Reviewerly/exHarmony huggingface dataset:
Semantically Enhanced Scene Captions with Physical and Weather Condition Changes

Vision-Language models (VLMs), i.e., image-text pairs of CLIP, have boosted image-based Deep Learning (DL). Moreover, Visual-Question-Answer (VQA) tools and open-vocabulary semantic segmentation provide us with more detailed scene descriptions, i.e., qualitative texts, in captions. Images from surveillance, auto-drive, and mobile phone cameras have been used with segmentation and captions. However, unlike indoor scenes, outdoor scenes with uncontrolled illumination and noise can degrade the accuracy of segmented objects. Moreover, unpredictable events such as natural phenomena and accidents can cause dynamic and adverse scene changes over time. This greatly increases unseen objects due to sudden changes. Therefore, only a single state-of-the-art (SOTA) VLM and DL model cannot sufficiently generate and enhance captions. Even one time VQA is limited to generate a good answer. This paper proposes RoadCAP for refined and enriched qualitative and quantitative captions by DL models and VLMs with different tasks in a complementary manner. In particular, 2D-Contrastive Physical-Scale Pretraining (CPP) is also proposed for captions with physical scales. An iterative VQA model is proposed to further refine incomplete segmented images with the prompts. Experimental results outperform SOTA DL models and VLMs using images with adverse conditions. A higher semantic level in captions for real-world scene descriptions is shown as compared with SOTA VLMs.
Facsimiles-based deep learning for matching relief-printed decorations on medieval ceramic sherds

In this paper, we addressed the problem faced by archaeologists in associating relief-printed decorations on ceramic objects discovered during excavations carried out with the same wheel. This is crucial to understand the trade networks between regions, but highly complex and time-consuming task. We used two approaches: supervised classification or unsupervised clustering of 2D relief views generated from 3D scans of ceramic sherds. Inspired by experimental archaeology, we created wheel facsimiles to supplement significantly the database with numerous plausible and clearly identified samples. Taking advantage of the powerful convolutional neural network EfficientNet to extract reliable discriminating features, experimental results show that the facsimiles significantly improve the networks’ training to achieve a classification accuracy exceeding 95% on real sherds. On the other hand, unsupervised spectral clustering from a vector reduced to a few hundred of the most significant features delivered by the network EfficientNet-B5 trained on ImageNet, without any fine-tuning, achieves an accuracy of 77.47% on our database. These results validate the strategy of using facsimiles to supplement a too-small data set and are very promising for the development of a computer-assisted archaeology tool for pattern-wheel association.
Clustering-based Domain-Incremental Learning

We consider the problem of learning multiple tasks in a continual learning setting in which data from different tasks is presented to the learner in a streaming fashion. A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task". Existing continual learning methods, such as Averaged Gradient Episodic Memory (A-GEM) and Orthogonal Gradient Descent (OGD), address catastrophic forgetting by minimizing the loss for the current task without increasing the loss for previous tasks. However, these methods assume the learner knows when the task changes, which is unrealistic in practice. In this paper, we alleviate the need to provide the algorithm with information about task changes by using an online clustering-based approach on a dynamically updated finite pool of samples or gradients. We thereby successfully counteract catastrophic forgetting in one of the hardest settings, namely: domain-incremental learning, a setting for which the problem was previously unsolved. We showcase the benefits of our approach by applying these ideas to projection-based methods, such as A-GEM and OGD, which lead to task-agnostic versions of them. Experiments on real datasets demonstrate the effectiveness of the proposed strategy and its promising performance compared to state-of-the-art methods.
On Offline Evaluation of 3D Object Detection for Autonomous Driving

Prior work in 3D object detection evaluates models using offline metrics like average precision since closed-loop online evaluation on the downstream driving task is costly. However, it is unclear how indicative offline results are of driving performance. In this work, we perform the first empirical evaluation measuring how predictive different detection metrics are of driving performance when detectors are integrated into a full self-driving stack. We conduct extensive experiments on urban driving in the CARLA simulator using 16 object detection models. We find that the nuScenes Detection Score has a higher correlation to driving performance than the widely used average precision metric. In addition, our results call for caution on the exclusive reliance on the emerging class of ‘planner-centric’ metrics.
Selective Freezing for Efficient Continual Learning

This paper aims to tackle the challenges of continual learning, where sequential learning from a stream of tasks can lead to catastrophic forgetting. Simultaneously, it addresses the need to reduce the computational demands of large-scale deep learning models to mitigate their environmental impact. To achieve this twofold objective, we propose a method that combines selective layer freezing with fast adaptation in a continual learning context. We begin by conducting an extensive analysis of layer freezing in continual learning, revealing that certain configurations allow for freezing a substantial portion of the model without significant accuracy degradation. Leveraging this insight, we introduce a novel approach that optimizes plasticity on new tasks while preserving stability on previous tasks by dynamically identifying a subset of layers to freeze during training. Experimental results demonstrate the effectiveness of our approach in achieving competitive performance with manually-tuned freezing strategies. Moreover, we quantitatively estimate the reduction in computation and energy requirements achieved through our freezing strategy by considering the number of parameters and updates required for model training.
Analysis of the Potential Use of Graphene Oxide Nanoparticles in the Field of Materials: Bibliometric Analysis

This research was carried out to present bibliometric analysis which can be useful for mapping ongoing research topics and trends, especially in the topic of the use of graphene oxide in the materials field, so that it is hoped that it can help further research that will be carried out. The research method used is descriptive qualitative with bibliometric analysis using VOSviewer to visualize related research terms. Data collection was carried out using the publish and publish application using the keywords graphene oxide, nanoparticles, and materials with a time span of 2017 to 2023. The research results showed that there were fluctuations in the number of research conducted each year on the topic of graphene oxide.
Deteksi Kerusakan Ban Mobil Menggunakan Convolutional Neural Network dengan Arsitektur ResNet-34

The examination of tire damage can be categorized as part of vehicle maintenance with the aim of ensuring the tires are in good condition. Visual inspection using human observation has limitations, making it not always accurate and prone to errors in determining tire roadworthiness. This study designs a machine learning model using Convolutional Neural Network (CNN) with a ResNet-34 architecture to detect car tire damage. The parameters used in training this CNN model include the Adam optimizer, a learning rate of 0.0001, batch size 32, and 50 epochs. In this study, there are two predicted image classes: normal tires and damaged tires. The research results indicate that the CNN model with ResNet-34 architecture can predict both classes very well, as evidenced by the model evaluation results with an accuracy of 0.916, precision of 0.907, recall of 0.927, and an F1 score of 0.917. These results suggest that the CNN model with ResNet-34 architecture can be used as an effective tool for inspecting tire damage.
Implementation of a 3‐Dimensional Embodied Conversational Agent to Support Neurologists in Conducting Cognitive Assessment in Alzheimer Patients

Abstract Background Alzheimer’s disease is a universal medical and public health challenge. A timely identification of this ailment can enhance both the standard of care and the patient’s quality of life. Alzheimer’s diagnosis includes a critical component called cognitive assessment. These examinations have been carried out by neurologists, manually and digitally since the development of clinical neurological tests. But it’s obvious that integrating AI technologies into such assessments has many advantages. This research paper describes the implementation of a 3‐Dimensional Embodied Conversational Agent to support Neurologists in conducting cognitive assessments in Alzheimer patients. Method Twenty‐two questions are included in the assessment. The assessment checks different mental abilities of patients such as time orientation, place orientation, ability to recall things, concentration skills, language skills, ability to follow written and oral instructions, visual interpretation skills, etc. A score is given for each correct answer. A normal score must be higher than 24. A score of 24 or less is typically seen as abnormal and indicative of possible cognitive impairment further subject to detailed analysis of a neurologist as the result may occasionally be impacted by certain physical impairments, language or speech barriers, education level, or cultural differences. The 3D agent was created using software such as Blender, iclone7, Adobe Photoshop, JavaScript, and MIT App Inventor. Text‐to‐speech synthesis algorithms were used to convert the questions to speech. The answers given by the user will be recognized using Natural Language Processing (NLP) algorithms. Correspondingly, a score will be awarded. TinyDB is used for local storage and CloudDB for web storage. Result The 3D agent receives the speech input, analyzes the input, and provides a vocal response. As per the total score, it will suggest suitable recommendations vocally. Also, an assessment report with the AI impression is generated. Conclusion The implementation of a 3‐D Embodied Conversational Agent to support Neurologists in conducting cognitive assessment is portrayed in this research. Instead of trying to replace clinicians, Artificial Intelligence aims to help them and make their tasks simpler.
EFEKTIVITAS PENGGUNAAN MEDIA KARTU HILANG (KAHI) TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS VI SD

The low mathematics learning outcomes of class VI students at SD 3 Purwosari is due to the fact that learning is still teacher-centered and has not utilized learning media so that effective and appropriate solutions and innovations are needed so that mathematics learning achieves the expected learning objectives. One of the innovations that can be applied to learning mathematics is the use of KAHI media. The aims of this study were 1) to examine the differences before and after applying KAHI media to students' mathematics learning outcomes, 2) to find out the increase in students' mathematics learning outcomes before and after applying KAHI Media. This research is a quantitative research type of pre-experimental design using one group pretest-posttest. The hypothesis test used is the Paired Sample t Test and the N-Gain test. The results of the Paired Sample t Test obtained a significance value of 0.000&lt;0.05 indicating that there was a significant difference between learning before and after applying the KAHI media. The results of the N-Gain test obtained 45% results in the medium category, so it can be concluded that the use of KAHI media is effective for the mathematics learning outcomes of grade VI elementary school students.
PENERAPAN METODE PICTURE AND PICTURE DALAM MENINGKATKAN HASIL BELAJAR PESERTA DIDIK KELAS II PADA MATA PELAJARAN BAHASA INDONESIA

The research was carried out with the aim of improving student learning outcomes in Indonesian language subjects. This research includes Classroom Action Research (PTK) which was carried out in two cycles. Each cycle consists of four stages, namely design, implementation, observation and reflection. The subjects in this research were 22 class IIA students at SDN 238 Palembang. Research data was obtained using teacher and student observation sheets and student test results, then this data was analyzed using a percentage formula. The research results obtained were (1) teacher activity in cycle I using the Picture and Picture method from 80.35% to 92.8% in cycle II, (2) student activity in cycle I using the Picture and Picture 78 method, 57% to 91% in cycle II and (3) the results of learning Indonesian in cycle I using the Picture and Picture method were 63% to 95.45% in cycle II. Based on the results of this research, it can be concluded that by implementing the Picture and Picture model, students' learning outcomes can be more active and teachers' abilities can improve and become better.
APPLICATION OF GRAPH THEORY ALGORITHMS IN MODELING 2D SCANNER FOR OBSTACLE AVOIDANCE

The research addresses the development and application of the RPLIDAR A1 laser distance sensor model for the task of obstacle avoidance within the functioning of a virtual robot, with a focus on the use of graph theory algorithms. The influence of Dijkstra's and A* algorithms on the efficiency of the obstacle avoidance process using a 2D scanner is thoroughly analyzed. The significance of these algorithms in the context of graph theory is emphasized due to their ability to optimize navigation trajectories in environments characterized by the presence of multiple obstacles. Particular attention is paid to the importance of these algorithms in the field of autonomous navigation, especially in their application in sectors such as unmanned aerial vehicles, agriculture, geodetic research, and search and rescue operations. A description is provided of the developed software tool with an integrated graphical component and user interface, which allows for control over the scanning and navigation process. A detailed description of the working principles and measurement models used in 2D sensors contributes to a deeper understanding of the functional capabilities and potential of the RPLIDAR A1. The research confirms that the inclusion of Dijkstra's and A* algorithms in the obstacle avoidance system significantly improves the efficiency and safety of mobile robots' operations. Testing of the developed model revealed a significant advantage of integrating these algorithms compared to traditional approaches, opening new perspectives for the development of advanced autonomous navigation systems. The importance of applying graph theory algorithms in the development of robotic systems is highlighted through their impact on improving the quality of detection and obstacle avoidance, thereby enhancing the efficiency of autonomous navigation systems in the fields of robotics and automated transport.
Expert System for Pest Diagnosis on Local Black Rice Plant in East Kalimantan Using the Naive Bayes Method

Rice plant is a food crop that produces rice as the staple food for the majority of Indonesian people. Local rice which significantly contributes to fulfill the national rice consumption is black rice produced in East Kalimantan. However, local black rice often experiences crop failure due to pest attacks and environmental factors. The amount of local black rice production also continues to decrease due to limited human resources who have the skills and knowledge to diagnose pests in black rice plants. Therefore, one effort that can be made to overcome this problem is to create an expert system that can diagnose pests and diseases in black rice plants. The expert system in this research uses the Naive Bayes method, which identifies 11 types of pests that attack black rice plants and 34 symptoms caused by these pest attacks. Naive Bayes can provide information about the percentage of pests that rice plants might experience. Based on the results of the test cases, an accuracy value of80% was obtained, so the expert system built in this research can diagnose pests on black rice plants quite well.
PENGARUH MODEL PEMBELAJARAN MAKE A MATCH TERHADAP HASIL BELAJAR SISWA PADA MATA PELAJARAN BAHASA INGGRIS DI KELAS IV SD NEGERI 066050 MEDAN

This study seeks to determine the impact of the make a match learning model on student learning outcomes in English class IV SD Negeri 066050 Medan Learning Year 2022/2023. The research method used is experimental quantitative research methodology. To obtain information in the form of data, a test instrument of 30 questions is needed. The number of research samples was 30 students based on purposive sample technique. To see the initial ability of students, the study conducted a Pre Test with the results representing a set of scores of 59.87 which fell into the category of less. The results of the Post Test are better than the results of the Pre Test given previously with the average value of the students reaching 80.17 in the excellent category. The results of the calculation of the correlation coefficient show an increase in the level of student learning success where 14rhitung"&gt; ≥ 14 rtable"&gt; l with the results of 0.770 ≥ 0.361. Furthermore, hypothesis testing is to compare the value of 14thitung "&gt; ≥ 14ttabel"&gt; . Obtained the value of 14thitung"&gt; = 6.387 while 14ttable"&gt; = 1.697. Because 14tcount"&gt; ≥ 14ttable"&gt; (6.387 ≥ 1.697) then H_a is accepted and H_o is rejected. Through the t-test, it can be concluded that the make a match learning model has a positive impact on student learning outcomes.
Application of Floating Maxim on Implicature in Japanese Language

Cooperative principle is a principle in communication to make speakers and listeners to be coordinative for achieve communication’s aims. In cooperative principle, there are maxims accordance cooperative principle. Speakers must give truth speech, be brief, do not more informative than its required, and make it clear. Sometimes people didn’t contribute in communication, and make utterance float the maxims. This floating maxim creates the implicature. Implicature is one of pragmatic’s scope discussed about conveyed meaning in utterances. Meanwhile pragmatic is a scope of speakers meaning which implicature has truth conditional meaning conveyed by speakers. But, only conversational implicature create by floating maxim. Implicature can be find in every language such as Japanese. It supported by ideology called aimai to make ambiguous speech. As the background, this research is to find floating maxim in implicature in Japanese. Floating maxims by Grice used for theory in this research. The method that used was descriptive analysis. Data source in anime and Japanese drama to find implicature in Japanese. The result of this research is maxim of relation were founded dominant. It according to the aims of communication, and other side speaker has different aims with their partner. Floating of relation maxim used for for showing disinterest, admiration, and sarcasm.
Professional development of teachers on the basis of a digital educational platform: Challenges to the expert community

The relevance of the research lies in the fact that teachers often fail to cope with the tasks of digital communication and their own learning via digital platform, and therefore, digital means become a kind of a communication barrier for them, instead of being a unifying bond. The research objective is to summarize the experience of psychological and pedagogical interaction with the participants of the professional development program based on a digital educational platform in order to identify psychological and pedagogical deficiencies of working teachers, who are enrolled in the professional development program, and the challenges faced by the experts of the training program. The study hypothesizes that the teacher-training professional development program, organized on the basis of a digital educational platform, will allow us to identify specific challenges to the expert community caused by the practice of exclusively distant interaction, on the one hand, and on the other hand, it will help to identify professional deficiencies of working teachers and to overcome them. Participants: 53 teachers working in different regions of the Russian Federation aged from 21 to 70 years old with work experience from 1 to 40 years. Methods (tools): the questionnaire for fixing the psychological and pedagogical problems of the interviewees and the significance of the professional activity (by I. V. Egorov, D. V. Naumova, T. V. Sklyarova, L. B. Schneider), the expert method (included observation and expert assessments) to determine the manifestation of professional competencies of teachers. Results. The research results helped to find out that under the conditions of online learning, teachers demonstrate commitment to the position of a passive participant/listener, rather than an active co-author of the process. It is shown that adult education (of teachers, in particular) becomes a challenge to the expert community: the established mental patterns, individual and habitual models of obtaining knowledge determine the internal resistance and distrust to digital learning tools. It is revealed that the key shortage of the students is insufficient digital communication. Conclusions: to overcome teacher deficiencies the authors propose the construction of an individual educational route for professional development and the use of gamification techniques in digital learning. Practical significance: the results obtained will allow to adjust the professional development programs that are conducted via the digital platform.