the client.