Reviewerly/exHarmony huggingface dataset:
FRI016 Utilizing Game-based Learning In Medical Education

Abstract Disclosure: S. Daniel: None. K. Bangalore-Krishna: None. In the era of advancing technologies, several apps and software are available for learners that are fun, interactive, innovative, and collaborative. Kahoot! is one such game-based learning platform that facilitates formative assessment and promotes student learning. Kahoot! has been used extensively in higher education and has been successful not only in keeping the learners attentive and engaged but also provided with positive feedback and motivation. The philosophy of learning and education has shifted from being teacher-centered or subject-centered to being learner-centered and current learners tend to lean towards an abstract-active style of learning. We completed a focused discussion with the endocrine fellows and identified topics which were deemed challenging/difficult. We then developed a Kahoot!- based curriculum to review these topics. Each session included 2-3 review questions and 8-10 topic-based questions that were meticulously designed to cover the breadth of the topic followed by a 15-minute didactic session to review core concepts, including multiple clinical correlates to ensure relevance and applicability. Participants used their cellphones to log in and points were awarded (by the app) based on the accuracy and timeliness of responses. After 8 sessions, we conducted a brief survey with qualitative and quantitative questions to assess the effectiveness of the curriculum and contrasted this with the traditional curriculum which involved either a didactic session or watching videos with minimal interaction with an educator. All 8 pediatric endocrine fellows participated in the in-person, weekly sessions (8 total) and the survey. Results showed that 8/8 participants found this platform to be more interesting, easy to use, were motivated to learn during these sessions, engaging and encouraged participation without fear of being incorrect. Participants stated that these sessions helped them ask thought-provoking questions, understand previously complex concepts better and highlighted areas of knowledge deficits. Participants had additional suggestions to enhance deeper learning which we plan to incorporate. Our next step is to assess content retention at 3 months and 6 months after the sessions. With evolving technology and change in learning styles of current learners, game-based learning should be further explored in medical education, to keep learners engaged and make learning more fun, diverse, interactive, and inclusive. Presentation: Friday, June 16, 2023
The Concept of Motherhood in a Modern Fiction Text (Mother's Time by G. Sosiashvili)

The concept of motherhood&nbsp; was forming or changing according to cultures, traditions or religious and moral laws in the history of different people and, accordingly, in folklore or artistic texts. Over time, it has been developing from matriarchal-patriarchal to alienation with a dynamic drive. In the 21st century, this is reflected in the artistic text as well. The article presents it on the example of the writer Giorgi Sosiashvili's story "Mother's Time". The story told by the author of the prose text also includes the elements of the story of three people, in the background of which the vicissitudes of motherhood towards the aged mother of the child are formed. The "sunny-shady" and the "two-eyed" relationship of Ilia is developed more from the point of view of the child than from the mother's point of view, which is more or less alienated in accordance with the change of environmental conditions and the tradition brought by the new times. Through the eyes of the author, the world is seen with amazing, deep psychologism, in which filial love is associated with its fragrance, so strangely it stirs it. Approaching the time of death reinforces the need to spend the time of life as close as possible to the mother. The variety of characters seems to be different, but they unite the sunny and shadowy aspects of love for the mother. The article shows the development of the concept of motherhood by witnessing his mother's death, presenting a dreamlike narrative of being admitted to an asylum. The father also appears in the story in the background of the dialogue between the brothers, but is presented as a retrospect of his loving family upbringing. The language of the author, Giorgi Sosiashvili, is still calm here, with exceptionally expressive phraseology, which acquires the power of psychological loading. The face of the characters is given more compactly, as if nothing more is needed to say. The main story revolves around the mother. This fictional text of the 1920s of the 21st century is valuable as a true concept of motherhood that must exist.
IMPLEMENTASI PENGINPUTAN DATA COMPONENT ELECTRONICA BERBASIS WEB MENGGUNAKAN METODE AGILE

ABSTRACT&#x0D; Along with the rapid development of the times, human needs that are always increasing, especially increasingly advanced technology encourages every change that is much more modern. A need that is indispensable for an organization, company, agency, and so on is like a website. Where the website becomes a necessity both in business activities but also needed in every sales promotion and helps in work within an organization or company. PT. PCI is one of the companies that develops in the field of technology, where the company still inputs work component data manually so that the data input is less accurate. With the help of artificial intelligence can provide solutions in inputting data Entering component data and providing innovation using website-based technology can provide learning for every employee to be able to increase knowledge about technology, changes in a website-based method with manual input are very different, data that was originally input manually but can be made manually automatic. AGILE is known as a form of data development, the development works collaboratively, structured and organized so that the development is able to provide ease of work. With this research carried out in order to introduce how to input data properly and correctly. Artificial intelligence used on this website that produces input component data properly and correctly for use by employees in the work process, the use of artificial intelligence is expected to be an alternative media, so that it becomes more interesting in inputting data. &#x0D; &#x0D; Keywords: PT.PCI; Artificial Intelligence; Website; Agilel; Data component&#x0D;
Few-Shot Person Re-Identification Based on Meta-Learning with a Compression and Stimulation Module

This paper proposes a few-shot pedestrian re-identification (Re-ID) model based on an improved ResNet50 with a compression and stimulation module, which is named CS-ResNet50. It combines the meta-learning framework with metric learning. This method first compresses residual network channels, then stimulates them to achieve the effect of feature weighting, ultimately making feature extraction more accurate. The research makes the model learn how to finish new tasks efficiently from its experience that it has obtained in the training process of former subtasks. In each subtask, the dataset is divided into a gallery set and a query set, where the model parameters are trained. In this way, the model can be trained efficiently and adopted to new tasks rapidly, which could solve few-shot Re-ID problems. Compared with the baseline, the proposed model improves two indicators efficiently on two Re-ID datasets and achieves better Re-ID effect in few-shot mode.
Decentralized Heterogeneous Federal Distillation Learning Based on Blockchain

Load forecasting is a crucial aspect of intelligent Virtual Power Plant (VPP) management and a means of balancing the relationship between distributed power grids and traditional power grids. However, due to the continuous emergence of power consumption peaks, the power supply quality of the power grid cannot be guaranteed. Therefore, an intelligent calculation method is required to effectively predict the load, enabling better power grid dispatching and ensuring the stable operation of the power grid. This paper proposes a decentralized heterogeneous federated distillation learning algorithm (DHFDL) to promote trusted federated learning (FL) between different federates in the blockchain. The algorithm comprises two stages: common knowledge accumulation and personalized training. In the first stage, each federate on the blockchain is treated as a meta-distribution. After aggregating the knowledge of each federate circularly, the model is uploaded to the blockchain. In the second stage, other federates on the blockchain download the trained model for personalized training, both of which are based on knowledge distillation. Experimental results demonstrate that the DHFDL algorithm proposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus (BFLC). Additionally, by combining asynchronous consensus with the FL model training process, the DHFDL training time is the shortest, and the training efficiency of decentralized FL is improved.
Typological Differences of Natural and Neural Network-Generated Texts in a Quantitative Aspect

The authors of this article identify distinctive features in texts written by humans and texts generated by the GPT-3 neural network. Texts generated by GPT-3 have not yet been subject to systematic in-depth study. In total, 160 texts were analyzed in the article, distributed across four topics (“Higher Education in My Eyes,” “How to Remain Human in Inhuman Conditions,” “How I Spent the Summer,” “Teacher of the Year”), with 80 texts generated by the neural network and 80 texts written by humans. The texts were analyzed using quantitative linguistic methods. A concordance was compiled for each text using the AntConc program, from which quantitative values were obtained for further analysis. The authors reached the following conclusions: (1) in the generated texts, words included in the title occur with the highest frequency; (2) the relative frequency of words included in the title is unreasonably inflated; (3) the list of the 20 most frequent words in all generated texts includes the highest number of full-fledged words; (4) the lexical diversity coefficient in the examined natural texts is significantly higher than that of the generated texts. The findings of this research can be useful for both educators and machine learning specialists.
Anomaly Detection in Smart Agriculture Systems on Network Edge Using Deep Learning Technique

With the widespread adoption of Internet of Things (IoT) technologies across various domains, including smart agriculture, urban environments, and homes, the threat of zero-day attacks has surged. This research delves into the application of deep learning techniques to detect anomalies in smart agricultural systems at the network edge, with a specific focus on safeguarding them against Distributed Denial of Service (DDoS) attacks. In this study, we propose an anomaly detection model based on CNN-LSTM to analyze sensor data collected from IoT devices. We rigorously train and test our model using two distinct datasets of sensor readings, simulating potential DDoS attack scenarios. The model's performance is assessed using key metrics such as detection accuracy, recall, and F1-score. Our results demonstrate the effectiveness of our approach, achieving an impressive anomaly detection accuracy of 99.7%. This research contributes significantly to the development of robust and efficient attack and anomaly detection techniques for smart agriculture systems at the network edge, ultimately enhancing the reliability and sustainability of agricultural practices.
Experimental Investigations and Modeling of Interference Fringe Geometry in Line-Shaped Gaussian Beam Intersections for Laser Doppler Sensors

Line-shaped beam-based Doppler distance sensors enable 3D shape measurements of rotating objects, for instance for working lathe monitoring with a simultaneous, multipoint velocity, and distance measurement. The velocity and distance estimations are fundamentally referred to the interference fringe spacing of the sensors. In this case, the fringe spacing variation-caused measurement error is significant; however, a complete, accurate model of the fringe geometry distribution for the line-shaped fringe volume is still missing and required to identify and minimize the error. Therefore, this work presents a high-accuracy 3D model for the fringe spacing evaluation of the sensors with experimental investigations. The model is derived from the phase expression of Gaussian beams introducing extension ratio, and can be universally applied to describe fringe geometry distribution throughout the intersection volume of spherical and line-shaped beams. With an experimental setup of a laser Doppler sensor, a full-field fringe spacing estimation using a high-resolution matrix camera is performed with dual-wavelength beams. The numerical modeling and experimental results show an average relative difference below 0.6%.
THE PRACTICE OF PEDAGOGICAL DIGITAL COMPETENCE AMONG ESL LECTURERS

With the goal of having students graduate with 21 century skills, there is a gap on who are the ambassador of the integration of digital skills. This is where language lecturers can be seen as role models of digital skills. Yet, there is a deficiency in studies looking at ESL lecturers’ use of technology in terms of pedagogical use. Taking in the lens of Krumsvik’s Teachers’ Digital Competence Model, the purpose of the qualitative study is to explore the practice of Pedagogical Digital Competence (PDC) among English as Second Language (ESL) lecturers through 14 in-depth interviews analyzed by Nvivo 12 software to define the themes and codes that emerged. The results revealed the lecturers’ use of ICT in the mastery of digital tools utilized for support their teaching, technological challenges faced during the implementation of said skills, as well as the cognizance of ethics in navigating the online world. It is interesting to note that the ESL lecturers highlighted policies restricting usage of technology in terms of formal assessments. The findings are highly relevant to bridging the gap in the growing literature of Malaysian ESL Lecturer’s PDC.
Sign Language Detection and Conversion to Text and Speech Conversion

Sign language is one of the oldest and most natural form of language for communication, but since most people do not know sign language and interpreters are very difficult to come by we have come up with a real time method using neural networks for fingerspelling based american sign language. In this method, the hand is first passed through a filter and after the filter is applied the hand is passed through a classifier which predicts the class of the hand gestures. This method provides 98.00 % accuracy for the 26 letters of the alphabet. Key Words: Python, Machine Learning, OpenCV, Keras, NumPY, Data Training
Analysis of Productivity Effects of Smart Factory Construction

Purpose This study aims to report an actual case of smart factory by choosing one of domestic small and medium-sized enterprises and eventually implement smart factory initiatives successfully across the country. This research demonstrates performance improvement through smart factory with several performance measures to present the guidelines for supporting Korean small and medium-sized enterprises.Methods A case study is carried out to check the effects of smart factory on a parts manufacturer in the defense industry. As one of the information systems for smart factory, a manufacturing execution system is applied to all the manufacturing processes of the parts manufacturer as an effort to integrate information in real time. In order to show performance improvement, this research makes the comparisons of several performance measures before and after the implementation of the smart factory.Results This paper quantitatively reveals that smart factory reduces inventory and improves the efficiency of the equipment as well as the productivity of the manufacturing process.Conclusion As a result of the analysis, it is shown that the improvement is made by analyzing and utilizing the data and information collected in real time in the smart factory.
How inclusivity can help develop machine translation (MT) literacy and autonomy

This article reports on the innovative design of an autonomy workshop that allowed us to accommodate the very diverse social and learning background of our students. We based the inclusive strategies on the Universal Design for Instruction (UDI) (Scott and Edwards 2018) and specifically worked on the choice of tools, the task creation process and the method of instruction. This design also help foster learners’ autonomy and their digital literacy, alongside most traditional language-related goals.
Further Topics around Encryption

This book offers an accessible and engaging introduction to quantum cryptography, assuming no prior knowledge in quantum computing. Essential background theory and mathematical techniques are introduced and applied in the analysis and design of quantum cryptographic protocols. The title explores several important applications such as quantum key distribution, quantum money, and delegated quantum computation, while also serving as a self-contained introduction to the field of quantum computing. With frequent illustrations and simple examples relevant to quantum cryptography, this title focuses on building intuition and challenges readers to understand the basis of cryptographic security. Frequent worked examples and mid-chapter exercises allow readers to extend their understanding, and in-text quizzes, end-of-chapter homework problems, and recommended further reading reinforce and broaden understanding. Online resources available to instructors include interactive computational problems in Julia, videos, lecture slides, and a fully worked solutions manual.
Merging Automation With Blockchain Technology

This article describes a novel approach to improve business process automation by introducing blockchain technology features into the design of a software solution.
A Multi-Head Attention Network Integrating Knowledge Graph and Collaborative Filtering

In the field of recommendation systems, knowledge graphs (KG) have received widespread attention because they can proficiently address the challenges of sparsity and cold start encountered by conventional recommendation algorithms. The existing KG-based recommendation methods mainly consider how to fully utilize the knowledge information in the knowledge graph, ignoring the collaborative information provided by user-item interaction. This leads to the fact that the embedding vector learned by the existing model fails to effectively express the potential semantics of entities in the vector space. To tackle this challenge, this paper proposes a multi-head attention network integrating knowledge graph and collaborative filtering, which is named as collaborative knowledge-aware multi-head attention network (CKAN-MH). This network introduces a multi-head attention mechanism based on the traditional CKAN model to adaptively focus on subsets of different features, and achieves dynamic weighting of tail entities by adjusting attention weights. The addition of multi-head attention enables the model to better grasp complex relationships and patterns in the data, thereby improving recommendation performance. In addition, we applied the proposed CKAN-MH model on three real-world datasets and conducted experimental evaluations. The results indicate that CKAN-MH is significantly superior to several popular advanced baseline models, demonstrating its effectiveness and superiority.
Mixed Strategy-Based Improved Pelican Optimization Algorithm

The new Pelican optimization Algorithm (POA) suffers from defects such as easy to sink into local optimum and premature convergence, which is not conducive to be applied in optimization problems in various complex domains, we propose a mixed strategy-based improved Pelican optimization Algorithm (MSPOA). The initial solution is produced using an Elite opposition-Based Learning mechanism, which improves the swarm quality; the Lévy flight mechanism is added to improve the swarm variegation and avoid sinking into a local optimum; finally, the adaptive t-distribution mutation mechanism is introduced at the end of development phase to enhance the global search capability while effectively balancing exploration and development. The challenging CEC2021 test functions are selected for simulation experiments. The CEC test functions are different from the basic functions in that the optimal solutions of these functions are no longer located at the coordinate origin, which can objectively test the algorithm performance, while five well-known meta-heuristic algorithms are chosen for comparison. The results and Wilcoxon rank-sum test indicate that MSPOA has even better global optimizttion ability, faster convergence speed, better stability and stronger ability to jump from the local optimum.
Multi-Level Feature Correction Inference Network for 3D Face Reconstruction and Dense Alignment

3D face reconstruction and dense alignment task has long been regarded as a challenging research problem, particularly in unconstrained environments, which has garnered significant attention in the computer vision community. In this paper, we propose a Multi-Level Feature Correction Inference Network (MFCINet), which is capable of recovering 3D faces with rich details and achieving more accurate dense alignment in unconstrained environments. Most traditional methods directly regress face identity and expression parameters, resulting in reconstructed 3D faces lacking fine-grained information. To address this issue, we utilize multi-level features to correct the features of the backbone network, yielding more robust prediction results. Specifically, we first employ a shallow network to learn the low-level spatial information of faces. This allows the model to better understand spatial relationships, such as relative positions, sizes, and orientations between facial components, by learning edge information and texture features. Secondly, we leverage a deep network to learn the high-level semantic information of faces, which enables the model to better capture semantic correlations in facial data and achieve a more accurate understanding and representation of facial semantic information. Finally, by combining the low-level spatial information and high-level semantic information, and applying a correction operation, we inject all the information into the backbone network for accurately reasoning. The effectiveness of our method is substantiated by extensive experiment conducted on the AFLW2000-3D and AFLW datasets.
Research on A Gesture Recognition Method of Arc Curve

Gesture recognition is an important way of human-computer interaction, improving gesture recognition rate is of great significance for human-computer interaction applications. To solve the problems of few categories and low performance of gesture recognition in traditional methods, a new method of gesture recognition based on circular arc curve is proposed in this paper. Firstly, the hand detection arc curve based on geometric characteristics of the hand is constructed. Then, key pixels intersecting with the curve are selected, clustered and are divided into different categories according to their spatial adjacency. Then spatial angle of key pixels is calculated by vectorial angle method. Finally, number and angle of the categories are used as gesture features, and gesture recognition is based on decision table. Experimental results show that the method has the characteristics of high recognition rate, high performance and good robustness.