Reviewerly/exHarmony huggingface dataset:
The geometric neural solution combined with text diagram parsing

Geometry problem solving, being a longstanding quandary in the domain of artificial intelligence education, has garnered significant attention. The resolution of geometry problems necessitates the amalgamation of textual depictions and geometric diagrams to construct a formal language, coupled with an understanding of geometric theorems to generate coherent sequences of solutions and ultimately attain answers. However, deficiencies in diagram parsing and modal fusion have resulted in subpar problem-solving capabilities. To proficiently extract diagram features, this paper proposes an enhanced diagram parser, DenseNet-121. Simultaneously, it implements meticulous structural and semantic analysis of textual descriptions to circumvent nuanced discrepancies in text comprehension that may lead to divergent solutions. The efficacy of this approach in geometry problem-solving has been substantiated through empirical experimentation.
Affect-based Planning for a Meta-Cognitive Robot Sculptor: First Steps

Today’s so-called “generative AI” is in the minds of some capable of specifically generating genuine visual art; DALL-E is an example. While in fact intensely skeptical, we grant for the sake of argument that the likes of DALL-E can in fact generate genuine visual art. However, we observe that fine visual artistry is by no means monolithic; in particular, some forms of fine visual artistry are seemingly harder than others for artificial agents to achieve. In the traditional ontology, perhaps the very hardest type of fine visual artistry for an AI to achieve is literary sculpture, an activity carried out in the human sphere at the very highest level by Rodin. Artificial agents that operate in this sphere must for obvious reasons be cognitive robots. We explain such literary sculpture in broad terms, making clear that it’s undertaken by the sculptor in question with the aim of bringing about certain affective states in the mind of the viewer of the sculpture. In short, literary sculpting is affect-driven. We provide a case study of robot sculpting, with help from pre-existing logic-based formalisms and automated-reasoning technology, and the cognitive robot PERI.2, operating as a sculptor. To our knowledge, ours is the very first foray into literary sculpture in AI and cognitive robotics.
Drone Flight Log Anomaly Severity Classification via Sentence Embedding

Log-based anomaly detection is one of the popular research topics in the cybersecurity domain. Typically, the log event classification target is only separated into two classes: normal and anomaly. However, the abnormality might contain words or phrases that determine the importance level of the anomalies. Therefore, this paper proposes to extend anomaly detection into log anomaly severity classification, which consists of four classes, i.e., normal, low, medium, and high. As an initial study, several machine learning models are used to build the detection models. A dataset is constructed to verify and evaluate the models’ performance by manually annotating drone flight log messages collected from two public drone flight log datasets. Since machine learning models cannot understand natural language, sentence embedding is used as the feature extractor. It embeds the messages into sentence-level vector embeddings to represent the linguistic features in the log message. The micro-average F1 score is selected as the main evaluation metric, considering the proportion between classes in the dataset is imbalanced. After experimenting with the models with 5-fold cross-validation, the multilayer perceptron outperforms the other models and obtains the highest F1 score of 94.788%. The proposed approach successfully recognizes and detects anomalous events in the drone’s flight log data with a promising result.
A Game theoretic approach for validator selection in proof of stake blockchains

In our study, we propose a novel validator selection mechanism for Proof-of-Stake blockchains that utilizes game theory through a Vickrey auction mechanism to encourage honest staking. The main element of our contribution is to include an auction mechanism that prevents domination by high-stake validators and a weighted random selection process to ensure fairness, and we aim to address a gap in selection methods that advantage nodes with more resources. The rationale for our approach is based on other game-theoretic approaches used in production-grade blockchains like Algorand, Avalanche, and Prism. We introduce a payment mechanism similar to a classic “stag hunt” scenario for mutual benefit through honest bidding. We propose a consensus mechanism that combines a weighted lottery pool and a second-price auction system for candidate selection and cost calculation. Our model incentivizes nodes to bid truthfully, reflecting their true value estimation of validating a block. We set up a PoS blockchain, the initialization of nodes and violators, and proceeded to simulate the addition of new blocks. We record the conditions of the bids and rewards to demonstrate that our mechanism promotes a fairer stake distribution while minimizing disparity among validators. Although a perfectly equitable system is challenging to design, our method offers a significant step towards a more equitable, secure, and efficient blockchain system.
Rule-Based Text Simplification for Extractive Automatic Summarization of News Articles in Indonesian

Text simplification is one of the techniques to improve the performance of an automatic summarization. Currently, the research about Indonesian text simplification is few. In this paper, we adapted rule-based text simplification for Indonesian language, and integrated it to the extractive automatic summarization of news articles in Indonesian. This integration was conducted to investigate its influence in automatic summarization performance. We adapted a text simplification method that was originally designed for the English language to build the Indonesian text simplification module. Additionally, we built a text simplification dataset based on an existing dataset to evaluate our module. Then, we evaluated the performance of the previous extractive automatic summarization of news articles in Indonesian using our module. We show that the text simplification module for the Indonesian language needs to be adjusted for the cases that are not addressed in the English language. With the text simplification module, the performance of automatic summarization of news articles in Indonesian is decreased in recall, but it is increased in precision.
Application of Neural-Controlled Differential Equations in Disease Progression Modeling Using Irregularly Sampled Multimodal Data

This paper presents a novel application of neural Controlled Differential Equations (Neural-CDEs) in modeling disease progression using multimodal data. Two deep learning approaches - Long short-term memory (LSTMs) and Neural-CDE models - were evaluated using structured and multimodal (combination of structured and image) time series data. The dataset used in the current study was from the Open-Source Imaging Consortium (OSIC). Using only the structured data, the test RMSE of the Neural-CDE was lower (0.2693) than the LSTM (0.3745), reflecting the better performance of the Neural-CDE approach. Furthermore, modeling the data by the addition of a CT scan to the structured data (multimodal), resulted in an improvement in Neural-CDE performance, with a lower test RMSE (0.2352). Compared to the literature reports, Neural-CDEs using multimodal data performed well.
MDE - Multimodal Data Explorer for Flexible Visualization of Multiple Data Streams

Static data visualizations are pervasive across STEM research disciplines. However, as the complexity of datasets continues to grow, often incorporating multiple modalities, the limitations of static visualization become apparent. Multimodal datasets require new tools for data exploration and visualization. In response to the limited capabilities of prior alternatives which tend to be less flexible or center on project-specific visualizations for data exploration, we are developing the Multimodal Data Explorer (MDE) - a novel application which is capable of exploring custom time-series multimodal data in context, providing a comprehensive solution that can enable a deeper understanding of the relationships between multiple streams of data. In addition, the client-server design of MDE facilitates multiple users’ data exploration at the same time.
What’s Next in Affective Modeling? Large Language Models

Large Language Models (LLM) have recently been shown to perform well at various tasks from language understanding, reasoning, storytelling, and information search to theory of mind. In an extension of this work, we explore the ability of GPT-4 to solve tasks related to emotion prediction. GPT-4 performs well across multiple emotion tasks; it can distinguish emotion theories and come up with emotional stories. We show that by prompting GPT-4 to identify key factors of an emotional experience, it is able to manipulate the emotional intensity of its own stories. Furthermore, we explore GPT-4’s ability on reverse appraisals by asking it to predict either the goal, belief, or emotion of a person using the other two. In general, GPT-4 can make the correct inferences. We suggest that LLMs could play an important role in affective modeling; however, they will not fully replace works that attempt to model the mechanisms underlying emotion-related processes.
Developing an Uncertainty-Aware Empathetic Conversational System

This document is a model and instructions for LATEX. Natural empathetic dialogue requires an understanding of emotion, intention, and context. In empathetic conversational systems, accurate classification of emotion, intention, and understanding of context contribute to a more natural and effective dialogue between the system and the user. However, due to the subjective nature of these concepts and the lack of contextual cues, the resulting models are susceptible to uncertain outputs, causing misunderstanding and inappropriate response generation in empathetic conversational systems. This research proposal aims to address this issue by proposing the use of Bayesian inference methods to estimate and reduce uncertainty in emotion, intention, and context modeling tasks. By incorporating uncertainty estimates into the downstream task of response generation, the proposed uncertainty-aware approach is expected to improve the performance of empathetic dialogue generation tasks significantly. The potential impact of this research is providing a more natural, effective, and engaging user experience in conversational systems, with applications in areas such as mental health, customer service, and education.
A Weighted Two-Level Ensemble Model for Sentiment Analysis of Finance Minister of Indonesia

The performance of ensemble learning depends on the performance of each weak learner. Assuming all weak learners are equal may lead to poor results. Therefore, determining the weight for each ensemble component is proposed to maximize a two-level ensemble model performance. The proposed model is used to classify the sentiment of Finance Minister Sri Mulyani Indrawati, using Twitter data. The dataset is tested in individual models first called validation. The weight of the two-level ensemble model is determined based on training performance and validation performance by considering individual models’ precision, recall, and F1-Score values. For each metric of an individual model greater than average performance, the weight for that model will increase. Validation performance has eight times the contribution compared to training in weight determination. The comparison with default two-level ensemble learning is needed to show the gain of our proposed model. Experiments on the dataset show that our proposed model improves recall and F1-Score of testing set by 4% without sacrificing the prediction performance of training set. The rationale is that each model usually receives the same weight in ensemble learning, which differs from a weighted ensemble. A weighted ensemble learning highlights the contribution of stronger models and lessens the influence of poorer models by allocating varying weights to individual models based on their training and validation performances.
Scientific Paper Classification by Fusing BERT and GCN

This paper proposes a BERT-based graph convolutional neural network (BERT-GCN) model for classifying scientific papers, which combines a fine-tuned BERT and a graph convolutional neural network to enhance the performance of classification. To enhance the generalization ability of BERT-GCN, this paper proposes using BERT as the basic model and combining three mechanisms - span masking, learning rate attenuation, and data augmentation - to fine-tune the BERT model for scientific paper classification. To ensure classification performance, this paper uses a graph convolutional neural network to capture the structural features of paper titles. Experimental results obtained from the test on two benchmark datasets validate the superiority of the proposed BERT-GCN model that achieves state-of-the-art performance algorithms against the baseline models.
A Comparative Analysis of Sentiment Classification Based on Two-Level Ensemble Models

Sentiment analysis is a method to classify text into positive, negative, and neutral labels. A machine learning model is needed to perform sentiment analysis accurately. A machine learning model called the two-level ensemble model performs better than individual ensemble models. This paper aims to find the best performance of two-level ensemble models with different compositions. The models consist of traditional models and ensemble models. By combining traditional models and ensemble models, six ensemble models can be formed: an ensemble of traditional models, a combination of tree-based models, a combination of two-level bagging-based and boosting-based ensemble models, a two-level bagging-based, a boosting-based ensemble model, and a complete two-level ensemble model. Accuracy, precision, recall, and F1-Score are considered metrics for the evaluation process. The experimental results show that the complete two-level ensemble model has the best overall performance, with an average accuracy of 83.5%. In contrast, the two-level bagging-based ensemble performs best for specific labels in 50% of cases. This is due to the fact that combining all models allows one model’s strengths to compensate for another model’s flaws. In our situation, the strength of the bagging-based models in the training set and the tree-based models in the testing set is absorbed by the complete two-level ensemble model.
Speech Emotion Classification from Affective Dimensions: Limitation and Advantage

In affective computing, two main paradigms are used to represent emotion: categorical representation and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. Our approach contains a neural regressor which predicts a vector of arousal, valence and dominance values for a given speech segment. This vector can be interpreted as an emotional category using a mapping algorithm. We investigate the performances of a neural network architectures, and three mapping algorithms on two corpora. Our study shows the limitation and an advantage of the emotion classification via regression approach.
Deep Reinforcement Learning Based Group Recommendation System with Multi-Head Attention Mechanism

The role of group recommender systems is pivotal in recommending content to groups of users across various information systems. In this paper, we propose, a deep reinforcement learning based Group Recommendation system with Multi-head Attention mechanism (GRMA), to address the need for dynamic preference aggregation in group recommender systems. By incorporating multi-head attention, GRMA captures varying weights of group members and their interactions with different items, resulting in a comprehensive modeling of member-item interactions. The multi-head attention mechanism enhances the system's adaptability to varying group dynamics and preferences, enabling personalized and context-aware recommendations. Evaluations on the MovieLens-Rand dataset, a random group dataset generated from MovieLens 1M, using two baselines (DRGR and AGREE), demonstrate that GRMA outperforms DRGR by enabling a more comprehensive modeling of member-item interactions. Moreover, GRMA achieves a better NDCG@K score compared to AGREE as GRMA using a multi-head attention mechanism improves ranking quality by placing more pertinent items at the top of the recommendation list.
Attacking a Transformer-Based Models for Arabic Language as Low Resources Language (LRL) Using Word-Substitution Methods

Transformer-based models are achieving high performance on a variety of natural language processing (NLP) tasks for example: sentiment analysis, information extraction or text classification. These architectures provide state-of-the-art solutions for most of the problems that NLP tasks face nowadays. However, it is really important to examine their effectiveness and robustness. To do so, researchers tried to attack these models with adversarial examples, which are small perturbations that make small changes in the actual inputs in order to mislead the models to check their vulnerabilities. However, most researchers have done their experiments on Transformer-based models that are trained on high resources corpora such as English. In this paper, we examine Transformer-based models for low resources languages (LRL) using word-substitution attacking algorithms for text classification tasks in Arabic, as a representative of LRL. Our models were successfully fooled after attacking it with word-substitution methods called Random and Prioritized, respectively. As a result of our experimentation, the accuracy of our Transformer-based models that we have attacked has been reduced by 7% and 10% on Random attack, and 44% and 40% on Prioritized attack.
Visual-Semantic Refinement Network: Towards Exploring the Capabilities of Decoder in Scene Text Recognition

Traditional scene text recognition (STR) is usually regarded as a visual unimodal recognition task, which has made some progress using the encoder-decoder framework. Introducing the language model (LM) that taps into semantic contextual relationships has significantly promoted the task from the language modality. However, in existing works, LM seriously relies on the output of the decoder in the vision model (VM), and the vision decoder itself lacks semantic and global context awareness. In this paper, we explore the capability of the vision decoder, which is generally ignored in previous works. We propose a Visual-Semantic Refinement Network (VSRN) to provide context and semantic guidance to the decoder, fully supporting the recognition capability. With the semantic refine module, the recognition results in the LM, in return, can be introduced to the VM. It provides semantic information while further facilitating the union of these two modalities. In the visual refinement module, we propose an adaptive mask strategy and explore visual features’ global contextual relationships to assist the VM further. The two complementary clues jointly promote the VM and iteratively improve the recognition performance. Experimental results on several scene text recognition benchmarks show that our proposed method is effective and achieves state-of-the-art performance.
Efficient Scene Reconstruction for Unmanned Aerial Vehicles

There is demand for small, cost-effective Unmanned Aerial Vehicles (U A V s) in industrial applications. These machines are expected to perform autonomous decision-making tasks. Recent work shows promise for systems based on images or video. We are motivated to study Structure From Motion (SFM), a three-dimensional (3D) reconstruction technique from two-dimensional images. It uses low-cost cameras rather than expensive LIDAR such as laser scanning sensors. The goal of this work is to seek an efficient way to improve accuracy and performance of SFM on small UAVs that lack the computation needed to perform costly algorithms such as neural networks. A major step of SFM is to estimate the homography between two views of the same scene by matching pairs of distinct features in an image (also known as geometric verification). Accurate estimation of pairs leads to better 3D models. We study feature-based homography estimation with various Random Sample Consensus (RANSAC) algorithms. Specifically, pre-sampling is one of the most common strategies utilized to improve the original RANSAC algorithm. Inspired by the work of others, we implement a new pre-sampling RANSAC method. Our adaptation, Blur and Contrast RANSAC (BC-RANSAC), has state-of-the-art performance while reducing run-time by a factor of 6.78.
BED: Bi-Encoder-Based Detectors for Out-of-Distribution Detection

This paper introduces a novel method leveraging bi-encoder-based detectors along with a comprehensive study comparing different out-of-distribution (OOD) detection methods in NLP using different feature extractors. The feature extraction stage employs popular methods such as Universal Sentence Encoder (USE), BERT, MPNET, and GLOVE to extract informative representations from textual data. The evaluation is conducted on several datasets, including CLINC150, ROSTD-Coarse, SNIPS, and YELLOW. Performance is assessed using metrics such as F1-Score, MCC, FPR@90, FPR@95, AUPR, and AUROC. The experimental results demonstrate that the proposed bi-encoder-based detectors outperform other methods, both those that require OOD labels in training and those that do not, across all datasets, showing great potential for OOD detection in NLP. The simplicity of the training process and the superior detection performance make them applicable to real-world scenarios. The presented methods and benchmarking metrics serve as a valuable resource for future research in OOD detection, enabling further advancements in this field. The code and implementation details can be found on our GitHub repository: https://github.com/yellowmessenger/ood-detection.
Knowledge guidance combined with end-to-end method to solve math word problem without implicit knowledge

Over the past few years, there has been an increasing fascination with the automated resolution of mathematical challenges. Many researchers have developed their own algorithm models, achieving success on the public dataset Math23k. Specifically, end-to-end algorithm models based on deep learning have shown a significant improvement in solving accuracy compared to other methods. However, these end-to-end deep learning models do not perform well in handling questions that lack implicit information. The reason for the poor performance of existing models is the lack of relevant background knowledge in solving such problems. Therefore, this paper proposes a solving strategy that combines knowledge guidance with an end-to-end approach for mathematical application questions that lack implicit information. The experimental results demonstrate the effectiveness of this method in improving the solving accuracy of various models for mathematical application questions that lack implicit information.