Reviewerly/exHarmony huggingface dataset:
Developing measures of immersion and motivation for learning technologies in healthcare simulation: a pilot study.

Medical education has benefitted from the introduction of new technology within recent years. Immersive devices, such as, 360-degree films and virtual reality have become new ways of simulating clinical experiences. The aim of the study was to validate and test reliability of a new measure of engagement.A between-participants design of 2 groups viewing a clinical consultation on a 360-degree headset or 2D monitor was conducted following computer random allocation of 40 healthcare professionals recruited from scheduled teaching. Twenty-three were assigned to 360-degree and 17 to 2D Medias. Adapted Immersion Experience Questionnaire (AIEQ) and Abridged Intrinsic Motivation Inventory (AIMI) were modified to match factors relating to clinical encounters. AIEQ and AIMI were utilised as the data collection tool by each group following video viewing. Spearman's rank correlation was used to assess relationship between immersion and motivation. Comparisons between 360-degree and 2D media responses were made using Wilcoxon's signed ranks test. Internal reliability coefficients of adapted measures were calculated with Cronbach alpha scores.Total immersion scores were statistically higher in those experiencing 360 (p<0.05), with a median difference of 14.50 (95% CI 6.50-22.00). A positive correlation existed between the total AIEQ and total score of the AIMI in both groups (rs =0.88, n=17, p<0.001). Internal consistency and reliability was demonstrated with a high Cronbach alpha score for the AIEQ (α= 0.91). AIMI subscale alpha value was also high at (α= 0.95) which shows the measures to be of high internal reliability.Adaptation and validation of existing measures for use in healthcare education can be used to quantify levels of immersion and motivation. Standardising measures for use in evaluating new Technology Enhanced Learning is a step to aid understanding on how we develop these tools in medical education and how we might learn from immersive technology.
Multi-Level Transformer-Based Social Relation Recognition

Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such as interactive robots and health self-management systems. The existing works about social relation recognition (SRR) focus on extracting features on different scales but lack a comprehensive mechanism to orchestrate various features which show different degrees of importance. In this paper, we propose a new SRR framework, namely Multi-level Transformer-Based Social Relation Recognition (MT-SRR), for better orchestrating features on different scales. Specifically, a vision transformer (ViT) is firstly employed as a feature extraction module for its advantage in exploiting global features. An intra-relation transformer (Intra-TRM) is then introduced to dynamically fuse the extracted features to generate more rational social relation representations. Next, an inter-relation transformer (Inter-TRM) is adopted to further enhance the social relation representations by attentionally utilizing the logical constraints among relationships. In addition, a new margin related to inter-class similarity and a sample number are added to alleviate the challenges of a data imbalance. Extensive experiments demonstrate that MT-SRR can better fuse features on different scales as well as ameliorate the bad effect caused by a data imbalance. The results on the benchmark datasets show that our proposed model outperforms the state-of-the-art methods with significant improvement.
Answering Hospital Caregivers’ Questions at Any Time: Proof-of-Concept Study of an Artificial Intelligence–Based Chatbot in a French Hospital

Access to accurate information in health care is a key point for caregivers to avoid medication errors, especially with the reorganization of staff and drug circuits during health crises such as the COVID‑19 pandemic. It is, therefore, the role of the hospital pharmacy to answer caregivers' questions. Some may require the expertise of a pharmacist, some should be answered by pharmacy technicians, but others are simple and redundant, and automated responses may be provided.We aimed at developing and implementing a chatbot to answer questions from hospital caregivers about drugs and pharmacy organization 24 hours a day and to evaluate this tool.The ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model was used by a multiprofessional team composed of 3 hospital pharmacists, 2 members of the Innovation and Transformation Department, and the IT service provider. Based on an analysis of the caregivers' needs about drugs and pharmacy organization, we designed and developed a chatbot. The tool was then evaluated before its implementation into the hospital intranet. Its relevance and conversations with testers were monitored via the IT provider's back office.Needs analysis with 5 hospital pharmacists and 33 caregivers from 5 health services allowed us to identify 7 themes about drugs and pharmacy organization (such as opening hours and specific prescriptions). After a year of chatbot design and development, the test version obtained good evaluation scores: its speed was rated 8.2 out of 10, usability 8.1 out of 10, and appearance 7.5 out of 10. Testers were generally satisfied (70%) and were hoping for the content to be enhanced.The chatbot seems to be a relevant tool for hospital caregivers, helping them obtain reliable and verified information they need on drugs and pharmacy organization. In the context of significant mobility of nursing staff during the health crisis due to the COVID-19 pandemic, the chatbot could be a suitable tool for transmitting relevant information related to drug circuits or specific procedures. To our knowledge, this is the first time that such a tool has been designed for caregivers. Its development further continued by means of tests conducted with other users such as pharmacy technicians and via the integration of additional data before the implementation on the 2 hospital sites.
Automation Attendance Systems Approaches: A Practical Review

Accounting for people is the first step of every manpower-based organization in today's world. Hence, it takes up a signification amount of energy and value in the form of money from respective organizations for both implementing a suitable system for manpower management as well as maintaining that same system. Although this amount of expenditure for big organizations is near to nothing, rather just a formality, it does not hold as much truth for small organizations such as schools, colleges, and even universities to a certain degree. This is the first point. The second point for discussion is that much work has been done to solve this issue. Various technologies like Biometrics, RFID, Bluetooth, GPS, QR Code, etc., have been used to tackle the issues of attendance collection. This study paves the path for researchers by reviewing practical methods and technologies used for existing attendance systems.
A review of Directed Reading Thinking Activity (DRTA) strategy in teaching reading comprehension

The purpose of this study is to ascertain whether the Directed Reading Thinking Activity (DRTA) technique enhances students' reading comprehension. The data used in this study were gathered using a library research methodology. Some of the literature sources used in this study included books and journals. The steps involved in the data analysis technique are data identification from pertinent literature sources, location identification, data acquisition, data evaluation, and data incorporation into the research presentation. The findings of this study demonstrate that students' reading comprehension abilities increased after receiving treatment with the DRTA technique employing its three fundamental steps (Predicting, Reading, and Proving). The students' test results supported the findings, and the application of the DRTA technique can assist pupils in resolving their reading difficulties. In conclusion, the DRTA approach is regarded as a successful teaching method that helps raise students' reading comprehension abilities.
Pemanfaatan Quizizz Sebagai Media Pembelajaran Berbasis Game Pada Masa Pandemi Covid-19

The Covid-19 pandemic has hit all countries in the world has had an impact on many sectors, including the education sector. Teaching and learning activities are transferred to a virtual face room. Online learning is carried out with the help of technology, one of those is quizizz which is a game-based learning media. This research is a qualitative research that aims to obtain information about the use of the Quizizz application in economic learning. The research subjects were high school and vocational high school students in Salatiga City, Boyolali Regency, and Semarang Regency. The results showed that the use of quizizz had a positive impact on students. Quizizz helped them to better understand the material, did not make them bored in learning, made them more enthusiastic, focused, and active in learning, relaxes when they were bored, makes students think critically, competitively, and broadly.
Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations

Recently, Deep Neural Networks (DNNs) have become a central subject of discussion in computer vision for a broad range of applications, including image classification and face recognition. Compared to existing conventional machine learning methods, deep learning algorithms have shown prominent performance with high accuracy and speed. However, they always require a large amount of data to achieve adequate robustness. Furthermore, additional samples are time-consuming and expensive to collect. In this paper, we propose an approach that combines generative methods and basic manipulations for image data augmentations and the FaceNet model with Support Vector Machine (SVM) for face recognition. To do so, the images were first preprocessed by a Deep Convolutional Generative Adversarial Net (DCGAN) to generate samples having realistic properties inseparable from those of the original datasets. Second, basic manipulations were applied on the images produced by DCGAN in order to increase the amount of training data. Finally, FaceNet was employed as a face recognition model. FaceNet detects faces using MTCNN, 128-D face embedding is computed to quantify each face, and an SVM was used on top of the embeddings for classification. Experiments carried out on the LFW and VGG image databases and ChokePoint video database demonstrate that the combination of basic and generative methods for augmentation boosted face recognition performance, leading to better recognition results.
2D Sin-Cos-Henon Map for Color Image Encryption with High Security

In this paper, a high security color image encryption algorithm is proposed by 2D Sin-Cos-HÃ©non (2D-SCH) system. A new two-dimensional chaotic system which is 2D-SCH. This system is hyperchaotic. The use of the 2D-SCH, a color image encryption algorithm based on random scrambling and localization diffusion, is proposed. First, the secret key is generated by SHA512 through plaintext. As the initial value of the 2D-SCH system, the secret key is used to generate chaotic sequences. Then, the random scrambling is designed based on chaotic sequences. Finally, a pair of initial points is generated by the secret key; the image diffuses around this point. The ciphertext is obtained by a double encryption. Different from the traditional encryption algorithm, this paper encrypts three channels of color image simultaneously, which greatly improves the security of the algorithm. Simulation results show that the algorithm can resist various attacks.
ferret: a Framework for Benchmarking Explainers on Transformers

As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable. While several explainable AI (XAI) techniques for interpreting the outputs of transformer-based models have been proposed, there is still a lack of easy access to using and comparing them. We introduce ferret, a Python library to simplify the use and comparisons of XAI methods on transformer-based classifiers. With ferret, users can visualize and compare transformers-based models output explanations using state-of-the-art XAI methods on any free-text or existing XAI corpora. Moreover, users can also evaluate ad-hoc XAI metrics to select the most faithful and plausible explanations. To align with the recently consolidated process of sharing and using transformers-based models from Hugging Face, ferret interfaces directly with its Python library. In this paper, we showcase ferret to benchmark XAI methods used on transformers for sentiment analysis and hate speech detection. We show how specific methods provide consistently better explanations and are preferable in the context of transformer models.
A Blind Robust Image Watermarking on Selected DCT Coefficients for Copyright Protection

This paper proposes a blind and robust image watermarking technique using Discrete Cosine Transform (DCT) for copyright protection on color images called BRIW-DCT.Each channel of the host image is divided into non-overlapping image blocks with the size of 8×8 pixels.Each image block is transformed into a frequency domain using the DCT transformation.The watermark image is embedded into the host image by modifying the 11 th to the 15 th DCT coefficient.The experimental result shows that the watermarked image achieved a high PSNR value of 50.4489 dB and a high SSIM value of 0.9991.Furthermore, various attacks are performed on the watermarked image.BRIW-DCT can successfully recover the watermark image from the tampered image, which produces a high NC value of 0.7805 and a low BER value of 0.1126.
Arabic Image Captioning: The Effect of Text Pre-processing on the Attention Weights and the BLEU-N Scores

Image captioning using deep neural networks has recently gained increasing attention, mostly for English langue, with only few studies in other languages.Good image captioning model is required to automatically generate sensible, syntactically and semantically correct captions, which in turn requires good models for both computer vision and natural language processing.The process is more challenging in case of data scarcity, and languages with complex morphological structures like the Arabic language.This was the reason why only limited number of studies have been published for Arabic image captioning, compared to those of English language.In this paper, an efficient deep learning model for Arabic image captioning has been proposed.In addition, the effect of using different text pre-processing methods on the obtained BLEU-N scores and the quality of generated images, as well as the attention mechanism behavior were investigated.Furthermore, the "THUMB" framework to assess the quality of the generated captions is used -for the first timefor Arabic captions' evaluation.As shown in the results, a BLEU-4 score of 27.12, has been achieved, which is the highest obtained results so far, for Arabic image captioning.In addition, the best THUMB scores were obtained, compared to previously published results on common images.
Descriptive Analytics System for Forest and Land Fire Patrol Data in Sumatra Indonesia

To prevent and suppress forest and land fires, the Ministry of Environment and Forestry conducts forest and land fire prevention patrol activities by forming a fire fighter team for each operational area.Previously, a mobile application and website had been developed for data acquisition carried out by the patrol team for forest and land fire prevention in the Sumatra region.The data that have been recorded through the Patroli Karhutla mobile application are still primary data.Presentation of primary data into data that users easily understand can be done through a business analytics approach.This study aims to develop an application that allows users to easily view data from reports and patrol activities to support decision-making.This study uses a geographic information system to analyze the data from patrols for forest and land fire prevention.This study focuses on analyzing and developing information systems using descriptive analysis and prototyping.Data visualization is presented in the form of maps to monitor the patrol data that have been collected through the mobile application.The functionality of the system has fulfilled the user's requirements.
The PDCA Approach with Seven Quality Tools for Quality Improvement Men’s Formal Jackets in Indonesia Garment Industry

Garment production with men's jacket products still finds products with poor quality and a high percentage of defects. The method used in this study is the PDCA approach with seven quality tools. The purpose of this study is to determine the cause of defects, provide solutions to repair these problems by reducing product defects, and reduce the percentage of dominant defects in the Finishing section. This study found that the factors causing the damage were human, machine, method, material, and environmental factors. Meanwhile, improvement efforts are contained in the Action stage, namely 5W+1H. The results of these improvements have an impact on the reduction of finishing defects from 17.8% to 11.8% which means a decrease in defects by 33.7% every month in the finishing section. The implications of this research can be used as a reference to reduce similar problems in the garment industry process. The combination of the PDCA approach and seven repair tools results in a defect percentage control system using a control chart so that defects will be controlled and corrective actions will be taken quickly.
End-to-End Mechanized Proof of an eBPF Virtual Machine for Micro-controllers

Abstract RIOT is a micro-kernel dedicated to IoT applications that adopts eBPF (extended Berkeley Packet Filters) to implement so-called femto-containers. As micro-controllers rarely feature hardware memory protection, the isolation of eBPF virtual machines (VM) is critical to ensure system integrity against potentially malicious programs. This paper shows how to directly derive, within the Coq proof assistant, the verified C implementation of an eBPF virtual machine from a Gallina specification. Leveraging the formal semantics of the CompCert C compiler, we obtain an end-to-end theorem stating that the C code of our VM inherits the safety and security properties of the Gallina specification. Our refinement methodology ensures that the isolation property of the specification holds in the verified C implementation. Preliminary experiments demonstrate satisfying performance.
Multiverse of HawkNess: A Universally-Composable MPC-Based Hawk Variant

The evolution of smart contracts in recent years inspired a crucial question: do smart contract evaluation protocols provide the required level of privacy when executing contracts on the blockchain? The Hawk (IEEE S&amp;P ’16) paper introduces a way to solve the problem of privacy in smart contracts by evaluating the contracts off-chain, albeit with the trust assumption of a manager. To avoid the partially trusted manager altogether, a novel approach named zkHawk (IEEE BRAINS ’21) explains how we can evaluate the contracts privately off-chain using a multi-party computation (MPC) protocol instead of trusting said manager. This paper dives deeper into the detailed construction of a variant of the zkHawk protocol titled V-zkHawk using formal proofs to construct the said protocol and model its security in the universal composability (UC) framework (FOCS ’01). The V-zkHawk protocol discussed here does not support immediate closure, i.e., all the parties (n) have to send a message to inform the blockchain that the contract has been executed with corruption allowed for up to t parties, where t&lt;n. In the most quintessential sense, the V-zkHawk is a variant because the outcome of the protocol is similar (i.e., execution of smart contract via an MPC function evaluation) to zkHawk, but we modify key aspects of the protocol, essentially creating a small trade-off (removing immediate closure) to provide UC (stronger) security. The V-zkHawk protocol leverages joint Schnorr signature schemes, encryption schemes, Non-Interactive Zero-Knowledge Proofs (NIZKs), and commitment schemes with Common Reference String (CRS) assumptions, MPC function evaluations, and assumes the existence of asynchronous, authenticated broadcast channels. We achieve malicious security in a dishonest majority setting in the UC framework.
Effects of Tai Chi and Walking Exercise on Emotional Face Recognition in Elderly People: An ERP Study

Both tai chi and walking can improve the physical health of the elderly, but the effect on the emotional cognitive function of the elderly is unclear. To investigate the effect of long-term walking and tai chi exercise on the emotional cognitive function of the elderly, 63 subjects were recruited in this study according to age and exercise habits, including 16 in the youth control group, 15 in the elderly non-exercise control group, 17 in the elderly walking group, and 18 in the elderly tai chi group. The "learning-test paradigm" of emotional faces was used to measure the subjects' ability to recognize and remember emotional (negative and neutral) faces. Behavioral and EEG data were recorded during the learning and testing phases. The results showed that there is aging in emotional cognition in older adults compared with younger adults. Long-term walking and tai chi exercise can delay the deterioration of emotional cognitive function in older adults to some extent. Both walking and tai chi exercise can delay the decline in aging-related emotional face recognition function to some extent. Walking exercise can delay the decline in aging-related emotional face memory function to some extent.
Automated Question-Answering for Interactive Decision Support in Operations &amp; Maintenance of Wind Turbines

Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support.The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs.Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions.In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines.The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine subcomponents, their properties and interactions, from a bespoke domain-specific knowledge graph.As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset.Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions.Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient.The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy.