Reviewerly/exHarmony huggingface dataset:
Adapting Acting Education for Pandemic-Friendly Mediums

When an educator drastically changes the physical learning environment, there is a potential for a psychological change in a student’s focus and will. Psychological changes can naturally manifest in one’s presentation of energy, behaviour, and appearance. With traditional classroom settings being converted into online and significantly different in-person training environments, the potential for a parallel transformation by actors and teachers in adjusting to these new environments can be expected. In self-presentation theory, an individual can control the self-image presented to others (Leary, 1983). But what if the new self-image is: survivor? Online classrooms have unique challenges in adapting traditional actor training methods, and educators should be alert to the impacts on students’ commitment by observing their changes in energy, behaviour, and costuming because self-presentation impacts not only the characters being portrayed, but more importantly, the actors. Our success as educators is largely determined by how well our students learn and what kind of actors they become. Self-presentation theory can be used as a lens through which to observe students’ focus, and it can be applied to reimagine the roles of the students and teachers. This chapter is written as a play, enabling you, our readers to take on a role. The question for our readers is: what role does self-presentation theory play for students and teachers in adapting actor training education into pandemic friendly mediums: online, in-person indoor and in-person outdoor classrooms?
ISO/TS 21564:2019- based Evaluation of a Semantic Map between Variables in the ISARIC Freestanding Follow Up Survey and ORCHESTRA Studies

Abstract The COVID-19 pandemic has led to tremendous investment in clinical studies to generate much-needed knowledge on the prevention, diagnosis, treatment and long-term effects of the disease. Case report forms, comprised of questions and answers (variables), are commonly used to collect data in clinical trials. Maximizing the value of study data depends on data quality and on the ability to easily pool and share data from several sources. ISARIC, in collaboration with the WHO, has created a case report form that is available for use by the scientific community to collect COVID-19 trial data. One of such research initiatives collecting and analyzing multi-country and multi-cohort COVID-19 study data is the Horizon 2020 project ORCHESTRA. Following the ISO/TS 21564:2019 standard, a mapping between five ORCHESTRA studies’ variables and the ISARIC Freestanding Follow-Up Survey elements was created. Measures of correspondence of shared semantic domain of 0 (perfect match), 1 (fully inclusive match), 2 (partial match), 4 (transformation required) or 4* (not present in ORCHESTRA) as compared to the target code system, ORCHESTRA study variables, were assigned to each of the elements in the ISARIC FUP case report form (CRF) which was considered the source code system. Of the ISARIC FUP CRF’s variables, around 34% were found to show an exact match with corresponding variables in ORCHESTRA studies and about 33% showed a non-inclusive overlap. Matching variables provided information on patient demographics, COVID-19 testing, hospital admission and symptoms. More in-depth details are covered in ORCHESTRA variables with regards to treatment and comorbidities. ORCHESTRA’s Long-Term Sequelae and Fragile population studies’ CRFs include 32 and 27 variables respectively which were evaluated as a perfect match to variables in the ISARIC FUP CRF. Our study serves as an example of the kind of maps between case report form variables from different research projects needed to link ongoing COVID-19 research efforts and facilitate collaboration and data sharing. To enable data aggregation across two data systems, the information they contain needs to be connected through a map to determine compatibility and transformation needs. Combining data from various clinical studies can increase the power of analytical insights.
Exploring Spatiotemporal Consistency of Features for Video Translation in Consumer Internet of Things

Video data has emerged as a primary source of information input in contemporary CIoT systems, significantly driving its development. However, due to the diversity of video capture devices, videos exhibit significant heterogeneity in various aspects, such as color, texture, and lighting conditions, posing challenges for video manipulation and analysis. Moreover, different information processing terminals have limited requirements for data types, which has led to a demand for the translation of heterogeneous videos. In this paper, we propose a novel method named Structure and Motion Consistency Network (SMCN). It updates and optimizes the model from the feature level, making it more efficient at extracting invariant spatiotemporal information from different types of video data. Specifically, it fuses the structure information, a.k.a. mean and standard deviation of features at each spatial position across channels, then re-injects it to refine the spatial consistency, and maximizes motion mutual information of features from adjacent frames to improve the temporal consistency of intermediate features. We conducted experiments on the common video translation dataset Viper and the infrared-to-visible video translation dataset IRVI. Extensive experiments indicate our SMCN outperforms the state-of-the-art methods and the lightweight module can be easily applied to other models in a plug-and-play manner, showing significant advantages in addressing the problem of heterogeneous video data transformation.
Causal Graph Discovery for Explainable Insights on Marine Biotoxin Shellfish Contamination

Harmful algal blooms are natural phenomena that cause shellfish contamination due to the rapid accumulation of marine biotoxins. To prevent public health risks, the Portuguese Institute of the Ocean and the Atmosphere (IPMA) regularly monitors toxic phytoplankton in shellfish production areas and temporarily closes shellfish production when biotoxins concentration exceeds safety limits. However, this reactive response does not allow shellfish producers to anticipate toxic events and reduce economic losses. Causality techniques applied to multivariate time series data can identify the variables that most influence marine biotoxin contamination and, based on these causal relationships, can help forecast shellfish contamination, providing a proactive approach to mitigate economic losses. This study used causality discovery algorithms to analyze biotoxin concentration in mussels Mytilus galloprovincialis and environmental data from IPMA and Copernicus Marine Environment Monitoring Service. We concluded that the toxins that cause diarrhetic and paralytic shellfish poisoning had more predictors than the toxins that cause amnesic poisoning. Moreover, maximum atmospheric temperature, DSP toxins-producing phytoplankton and wind intensity showed causal relationships with toxicity in mussels with shorter lags, while chlorophyll a (chl-a), mean sea surface temperature and rainfall showed causal associations over longer periods. Causal relationships were also found between toxins in nearby production areas, indicating a spread of biotoxins contamination. This study proposes a novel approach to infer the relationships between environmental variables to enhance decision-making and public health safety regarding shellfish consumption in Portugal.
Pengadaan Perahu Motor Klotok Sebagai Transportasi Pemadam Kebakaran Sungai di Pemukiman Bantaran Sungai di Kelurahan Benua Melayu Laut Kecamatan Pontianak Selatan

Based on the ground figure data, it can be seen that the number of buildings in riverbank settlements has poor spatial planning with a fairly high density, especially in riverbank areas, where many buildings are above the river. Residential houses in the Bantaran Sungai settlement are residential houses that are densely populated and reach access to one residential house and another using old and weathered wooden snaps with several criteria including: residential houses located above the river, residential houses that are not livable. density of residential houses that are difficult to reach in the event of a fire and drowning of residents. community service implementation activities (PPM) Pontianak State Polytechnic 2021 Procurement of klotok motorboats as river firefighting transportation: Boat 4 meters long, 1.5 meters wide, with timbau board wood material, Using 4 cans of oil paint, Fire pump, boat engine.
Impacto do uso da Telemedicina nos indicadores clínicos assistenciais de unidades de terapia intensiva pediátricas: protocolo para um ensaio clínico randomizado em cluster

The objective of this study is to present the protocol of a cluster randomized clinical trial to be conducted through the TeleICU project - Qualification of Intensive Care by Telemedicine. The study will consist of a cluster randomized clinical trial, open label, in pediatric intensive care units, with an allocation ratio of 1:1, to compare the intervention group (support of Telemedicine for patients admitted to the pediatric intensive care unit) with a control group (pediatric intensive care unit usual care). The study proposed to select 16 pediatric intensive care units, including 100 participants per site, with a total of 1,600 participants. The intervention group will receive telerounds from Monday to Friday and will have specialists and continuing education activities available. The primary outcome measure will be the length of stay in the pediatric intensive care unit, defined as the difference between the date of discharge of the participant and the date of admission to the intensive care unit. The secondary outcomes will be mortality rate, invasive mechanical ventilation-free days, days using antibiotics, days using vasoactive drugs and days using sedoanalgesia. This study will be conducted in accordance with Resolution 466/12 of the National Health Council, with approval by the Research Ethics Committee of the institutions involved. The present study has the potential to reproduce studies on Telemedicine in intensive care and may make important contributions to care in intensive care units in Brazil and other settings. If Telemedicine shows positive clinical care results compared to conventional treatment, more pediatric patients may benefit. ClinicalTrials.gov registry: NCT05260710
Motion Control of a Mobile Robot Using the Hedge–Algebras-Based Controller

Hedge–algebras (HA) theory provides a useful mathematical tool for modeling the linguistic values of a linguistic variable. These values are quantified by real numbers between 0 and 1. Therefore, the HA-based controller (HAC) has many advantages over the traditional fuzzy set theory-based controller (FC) in setup steps, control efficiency, computation time, and optimization. This study aims to control the avoidance of obstacles in the workspace and move to the destination of an autonomous robot using HAC, in which the HAC is optimized using the balancing composite motion optimization (BCMO) to return the optimal path. In which the investigated model is inherited from a reference. The HAC is established and optimized to minimize the traveling distance of the mobile robot and help it to avoid obstacles simultaneously. Simulations include one and two obstacle environments. Design variables, when optimizing, include the fuzzy parameters of linguistic variables and the reference range of state variables. This work is the first study in motion control of mobile robots based on the HA theory. The simulation data show that the proposed control rule base suits the mobile robot models. Therefore, the control efficiency of HAC is higher than that of a FC both in terms of the traveling distance of the robot and computation time (CPU time). Also, the establishment steps of the HAC controller show that HAC is more explicit, easier to optimize, and simpler to operate than FC. Research results in the present work also indicate that HAC can be developed and applied in motion control problems for different robot models with the advantages of a smaller traveling distance and faster computation time.
Versatile Remote Data Checking Scheme for Cloud-Assisted Internet of Things

Internet of Things (IoT) revolutionizes data collection, especially in e-healthcare, where patients data from wearables and sensors improves medical services. However, IoT’s limitations in computing and storage require cloud outsourcing. Combining IoT with the cloud has potential but raises concerns about data security. Leveraging cloud storage presents an attractive solution for accommodating the substantial volume of data outsourced by IoT devices. As the outsourcing of real-time data to cloud storage becomes commonplace, the adoption of data auditing schemes emerges as a means to ensure data integrity. To curtail operational expenses, various deduplication techniques are commonly employed on outsourced data, effectively sidestepping redundant data and resulting in storage and bandwidth efficiencies. Although real-time data typically remains distinct due to its diverse origins, scenarios such as data sharing or trading in data-driven services and datamarkets can lead to data redundancy. Moreover, in order to fortify against any potential information leakage, encryption is implemented prior to deduplication. Convergent Encryption (CE) stands as a prominent exemplar of this approach. Effectively integrating data auditing, deduplication, and encryption for wireless sensor devices is no trivial task. To efficiently and securely accommodate data while authenticating them through a heterogeneous framework, we present a novel remote data checking scheme, denoted as the VRDC scheme. This scheme empowers IoT data to be encrypted, updated, deduplicated, and audited, aligning with the imperatives of security, privacy, and efficiency. Through comprehensive security analysis, we establish that our VRDC scheme is fortified against potential threats. Our experimental findings highlight the efficiency of our approach in the realms of auditing, deduplication, and updates. Furthermore, the evidence highlights the potential for optimization within our scheme when compared to related works. This is achieved through the careful management of dynamic update scales within a file.
CLIPC: Contrastive Learning-Based Radar Signal Intra-Pulse Clustering

The radar signal intra-pulse clustering (RSIPC) can help achieve unsupervised radar emitter identification, which is of great significance in the field of electronic warfare. In order to address the poor performance of traditional clustering methods in handling RSIPC tasks, we propose a contrastive learning-based RSIPC method called CLIPC. Since the single-domain information of radar signal intra-pulses may result in the loss of important features, we integrate the multi-domain information of radar signal intra-pulses to obtain information fusion samples. By training a contrastive learning network on these information fusion samples, the network can extract deep features of radar signal intra-pulses. Subsequently, we realize RSIPC using these deep features. To enhance the adaptability of the used contrastive learning network, we optimize the data augmentation methods in the network through experimental analysis. Additionally, we optimize the dimension of the deep features extracted by the network to reduce information redundancy and improve the efficiency of features clustering. Experimental results demonstrate that our improvements in contrastive learning network lead to better clustering performance and efficiency. We also investigate the clustering performance and reliability of the CLIPC under different signal-to-noise ratios (SNRs) through experiments. When the SNR is 0dB, the proposed method has a clustering accuracy that is 0.1 higher than the contrastive learning method based on single-domain information, 0.3 higher than the traditional clustering method, and 0.2 higher than the clustering method based on auto-encoder.
PSAM: Parameter-Free Spatiotemporal Attention Mechanism for Video Question Answering

Spatiotemporal attention learning has always been a challenging research task in video question answering (VideoQA). It needs to consider not only the modelling of local neighbourhood dependencies between the adjacent frames in a video but also the modelling of long-term dependencies between nonadjacent frames. Although the existing methods are usually good at modelling temporal dependencies in one aspect, they cannot simultaneously and effectively model the temporal dependencies between adjacent and nonadjacent frames. To address this issue, we first derive a novel statistic-driven difference-aware generation function, which can efficiently calculate the difference between a sequence feature value and the whole mean value to identify the significance of the feature. Subsequently, we design a novel parameter-free spatiotemporal attention mechanism (PSAM), which captures the most relevant cues scattered in the context of a spatiotemporal video by generating functions and utilizes a gating mechanism to adaptively integrate and filter relevant and irrelevant information. Finally, we use the PSAM and hierarchical modelling to construct a lightweight multiscale context fusion- and reasoning-based VideoQA model. Extensive experimental research results obtained on five benchmark datasets for the VideoQA task show that our VideoQA model has high Q&A performance and lightweight characteristics. Simultaneously, comprehensive ablation experimental results show that the PSAM can not only improve the performance of the model but also significantly reduce the number of model parameters. In addition, extensive experimental findings obtained on the benchmark dataset of joint tasks (video moment retrieval and video highlight detection) further demonstrate that the PSAM is a general and effective spatiotemporal attention mechanism.
Computer Mouse Control Using Iris Tracking: an Accessible and Cost-Effective Approach for People with Mobility Disabilities

Over the years, technologies have made cameras and complex face-detection and eye-tracking systems available for computer and device uses. Due to various reasons, there are people with motor disabilities and limitations to using computational tools because they cannot control mouse functions. Since commercial systems for this purpose are limited and expensive, the present work aims to help people with reduced motor skills who cannot control the movements or functions of a computer. The proposed method is based on detecting and tracking iris movements to control mouse actions. Mathematical calculations were performed for iris tracking and the transformation of movements into mouse actions due to the proportionality of video resolution over screen size. In addition, mouse clicks are reproduced through blink control, which is performed based on distance comparison throughout the video capture. Experimental results show high precision in the mouse control through the developed tool. Finally, the source code is shared for free dissemination and use at: https://github.com/c0daa/proyecto-cursor
Selecting Application Frameworks Using Architectural Patterns and Tactics

Architects often evaluate and analyze application frameworks that implement architectural patterns that structure software designs to address quality attribute concerns. To satisfy the quality attributes through architectural patterns, these must be complemented by architectural tactics. Although architectural patterns pack architectural tactics, there has been little discussion on the effect of using architectural tactics to support architectural patterns to select application frameworks in architectural design. This study reports a controlled experiment with IT professionals (N = 28) that evaluates architectural patterns and tactics to select application frameworks. Two scenarios are considered. Scenario 1 included architectural patterns and tactics as decision mechanisms, while scenario 2 considered only architectural patterns. We used precision, recall, and a custom efficiency metric to compare the scenarios. The results indicate that scenario 1 produces more pragmatic and efficient solutions than scenario 2 does. Architectural tactics reduce space for solutions to make more precise decisions regarding architectural design.
Neural-Symbolic Recommendation with Graph-Enhanced Information

The recommendation task is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale. In this work, we combine the advantages of graph neural networks and propositional logic operations to construct a neuro-symbolic recommendation model with both global implicit reasoning ability and local explicit logic reasoning ability. We first build an item-item graph based on the principle of adjacent interaction and use graph neural networks to capture implicit information in global data. Then we transform user behavior into propositional logic expressions to achieve recommendations from the perspective of cognitive reasoning. Extensive experiments on five public datasets show that our proposed model outperforms several state-of-the-art methods, source code is avaliable at [ https://github.com/hanzo2020/GNNLR ].
Extending Neural Network Verification to a Larger Family of Piece-wise Linear Activation Functions

In this paper, we extend an available neural network verification technique to support a wider class of piece-wise linear activation functions. Furthermore, we extend the algorithms, which provide in their original form exact respectively over-approximative results for bounded input sets represented as start sets, to allow also unbounded input set. We implemented our algorithms and demonstrated their effectiveness in some case studies.
Multipartite entanglement encoded in the photon-number basis by sequential excitation of a three-level system

We propose a general scheme to generate entanglement encoded in the photon-number basis, via a sequential resonant two-photon excitation of a three-level system. We apply it to the specific case of a quantum dot three-level system, which can emit a photon pair through a biexciton–exciton cascade. The state generated in our scheme constitutes a tool for secure communication, as the multipartite correlations present in the produced state may provide an enhanced rate of secret communication with respect to a perfect GHZ state.