Reviewerly/exHarmony huggingface dataset:
Analysis E-Modul Using Kondular Application on Poetry Writing Material at SDN Tasikmadu 1 Malang

This research aims to produce e-module using the kondular application, describe the feasibility of e-module using the kondular application, and describe the effectiveness of e-module using the kondular application in learning to write poetry in class IV SDN Tasikmadu 1. The product test design used is a product feasibility test and a test product effectiveness. The subjects of this research are divided into 2 categories, namely the feasibility test subjects consisting of validators, teachers, and class IV students at SDN Tasikmadu 1 Malang for the 2023/2024 academic year, while the effectiveness test subjects are teachers and class IV students at SDN Tasikmadu 1 Malang for the 2023/2024 academic year. This research uses the development of the ASSURE model. Data collection technique by distributing validation questionnaires to validadors. The instruments used in this research were questionnaire sheets, interview sheets and observation sheets. The data analysis technique is to calculate the average validator score during the feasibility test and effectiveness test. The result of this research is to produce teaching materials in the form of poetry e-modules based on the Kondular application, where the average feasibility score from the material validator is 87%, the language validator is 90%, and the media validator is 88%, where the teaching material shows a very feasible level. and the effectiveness of teaching materials in learning to write poetry has been proven to be effective for use by fourth grade elementary school students. The response from students and teachers to this e-module using the kondular application was very positive, because the teaching material was very interesting and interactive.
Pengembangan Media Busy Book Numbering dari Bahan Bekas untuk Pengenalan Matematika pada Anak Usia Dini

Needing analysis of the lack in media, especially the introduction of children's mathematics, due to media in the form of posters, magazine, making children bored, that is not up to date in the learning process, the existing media has not been able to develop the competence of mathematical concepts appropriately, the media is still the same there is no upgrade, the media must be updated from the concept of number recognition for AUD and it is essential part to develop appropriate and holistic. It includes activities of Mathcing, Classification, Comparing, Ordering or Seriation. The objectives of this research are: (1) analyze the feasibility of developing busy book Numbering from Used Materials for the introduction of mathematics in children aged 5-6 years at Hafizul Ilmi Kindergarten. (2) describe the process of developing Busy Book Numbering from used materials for the introduction of mathematics in children aged 5-6 years at Hafizul Ilmi Kindergarten. This research uses R&amp;D research by ADDIE model. The research instruments used in this study include: Busy Book Numbering Validation Feasibility Sheet and Observation Sheet of Math Recognition Ability for AUD. The results of the research are: Validation results obtained from media experts, 85% with very feasible criteria while for material experts 83,12% with feasible criteria. The implementation stage or trial conducted at Hafizul Ilmi Kindergarten obtained a percentage of 96% in very good developing criteria (BSB).
Empirical Assessment of User Satisfaction with American and Chinese AI Chatbots

As artificially intelligent chatbots grow in popularity, exploring user satisfaction has become an important topic. This study deviates from the conventional questionnaire approach by adopting a balanced paired design. We employed 40 users to participate in usability testing to assess their satisfaction with the responses of two prominent chatbots (ChatGPT 4.0 from the US and inChat from China) over five domains (daily life, the workplace, advertising copy, current affairs commentary, and translation). We conducted a comparative analysis based on three demographic variables: gender, experience with chatbots, and generation (i.e., age). Empirical results revealed that the participants were more satisfied with inChat's responses overall and for daily life scenarios in particular than with those of ChatGPT 4.0. Further, female participants expressed higher satisfaction with inChat's commentary on current affairs than did male participants. Prior use of chatbots did not significantly influence satisfaction levels; however, Baby Boomers (i.e., those born between 1946 and 1964) showed a notably higher appreciation for inChat's translation capabilities and higher satisfaction overall compared to Generation Y (i.e., those born between 1981 and 1996).
A Study of Fitness Gains in Evolving Finite State Machines

Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research. They are easy to understand in concept and can be visualised clearly in simple cases. They have a ready fitness criterion through their relationship with Regular Languages. They have also been shown to be tractably evolvable, even up to exhibiting evidence of open-ended evolution in specific scenarios. In addition to theoretical attraction, they also have industrial applications, as a paradigm of both automated and user-initiated control. Improving the understanding of the factors affecting FSM evolution has relevance to both computer science and practical optimisation of control. We investigate an evolutionary scenario of FSMs adapting to recognise one of a family of Regular Languages by categorising positive and negative samples, while also being under a counteracting selection pressure that favours fewer states. The results appear to indicate that longer strings provided as samples reduce the speed of fitness gain, when fitness is measured against a fixed number of sample strings. We draw the inference that additional information from longer strings is not sufficient to compensate for sparser coverage of the combinatorial space of positive and negative sample strings.
PENGGUNAAN BAHASA INGGRIS SEBAGAI STRATEGI KOMUNIKASI GLOBAL DALAM INDUSTRI PARIWISATA

The more advanced the development of technology, the more likely it is that something can be searched for and known. It's the same as finding out about vacation spots and relaxing while enjoying your time. So with this convenience, one of the things that is affected is the tourism industry sector. The tourism industry in Indonesia is currently developing rapidly. Indonesian tourism is currently considered Indonesia's core business by the World Bank. With so many tourists present as tourism objects, this development must be in line with the competence and skills of the people involved in this industry, especially English language skills, for the good and sustainability as well as the special attraction of the tourist spot. The aim of this research is to find out "The Use of English as a Global Communication Strategy in the Tourism Industry". This research uses qualitative methods with observations and interviews and from several scientific journals relevant to this topic. The results of this research are that English has a very important role in the communication process that occurs in the tourism industry, both communication between tourism actors and tourists and communication in business and economic matters.
DG2: Data Augmentation Through Document Grounded Dialogue Generation

Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and the need for extensive annotation. Especially in document-grounded dialog systems, human experts need to carefully read the unstructured documents to answer the users’ questions. As a result, existing document-grounded dialog datasets are relatively small-scale and obstruct the effective training of dialogue systems. In this paper, we propose an automatic data augmentation technique grounded on documents through a generative dialogue model. The dialogue model consists of a user bot and agent bot that can synthesize diverse dialogues given an input document, which is then used to train a downstream model. When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods. We also achieve great performance in the low-resource setting.
Systematic Analysis of the Impact of Label Noise Correction on ML Fairness

Arbitrary, inconsistent, or faulty decision-making raises serious concerns, and preventing unfair models is an increasingly important challenge in Machine Learning. Data often reflect past discriminatory behavior, and models trained on such data may reflect bias on sensitive attributes, such as gender, race, or age. One approach to developing fair models is to preprocess the training data to remove the underlying biases while preserving the relevant information, for example, by correcting biased labels. While multiple label noise correction methods are available, the information about their behavior in identifying discrimination is very limited. In this work, we develop an empirical methodology to systematically evaluate the effectiveness of label noise correction techniques in ensuring the fairness of models trained on biased datasets. Our methodology involves manipulating the amount of label noise and can be used with fairness benchmarks but also with standard ML datasets. We apply the methodology to analyze six label noise correction methods according to several fairness metrics on standard OpenML datasets. Our results suggest that the Hybrid Label Noise Correction [20] method achieves the best trade-off between predictive performance and fairness. Clustering-Based Correction [14] can reduce discrimination the most, however, at the cost of lower predictive performance.
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues

Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multi-domain dialogues. However, learning with reinforcement in such context becomes difficult because the state-action dimension is larger while the reward signal remains scarce. Our experimental results suggest that structured policies based on graph neural networks combined with different degrees of imitation learning can effectively handle multi-domain dialogues. The reported experiments underline the benefit of structured policies over standard policies.
Metric-Based In-context Learning: A Case Study in Text Simplification

In-context learning (ICL) for large language models has proven to be a powerful approach for many natural language processing tasks. However, determining the best method to select examples for ICL is nontrivial as the results can vary greatly depending on the quality, quantity, and order of examples used. In this paper, we conduct a case study on text simplification (TS) to investigate how to select the best and most robust examples for ICL. We propose Metric-Based in-context Learning (MBL) method that utilizes commonly used TS metrics such as SARI, compression ratio, and BERT-Precision for selection. Through an extensive set of experiments with various-sized GPT models on standard TS benchmarks such as TurkCorpus and ASSET, we show that examples selected by the top SARI scores perform the best on larger models such as GPT-175B, while the compression ratio generally performs better on smaller models such as GPT-13B and GPT-6.7B. Furthermore, we demonstrate that MBL is generally robust to example orderings and out-of-domain test sets, and outperforms strong baselines and state-of-the-art finetuned language models. Finally, we show that the behavior of large GPT models can be implicitly controlled by the chosen metric. Our research provides a new framework for selecting examples in ICL, and demonstrates its effectiveness in text simplification tasks, breaking new ground for more accurate and efficient NLG systems.
CONVERSER: Few-shot Conversational Dense Retrieval with Synthetic Data Generation

Conversational search provides a natural interface for information retrieval (IR). Recent approaches have demonstrated promising results in applying dense retrieval to conversational IR. However, training dense retrievers requires large amounts of in-domain paired data. This hinders the development of conversational dense retrievers, as abundant in-domain conversations are expensive to collect. In this paper, we propose Converser, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues. Specifically, we utilize the in-context learning capability of large language models to generate conversational queries given a passage in the retrieval corpus. Experimental results on conversational retrieval benchmarks OR-QuAC and TREC CAsT 19 show that the proposed Converser achieves comparable performance to fully-supervised models, demonstrating the effectiveness of our proposed framework in few-shot conversational dense retrieval. All source code and generated datasets are available: https://github.com/MiuLab/CONVERSER
Prompting, Retrieval, Training: An exploration of different approaches for task-oriented dialogue generation

Task-oriented dialogue systems need to generate appropriate responses to help fulfill users’ requests. This paper explores different strategies, namely prompting, retrieval, and fine-tuning, for task-oriented dialogue generation. Through a systematic evaluation, we aim to provide valuable insights and guidelines for researchers and practitioners working on developing efficient and effective dialogue systems for real-world applications. Evaluation is performed on the MultiWOZ and Taskmaster-2 datasets, and we test various versions of FLAN-T5, GPT-3.5, and GPT-4 models. Costs associated with running these models are analyzed, and dialogue evaluation is briefly discussed. Our findings suggest that when testing data differs from the training data, fine-tuning may decrease performance, favoring a combination of a more general language model and a prompting mechanism based on retrieved examples.
Few-Shot NER in Marine Ecology Using Deep Learning

In the field of marine ecological named entity recognition (NER), challenges arise due to limited domain-specific text, weak semantic representations of input vectors and the neglect of local features. To address these challenges of NER in a low-resource environment, a deep learning-based few-shot NER model was proposed. Firstly, Sequence Generative Adversarial Nets (SeqGAN) was utilized to train on the original text and generated new text, thereby expanding the original corpus. Subsequently, BERT-IDCNN-BiLSTM-CRF was introduced for extracting marine ecological entities. BERT (Bidirectional Encoder Representation from Transformers) was pre-trained on the expanded corpus. The embeddings produced by BERT were then fed into Iterative Dilation Convolutional Networks (IDCNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM) to facilitate feature extraction. Finally, Conditional Random Fields (CRF) was employed to enforce label sequence constraints and yielded the final results. For the proposed few-shot NER method based on deep learning, comparative experiments were conducted horizontally and vertically against BiLSTM-CRF, IDCNN-CRF, BERT-IDCNN-CRF and BERT-BiLSTM-CRF models on both the original and expanded corpora. The results show that BERT-IDCNN-BiLSTM-CRF outperforms BERT-BiLSTM-CRF by 2.48 percentage points in F1-score on the original corpus. On the expanded corpus, BERT-IDCNN-BiLSTM-CRF achieves a F1-score 2.65 percentage points higher than that on the original corpus. This approach effectively enhances entity extraction in the domain of marine ecology, laying a foundation for downstream tasks such as constructing marine ecological knowledge graphs and ecological governance.
PLKA-MVSNet: Parallel Multi-view Stereo with Large Kernel Convolution Attention

In this paper, we propose PLKA-MVSNet to address the remaining challenges in the in-depth estimation of learning-based multi-view stereo (MVS) methods, particularly the inaccurate depth estimation in challenging areas such as low-texture regions, weak lighting conditions, and non-Lambertian surfaces. We ascribe this problem to the insufficient performance of the feature extractor and the information loss caused by the MVS pipeline transmission, and give our optimization scheme. Specifically, we introduce parallel large kernel attention (PLKA) by using multiple small convolutions instead of a single large convolution, to enhance the perception of texture and structural information, which enables us to capture a larger receptive field and long-range information. In order to adapt to the coarse-to-fine MVS pipeline, we employ PLKA to construct a multi-stage feature extractor. Furthermore, we propose the parallel cost volume aggregation (PCVA) to enhance the robustness of the aggregated cost volume. It introduces two decision-making attentions in the 2D dimension to make up for information loss and pixel omission in the 3D convolution compression. Particularly, our method shows the best overall performance beyond the transformer-based method on the DTU dataset and achieves the best results on the challenging Tanks and Temples advanced dataset.
Streaming Graph-Based Supervoxel Computation Based on Dynamic Iterative Spanning Forest

Streaming video segmentation decreases processing time by creating supervoxels taking into account small parts of the video instead of using all video content. Thanks to the good performance of the Iterative Spanning Forest to compute Supervoxels (ISF2SVX) based on Dynamic Iterative Spanning Forest (DISF) for video segmentation framework we propose a new graph-based streaming video segmentation method for supervoxel generation by using dynamic iterative spanning forest framework, so-called StreamISF, based on a pipeline composed of six stages: (1) formation of the graph for each block of the video; (2) seed oversampling; (3) IFT-based supervoxel design; (4) reduction in the number of supervoxels; (5) spread of trees; and (6) creation of the segmented video. The difference in our proposed method is that it is unnecessary to have all the video in memory and the only previous information necessary to segment a block is the intersection frame between the blocks. Moreover, experimental results show that StreamISF creates supervoxels that maintain temporal coherence, producing very competitive measures compared to the state-of-the-art. Our code is publically available at https://github.com/IMScience-PPGINF-PucMinas/StreamISF .
Optimasi Kenyamanan Suhu Ruangan Melalui Perancangan Otomatisasi Kipas Angin di Masjid Nurhidayatullah Maguwoharjo

This research explores a solution to optimise room temperature comfort in Masjid Nurhidayatullah Maguwoharjo. Mosques are susceptible to temperature and humidity fluctuations, which can affect the worship experience of congregants. To address this challenge, the study developed an automated fan system that dynamically responds to environmental changes, such as temperature and congregation density, using DHT22 temperature and PIR sensors to monitor these parameters. The results indicate that the system effectively maintains temperature and humidity within predefined comfort ranges. Additionally, the study incorporated the TM1637 module and LCD I2C 20x4 display to provide users with temperature, humidity, fan speed, and room capacity information. During testing, temperature and humidity fluctuations throughout the day were recorded, along with changes in congregation density during prayer times. The fan speed adjustment results were reflected in the system's responsiveness to environmental conditions. This research successfully provides an effective solution to enhance comfort in mosques. Therefore, it makes a valuable contribution to the development of automation systems aimed at improving the congregational worship experience and energy efficiency in mosques.
Analisis Layanan Tata Kelola Aplikasi Sistem Informasi Akademik dengan Menggunakan Cobit 5 pada STIK Bina Husada

Analyze the level of capability in the process of implementing information technology governance that runs at STIK Bina Husada according to national standards. The Academic Information System aims to improve the quality of academic services at the Bina Husada College of Health Sciences, including facilitating the management of student academic data such as student data, student grades/grade transcripts, lecturer data, Study Plan Cards (KRS), and Study Results Cards (KHS). . This is the reason that STIK Bina Husada must regularly carry out the process of monitoring, managing and evaluating the level of readiness of IT services. Based on this, Application Evaluation and Auditcarried out using the Cobit 5 framework, while on Quality Testing and Measurementwebsite using the Apache Jmeter tool. The Cobit 5 processes used are: Risk Optimization (EDM03), Security Management (APO13), Risk Management (APO12), Change Management (BAI06), Operations Management (DSS01), Service Requests (DSS02), Problem Handling (DSS03), Service Security (DSS05), Monitoring and Evaluation of Work (MEA01), Internal Control of Monitoring and Evaluation (MEA02). Gap analysis and Maturity Level are intended to obtain Audit results with good maturity. The results obtained from the average value based on the Gap Analysis of 10 Cobit 5 Processes are 1.2 and the Apache Jmeter test showsthroughput of 101,746/minute and the deviation is 3,671.
Indoor Multi-Lingual Scene Text Database with Different Views

This paper introduces a database of multi-script (Arabic and English) for indoor scene text detection, taken from different angle-of-view. This database can be used in a variety of real-world applications, such as image search, robot navigation, and assisting the visually impaired. The database contains 944 images taken with smartphones in an indoor environment at Qatar University. These images were taken from at least three angles, making the database even more challenging. To evaluate the database, an OCR method based on multiple language detection is considered. The results show that multi-language detection should be given more attention in practice. The database is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https:/www.dropbox.com/s/7s7f936y4etzsu7/QU_door_dataset%20%282%29.zip?dl=0.
Case Study of Robot Arm Billiards

This paper mainly studies the function of the robot arm to play pool. To perform this function, a special gripper must be designed, so we use 3D printing to design a gripper mechanism for playing pool, and design the drive and control circuits for the gripper. In addition, we also design a camera to be installed on the gripper to provide image recognition. This paper only discusses the case study of "one cushion" bank shot. Bank shot is a playing pool technique in which the player shoots the object ball into the cushion first, and let the object ball bounce into the pocket. This practical study is a mechatronic system of a robotic arm with special-purpose functions.