Reviewerly/exHarmony huggingface dataset:
Physics language and language use in physics--What do we know and how AI might enhance language-related research and instruction

Abstract Language is an important resource for physicists and learners of physics to construe physical phenomena and processes, and communicate ideas. Moreover, any physics-related instructional setting is inherently language-bound, and physics literacy is fundamentally related to comprehending and producing both physics-specific and general language. Consequently, characterizing physics language and understanding language use in physics are important goals for research on physics learning and instructional design. Qualitative physics education research offers a variety of insights into the characteristics of language and language use in physics such as the differences between everyday language and scientific language, or metaphors used to convey concepts. However, qualitative language analysis fails to capture distributional (i.e., quantitative) aspects of language use and is resource-intensive to apply in practice. Integrating quantitative and qualitative language analysis in physics education research might be enhanced by recently advanced artificial intelligence-based technologies such as large language models, as these models were found to be capable to systematically process and analyse language data. Large language models offer new potentials in some language-related tasks in physics education research and instruction, yet they are constrained in various ways. In this scoping review, we seek to demonstrate the multifaceted nature of language and language use in physics and answer the question what potentials and limitations artificial intelligence-based methods such as large language models can have in physics education research and instruction on language and language use.
Ion shuttling based on accurate potential control technology

We have developed fast and accurate protocols for ion shuttling in a surface-electrode ion trap, which allows efficient splitting and merging of two ions. These protocols are based on innovative improvements in the driving potential field model, where voltage is introduced as a variable parameter. This enhancement specifically addresses the issue of increased control errors that occur with larger inter-ion distances. We have devised a general model-solving procedure that uses a parameterized model to describe the total potential energy of the two ions and ions oscillating eigenfrequency in a dynamical normal modes. By employing optimization techniques, we determine optimal voltage ramps for the dynamic potential field, enabling accurate control of physical quantities during the splitting process. In additionally, through simulating the classical dynamics of the theoretical scheme, we decouple the ion oscillations by employing discrete Fourier transform (DFT) and nonlinear fitting techniques. Two ions are separated into individual potential wells with a distance of 200 <i>&mu;𝑚</i> within 50 <i>&mu;s</i>. The ion position drift is only 0.01%. During the splitting process, the frequency fluctuation of the center-of-mass mode of the ions is less than 0.69%, and the minimum average excitation of a single ion is 0.02 phonon. Accurate and fast ion shuttling technology facilitates the development of large-scale trapped-ion quantum computing. Maintaining the ions oscillating eigenfrequency constant during the shuttling operation alleviates the problem of decoherence of quantum information.
DAMPAK BUDAYA LITERASI TERHADAP KARAKTER TANGGUNGJAWAB PESERTA DIDIK PADA ABAD 21

The application of a literacy culture can have an impact on strengthening the character of elementary school students. A literacy culture that is instilled early in elementary school can continuously form good character in elementary school children. This study aims to determine the impact of literacy culture on the responsible character of students in the 21st century. The research study was conducted using a quantitative approach using the ex post facto method. The population in this study were 87 students from SD Negeri Bawakareang I Makassar City from grades 4, 5 and 6, while the sample consisted of 55 students obtained by random sampling technique. The data collection method used a questionnaire and was analyzed using SPSS 21. The results of the descriptive analysis showed that a literacy culture of 55% was in the very influential category and the character of responsibility was in the very good category with a percentage value of 53. The results of the inferential analysis showed that there was an influence of literacy culture on the character of the participants' responsibilities students in the 21st century by obtaining a value of Sig 0.000 as determined by Sig (0.000) &lt; (0.05) so that H0 is rejected and H1. The implications of this research are (1) Teachers should pay attention to the media used as teaching materials; (2) Students who are digitally literate must be motivated to develop their learning outcomes (3) A literacy culture can foster an interest in reading and have an impact on the character of responsibility; (4) For other researchers, they can conduct studies outside the culture of literacy and character of responsibility.
Optimization of Artificial Potential Field Using Genetic Algorithm for Human-Aware Navigation of Autonomous Mobile Robots

Path planning is an indispensable capability for an autonomous mobile robot to formulate a path from the initial position to the desired position while maneuvering safely from obstacles. In traditional path planning methods, the task constraint might be to generate a minimum energy or minimum distance path. However, in human-rich environments, apart from avoiding obstacles, the mobile robot must also navigate by recognizing and acting according to the social norms of humans. Hence, this study proposes an artificial potential field (APF)-based human-aware navigation framework for an autonomous mobile robot. Also, the genetic algorithm is utilized to optimize the scaling factors and the orders of potential functions of the APF. As a result, the shortest path from the start to the desired position is found for autonomous mobile robots without violating human factor constraints.
Perspectives and Final Remarks

In this chapter, we consider perspectives in deep learning for fluid animation. Firstly, the input data generation for neural network training depends on simulating fluid dynamics using traditional methods to produce the training data, which is time-consuming. To address this issue, we describe the basic model for physics-informed neural networks (PINNs) applied to the Navier–Stokes simulation. In this case, the fluid equations and the initial and boundary conditions are included in the model as new terms in the loss function, which can reduce the necessity for large training databases. Secondly, the success of deep architectures happens with the increase in the difficulties of understanding how deep neural networks come to decisions. This fact motivates the development of explainable artificial intelligence (XAI) techniques, like Shapley additive explanations (SHAP), to quantify the importance of hidden layers for model outcomes. From the rendering perspective, machine learning is a new approach that brings many exciting possibilities to improve visual results for the entertainment industry. But at the same time, it still has drawbacks to scientific applications, where visual accuracy is a vital requirement. We end the material by discussing how machine learning techniques improve the rendering algorithms.
An energy-efficient task and virtual machine placement in virtualised cloud server using FY-SFLA and RMMS-DLVQ

Creating infrastructures, virtual servers, computing resources, along with devices is termed virtualisation. In this methodology, to augment resource usage along with to mitigate the total power consumption, mapping of a group of virtual machine (VM) onto a set of physical machines (PM) is performed in a data centre (DC). Nevertheless, a crucial challenge is presented by the VM allocation together with the higher energy consumption (EC) of cloud data centres (CDC). Thus, to alleviate the resource wastage along with to mitigate the DCs' EC, an effectual Fisher Yates-Shuffled frog leaping algorithm (FY-SFLA) is proposed here: 1) task feature extraction; 2) resource information extraction; 3) task separation by utilising cosine distance - K means algorithm (CD-KMA); 4) task placement in VM by employing the FY-SFLA task; 5) VM status identification by deploying random mutation monkey search deep learning vector quantisation (RMMS - DLVQ) are '5' phases comprised in the proposed methodology.
Pelatihan Desain Media Pembelajaran Berbasis ICT

The purpose of the information and communication technology-based learning media design training is to improve the skills of teachers in making teaching materials using powerpoint media and making quiz questions, assignments, or exams using the wondershare quiz creator media. The methods used in the service are training and workshops. The subjects in this training were teachers of MTs Negeri 4 Maluku Tengah and MA Negeri 3 Maluku Tengah. After this training, the teacher participating in the activity got additional insight into the use of powerpoint media and the Wondershare Quiz Creator application and was also skilled in using both media. Keywords: learning media, information dan communication technology, powerpoint, wondershare quiz creator.
Perancangan Sistem Informasi Geografis Pemetan Jalan Pada Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Lombok Tengah Menggunakan Metode Waterfall

The road is a means of land transportation that has a function to connect one area to another. Therefore the development of an area can run well if access and mobility are not disturbed or hindered. Road conditions are very important in facilitating the mobility of various sectors such as the economic, industrial, trade and transportation sectors. The Central Lombok PUPR Service has duties regulated in government regulation number 38 of 2007 regarding the division of government affairs between the provincial government and district/city regional governments. In accordance with regional regulation number 11 of 2016 concerning the formation and composition of the regional apparatus of the NTB province, the position of the public works and spatial planning office of the NTB province has the task of assisting the governor in carrying out regional government affairs in the field of public works and spatial planning geographic information system design for road mapping at the PUPR Service. make it easier for officers to check the road. It is hoped that this system can improve data management, so as to create quality road construction in the Central Lombok Regency area.&#x0D; &#x0D;
Distributed Solution of the Blendshape Rig Inversion Problem

The problem of rig inversion is central in facial animation, but with the increasing complexity of modern blendshape models, execution times increase beyond practically feasible solutions. A possible approach towards a faster solution is clustering, which exploits the spacial nature of the face, leading to a distributed method. In this paper, we go a step further, involving cluster coupling to get more confident estimates of the overlapping components. Our algorithm applies the Alternating Direction Method of Multipliers, sharing the overlapping weights between the subproblems and show a clear advantage over the naive clustered approach. The method applies to an arbitrary clustering of the face. We also introduce a novel method for choosing the number of clusters in a data-free manner, resulting in a sparse clustering graph without losing essential information. Finally, we give a new variant of a data-free clustering algorithm that produces good scores with respect to the mentioned strategy for choosing the optimal clustering.
Server node video processing based on feature depth analysis algorithm

The complex and diverse server video data leads to the problem of effective retrieval of these data. The current shot edge detection algorithm and key frame extraction algorithm in server node video processing have problems such as poor extraction performance and poor adaptability. Therefore, the research combined the feature depth analysis to improve the two, and the performance was verified by experiments. The shot detection algorithm is verified by modifying the secondary detection model. This method can detect lens mutation, gradual change and other phenomena well, and the accuracy rate can reach 99.7%. The precision under the gradient lens is 92.08%, far higher than 63.50% and 85.39% of ISIFT and CS-DFS. In the verification experiment using Convolution Neural Networks (CNNs) key frame extraction algorithm, the number of key frame extractions of the proposed algorithm can reach up to 88 frames. Compared with other methods, the accuracy of the algorithm studied can reach 99.67%, which is higher than the comparison algorithm. In general, the improved algorithm proposed in the study has high adaptability to edge detection and the ability to express key frame video, and has high practicability in actual server node video processing.
Chinese Medical Intent Recognition Based on Multi-feature Fusion

The increasing popularity of online query services has heightened the need for suitable methods to accurately understand the truth of query intention. Currently, most of the medical query intention recognition methods are deep learning-based. Because of the inadequate of corpus of the medical field in the pre-trained phase, these methods may fail to accurately extract the text feature constructed by medical domain knowledge. What's more, they rely on a single technology to extract the text information, and can't fully capture the query intention. To mitigate these issues, in this paper, we propose a novel intent recognition model called EDCGA (ERNIE-Health+D-CNN+Bi-GRU+Attention). EDCGA achieves text representation using the word vectors of the pre-trained ERNIE-Health model and employs D-CNN to expand the receptive field for extracting local information features. Meanwhile, it combines Bi-GRU and attention mechanism to extract global information to enhance the understanding of the intent. Extensive experimental results on multiple datasets demonstrate that our proposed model exhibits superior recognition performance compared to the baselines.
MaxCorrMGNN: A Multi-graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction

With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data. Predicting outcomes effectively requires fusion frameworks capable of modeling fine-grained and multi-faceted complex interactions between modality features within and across patients. We develop an innovative fusion approach called MaxCorr MGNN that models non-linear modality correlations within and across patients through Hirschfeld-Gebelein-Reǹyi maximal correlation (MaxCorr) embeddings, resulting in a multi-layered graph that preserves the identities of the modalities and patients. We then design, for the first time, a generalized multi-layered graph neural network (MGNN) for task-informed reasoning in multi-layered graphs, that learns the parameters defining patient-modality graph connectivity and message passing in an end-to-end fashion. We evaluate our model an outcome prediction task on a Tuberculosis (TB) dataset consistently outperforming several state-of-the-art neural, graph-based and traditional fusion techniques.
STOP SAYING VERY: AN EXCEPTIONAL TEACHING STRATEGY TO ENHANCE STUDENTS’ VOCABULARY MASTERY

This Classroom Action Research (henceforth, CAR) was primarily carried out to prove that the use of Stop saying very teaching strategy is effective to enhance the students’ vocabulary mastery on adjectives and to describe the students’ responses towards the learning teaching processes of adjectives through the application of Stop saying very teaching strategy. There are two cycles in this CAR in which each cycle consisted of three meetings excluded the pre-test, formative test, and post-test The research subjects of this CAR are the first semester students of the English Language Education, Faculty of Teachers Training and Education, Universitas Katolik Santo Thomas in the Academic Year of 2022/2023 consisting of 20 students. The analysis of quantitative data shows that the mean of the students’ score in the pre-test, formative test, and post-test increases continually after Stop saying very teaching strategy was implemented in teaching adjectives. The students’ total score in the pre-test is 216 and the pre-test mean is 10.8. The students’ total score in the formative test is 1.528 and the formative test mean is 76.4. The students’ total score in the post-test is 1.708 and the post-test mean is 85.4. The analysis of qualitative data shows that the students gave positive responses towards the learning teaching processes of adjectives through the application of Stop saying very teaching strategy. They all agree that the application of Stop saying very teaching strategy helps trigger their curiosity and enhance their knowledge and vocabulary mastery on adjectives. The writers recommend that English teachers/lecturers apply Stop saying very teaching strategy to teach better and more impressive adjectives to their students.&#x0D; Keywords: adjectives, stop saying very, teaching strategy, vocabulary mastery&#x0D; &#x0D;
Experimental Comparative Study of Three Models of Convolutional Neural Networks for Emotion Recognition

Many researchers agree that facial expressions are one of the main non-verbal ways that human beings use to communicate and express emotions. For this reason, there has been a significant increase in interest in capturing facial movements to generate realistic digital animations incorporated into virtual environments. Our general project has the final objective of developing and evaluating different models of convolutional neural networks (CNN). These models will fit a linear “blendshapes” model of facial expression from images obtained by a Head-mounted display (HMD). At this stage of the project, in this paper, our goal is to compare, through different evaluation metrics (accuracy, runtime, and so on), three CNN models designed to detect emotions. These models differ in the way they treat input images. Two of the models partition the input images; one divides the images into 3 fundamental parts: forehead, eyes + nose, and mouth + chin; the other divides the images, right in the middle, into two presumably symmetrical parts. The third model works with full images. In these first proposals, we used 2D images with frontal faces in black-and-white to train all the CNN models. The main experiments are carried out on the database “The Japanese Female Facial Expression (JAFFE). JAFFE dataset contains 213 images categorized into 7 facial expressions, 6 basic facial expressions (Happiness, Sadness, Surprise, Anger, Disgust, and Fear) + 1 neutral. The three proposed models yielded satisfactory results in terms of accuracy. In addition, the training time remained within acceptable values.
An Image Encryption Method Based on Lorenz Chaotic Map and Hunter-Prey Optimization

Through the development of communication technology, fast and efficient tools are required to practically secure the process of data exchange in securing images. This paper presents a new method of encryption for protecting images against many attacks from unsafe public networks. Lorenz chaos map is used to generate a sequence of random numbers for each stage depending on the initial parameters. The Hunter Prey optimization algorithm is applied in order to obtain these parameters to use them based on the original image. Therefore, the random sequence number generated by the Lorenz chaotic map will be different from one image to another. That will make it unpredictable and very difficult to discover the process of encryption. The results of simulation experiments demonstrate that the encryption algorithm have passed the plaintext sensitivity test with the NPCR of 0.99785 and the UACI of 0.33623. As well as the correlation coefficient test values in the three directions gave the values of (v = -0.0007, h = -0.0000, d = 0.0005). Also, the calculated information entropy test value was 7.9983. These results demonstrate that this algorithm is very strong enough to withstand the various types of attacks that images can be exposed during transmission on the Internet or any public network. The security analysis's comparison of the proposed changes to similar ones revealed that the proposed encryption system is more efficient.
User Satisfaction Modeling with Domain Adaptation in Task-oriented Dialogue Systems

User Satisfaction Estimation (USE) is crucial in helping measure the quality of a task-oriented dialogue system. However, the complex nature of implicit responses poses challenges in detecting user satisfaction, and most datasets are limited in size or not available to the public due to user privacy policies. Unlike task-oriented dialogue, large-scale annotated chitchat with emotion labels is publicly available. Therefore, we present a novel user satisfaction model with domain adaptation (USMDA) to utilize this chitchat. We adopt a dialogue Transformer encoder to capture contextual features from the dialogue. And we reduce domain discrepancy to learn dialogue-related invariant features. Moreover, USMDA jointly learns satisfaction signals in the chitchat context with user satisfaction estimation, and user actions in task-oriented dialogue with dialogue action recognition. Experimental results on two benchmarks show that our proposed framework for the USE task outperforms existing unsupervised domain adaptation methods. To the best of our knowledge, this is the first work to study user satisfaction estimation with unsupervised domain adaptation from chitchat to task-oriented dialogue.
Beyond the Bias: Unveiling the Quality of Implicit Causality Prompt Continuations in Language Models

Recent studies have used human continuations of Implicit Causality (IC) prompts collected in linguistic experiments to evaluate discourse understanding in large language models (LLMs), focusing on the well-known IC coreference bias in the LLMs’ predictions of the next word following the prompt. In this study, we investigate how continuations of IC prompts can be used to evaluate the text generation capabilities of LLMs in a linguistically controlled setting. We conduct an experiment using two open-source GPT-based models, employing human evaluation to assess different aspects of continuation quality. Our findings show that LLMs struggle in particular with generating coherent continuations in this rather simple setting, indicating a lack of discourse knowledge beyond the well-known IC bias. Our results also suggest that a bias congruent continuation does not necessarily equate to a higher continuation quality. Furthermore, our study draws upon insights from the Uniform Information Density hypothesis, testing different prompt modifications and decoding procedures and showing that sampling-based methods are particularly sensitive to the information density of the prompts.
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions

Neeraj Cherakara, Finny Varghese, Sheena Shabana, Nivan Nelson, Abhiram Karukayil, Rohith Kulothungan, Mohammed Afil Farhan, Birthe Nesset, Meriam Moujahid, Tanvi Dinkar, Verena Rieser, Oliver Lemon. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.