Reviewerly/exHarmony huggingface dataset:
Development of Science Learning Media Klanimal Android-Based for Elementary School Students

The difficulties students face in understanding the concepts of Science are due to their abstract nature, making it difficult to retain long-term memory. The research objective is to develop an interactive Android-based media to visualize Science learning, specifically the concept of animal classification based on their types of food, to facilitate its retention in students' long-term memory. The "Klanimal" media is an Android-based platform that includes content menus, learning objectives menus, animal guessing menus, and quiz menus. The research methodology employed in this study is the Research and Development (R&amp;D) approach. The development model used is the ADDIE development model, which consists of the Analysis, Design, Development, Implementation, and Evaluation stages. Media experts and subject matter experts validated the Klanimal media. Subsequently, it was tested on 6th-grade students. The test results showed a percentage of 89.8, indicating a highly suitable category. Pretest and post-test scores improved, and the N Gain analysis indicated a value of 0.70, indicating a significant increase in understanding. The research findings suggest that the effective use of Android-based Science learning media enhances the understanding of 5th-grade students at SDN Candibinangun IV Sukorejo, specifically regarding the topic of animal classification based on their types of food.
Generative AI Chatbots - ChatGPT versus YouChat versus Chatsonic: Use Cases of Selected Areas of Applied English Language Studies

This paper reports on the comparison of the accuracy and quality of the responses produced by the three artificial intelligence (AI) chatbots, ChatGPT, YouChat, and Chatsonic, based on the prompts (use cases) related to selected areas of applied English language studies (AELS). An exploratory research design was employed and we utilised purposive sampling. The three aforementioned AI chatbots were used to collect data sets. Of the three chatbots, YouChat was technically unstable and unreliable, and had some inconsistency in generating responses. The other two chatbots, ChatGPT and Chatsonic, consistently exhibited a tendency to plagiarise responses from internet information without acknowledging the sources. In certain cases, the three chatbots all generated almost similar responses for different and unrelated prompts. This made their responses look like run-of-the-mill responses that lacked credibility, accuracy, and quality. One chatbot (ChatGPT) could not recognise a scholar mentioned in one of the prompts, while the other one (Chatsonic) misrecognised this scholar, and ended up rambling parts of its response. Additionally, the three chatbots all mechanically and superficially generated phrases and ideas in their responses without detecting the related critical nuances in the original sources in which they were used. This made the knowledge communicated by those responses appearing too fluffy. In this paper the educational and knowledge implications of the generated responses for AELS were educed. Based on the shortcomings the three AI chatbots displayed, I concluded that these three chatbots are not yet credible and reliable generators of knowledge for the aspects of AELS discussed.
Improving Automated Evaluation of Student Text Responses Using GPT-3.5 for Text Data Augmentation

In education, intelligent learning environments allow students to choose how to tackle open-ended tasks while monitoring performance and behavior, allowing for the creation of adaptive support to help students overcome challenges. Timely feedback is critical to aid students’ progression toward learning and improved problem-solving. Feedback on text-based student responses can be delayed when teachers are overloaded with work. Automated evaluation can provide quick student feedback while easing the manual evaluation burden for teachers in areas with a high teacher-to-student ratio. Current methods of evaluating student essay responses to questions have included transformer-based natural language processing models with varying degrees of success. One main challenge in training these models is the scarcity of data for student-generated data. Larger volumes of training data are needed to create models that perform at a sufficient level of accuracy. Some studies have vast data, but large quantities are difficult to obtain when educational studies involve student-generated text. To overcome this data scarcity issue, text augmentation techniques have been employed to balance and expand the data set so that models can be trained with higher accuracy, leading to more reliable evaluation and categorization of student answers to aid teachers in the student’s learning progression. This paper examines the text-generating AI model, GPT-3.5, to determine if prompt-based text-generation methods are viable for generating additional text to supplement small sets of student responses for machine learning model training. We augmented student responses across two domains using GPT-3.5 completions and used that data to train a multilingual BERT model. Our results show that text generation can improve model performance on small data sets over simple self-augmentation.
Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption

Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models.In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE).We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners.Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison.It supports the computation of encrypted data with different public keys.At the same time, we further design a new scheme that outsources evaluation works to a third-party evaluator who should not have access to the models and data.In the evaluation process, each model owner encrypts the model and uploads the encrypted models to the evaluator.After receiving encrypted the kNN classifier and the user's inputs, the evaluator calculated the aggregated results.The evaluator will perform a secure computing protocol to aggregate the number of each class label.Then, it sends the class labels with their associated counts to the user.Each model owner and user encrypt the result together.No information will be disclosed to the evaluator.The experimental results show that our new system can securely allow multiple model owners to delegate the evaluation of kNN classifier.
PENGARUH MEDIA AUDIOVISUAL DENGAN MENGGUNAKAN WONDERSHARE FILMORA DALAM PEMBELAJARAN KETERAMPILAN MENYIMAK CERITA SISWA KELAS VIII SMP NEGERI 2 BAUBAU

The purpose of this study is to see if Wondershare Filmora has a substantial impact on developing story listening abilities in Class VIII students at SMP Negeri 2 Baubau. Students in classes VIIIa and VIIIb at SMP Negeri 2 Baubau, a total of 40 students, were the subjects of this study. The results of this study indicate that (1) there is a significant effect of using audiovisual media on the effectiveness of learning students' story listening skills. This is indicated by the value of tcount which is greater than ttable (12,353&gt;2,042), with a significance value of 0.000 which is smaller than a significance of 0.05 (0.000&lt;0.05); and (2) there is a difference in the effect of audiovisual media in learning students' story listening skills compared to using conventional methods, which is shown from the results of the average score on the posttest being greater than the pretest (17.65&gt;14.65). The magnitude of the increase in the level of story listening skills of students who are taught using conventional methods and the level of students' story listening skills taught using audiovisual media is 3.00.
The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments

This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.
The Role of Perceived Usefulness and Confirmation in Influencing Student’s Satisfaction on Online Distance Learning

Today's learning opportunities are immense in scale and bolstered by the support of technological advancements.Many universities in Malaysia have contributed to the trend of advancements in education and technology.It was recommended that online distance learning (ODL) and online teaching processes should be used at all levels of education during the COVID-19 pandemic.However, the use of these technological advancements has encountered challenges, which were recognized during the initial application of ODL.Both students and academics faced many obstacles as first-time users of online education.This study explores students' expectations of ODL based on Expectation-confirmation theory (ECT).A survey was conducted with 579 university students to test the hypotheses of this study.The results of this questionnaire were tested using structural equation modeling (SEM) AMOS mediation analysis.The results showed partial mediating effects between perceived usefulness, confirmation, and satisfaction with online distance learning.Furthermore, it was found that perceived usefulness has a significant influence on student satisfaction with and confirmation of ODL.To ensure that students of higher learning institutions are satisfied with ODL, it is vital for universities and colleges to make these students believe that the application of ODL is useful and can create significant value for them.
Efficient thermal face recognition method using optimized curvelet features for biometric authentication

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user’s face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.
FiTs: Fine-Grained Two-Stage Training for Knowledge-Aware Question Answering

Knowledge-aware question answering (KAQA) requires the model to answer questions over a knowledge base, which is essential for both open-domain QA and domain-specific QA, especially when language models alone cannot provide all the knowledge needed. Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities. To address the above two problems, we propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance: The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them, named knowledge adaptive post-training. The second stage, called knowledge-aware fine-tuning, aims to improve the model's joint reasoning ability based on the aligned representations. In detail, we fine-tune the post-trained model via two auxiliary self-supervised tasks in addition to the QA supervision. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA) and medical question answering (i.e., MedQA-USMILE) domains.
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian Languages

A cornerstone in AI research has been the creation and adoption of standardized training and test datasets to earmark the progress of state-of-the-art models. A particularly successful example is the GLUE dataset for training and evaluating Natural Language Understanding (NLU) models for English. The large body of research around self-supervised BERT-based language models revolved around performance improvements on NLU tasks in GLUE. To evaluate language models in other languages, several language-specific GLUE datasets were created. The area of speech language understanding (SLU) has followed a similar trajectory. The success of large self-supervised models such as wav2vec2 enable creation of speech models with relatively easy to access unlabelled data. These models can then be evaluated on SLU tasks, such as the SUPERB benchmark. In this work, we extend this to Indic languages by releasing the IndicSUPERB benchmark. Specifically, we make the following three contributions. (i) We collect Kathbath containing 1,684 hours of labelled speech data across 12 Indian languages from 1,218 contributors located in 203 districts in India. (ii) Using Kathbath, we create benchmarks across 6 speech tasks: Automatic Speech Recognition, Speaker Verification, Speaker Identification (mono/multi), Language Identification, Query By Example, and Keyword Spotting for 12 languages. (iii) On the released benchmarks, we train and evaluate different self-supervised models alongside the a commonly used baseline FBANK. We show that language-specific fine-tuned models are more accurate than baseline on most of the tasks, including a large gap of 76% for Language Identification task. However, for speaker identification, self-supervised models trained on large datasets demonstrate an advantage. We hope IndicSUPERB contributes to the progress of developing speech language understanding models for Indian languages.
Reward-Based Negotiating Agent Strategies

This study proposed a novel reward-based negotiating agent strategy using an issue-based represented deep policy network. We compared the negotiation strategies with reinforcement learning (RL) by the tournaments toward heuristics-based champion agents in multi-issue negotiation. A bilateral multi-issue negotiation in which the two agents exchange offers in turn was considered. Existing RL architectures for a negotiation strategy incorporate rich utility function that provides concrete information even though the rewards of RL are considered as generalized signals in practice. Additionally, in existing reinforcement learning architectures for negotiation strategies, both the issue-based representations of the negotiation problems and the policy network to improve the scalability of negotiation domains are yet to be considered. This study proposed a novel reward-based negotiation strategy through deep RL by considering an issue-based represented deep policy network for multi-issue negotiation. Comparative studies analyzed the significant properties of negotiation strategies with RL. The results revealed that the policy-based learning agents with issue-based representations achieved comparable or higher utility than the state-of-the-art baselines with RL and heuristics, especially in the large-sized domains. Additionally, negotiation strategies with RL based on the policy network can achieve agreements by effectively using each step.
Predicate Invention for Bilevel Planning

Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to perform bilevel planning with abstractions, where a high-level search for abstract plans is used to guide planning in the original transition space. Previous work has shown that when state abstractions in the form of symbolic predicates are hand-designed, operators and samplers for bilevel planning can be learned from demonstrations. In this work, we propose an algorithm for learning predicates from demonstrations, eliminating the need for manually specified state abstractions. Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective. We use this surrogate objective in a hill-climbing search over predicate sets drawn from a grammar. Experimentally, we show across four robotic planning environments that our learned abstractions are able to quickly solve held-out tasks, outperforming six baselines.
Reinforcement Causal Structure Learning on Order Graph

In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal policy with high probability using a finite amount of samples and computation. In recent years, researchers have introduced objectives and corresponding reinforcement-learning algorithms beyond the classic cumulative rewards, such as objectives specified as linear temporal logic formulas. However, questions about the PAC-learnability of these new objectives have remained open. This work demonstrates the PAC-learnability of general reinforcement-learning objectives through sufficient conditions for PAC-learnability in two analysis settings. In particular, for the analysis that considers only sample complexity, we prove that if an objective given as an oracle is uniformly continuous, then it is PAC-learnable. Further, for the analysis that considers computational complexity, we prove that if an objective is computable, then it is PAC-learnable. In other words, if a procedure computes successive approximations of the objective's value, then the objective is PAC-learnable. We give three applications of our condition on objectives from the literature with previously unknown PAC-learnability and prove that these objectives are PAC-learnable. Overall, our result helps verify existing objectives' PAC-learnability. Also, as some studied objectives that are not uniformly continuous have been shown to be not PAC-learnable, our results could guide the design of new PAC-learnable objectives.
Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting It into MLPs: An Effective GNN-to-MLP Distillation Framework

Recent years have witnessed the great success of Graph Neural Networks (GNNs) in handling graph-related tasks. However, MLPs remain the primary workhorse for practical industrial applications due to their desirable inference efficiency and scalability. To reduce their gaps, one can directly distill knowledge from a well-designed teacher GNN to a student MLP, which is termed as GNN-to-MLP distillation. However, the process of distillation usually entails a loss of information, and ``which knowledge patterns of GNNs are more likely to be left and distilled into MLPs?" becomes an important question. In this paper, we first factorize the knowledge learned by GNNs into low- and high-frequency components in the spectral domain and then derive their correspondence in the spatial domain. Furthermore, we identified a potential information drowning problem for existing GNN-to-MLP distillation, i.e., the high-frequency knowledge of the pre-trained GNNs may be overwhelmed by the low-frequency knowledge during distillation; we have described in detail what it represents, how it arises, what impact it has, and how to deal with it. In this paper, we propose an efficient Full-Frequency GNN-to-MLP (FF-G2M) distillation framework, which extracts both low-frequency and high-frequency knowledge from GNNs and injects it into MLPs. Extensive experiments show that FF-G2M improves over the vanilla MLPs by 12.6% and outperforms its corresponding teacher GNNs by 2.6% averaged over six graph datasets and three common GNN architectures.