Reviewerly/exHarmony huggingface dataset:
A Multi-Objective Co-Operative Co-Evolutionary Method for Classification with Imbalanced Data

In this paper, the authors propose a co-operative co-evolutionary ensemble algorithm (named IBMCCA) for imbalanced data classification. IBMCCA resolves the imbalance issue by simultaneously optimizing multiple objectives, including maximizing classification accuracy for both majority and minority classes and minimizing the selected features and instances. In IBMCCA, two different populations represent different aspects of the classification problem, such as feature selection, instance selection. These populations collaborate and exchange information through a co-operative co-evolutionary framework to find optimal individuals that have both convergence and diversity factors. These individuals are then utilized to generate a collection of subsets of data that are used to generate classifiers in ensemble learning algorithms. Combined with hybrid data resampling methods, IBMCCA has shown a good ability to handle problems related to imbalanced data. Experimental results on 21 datasets and comparisons with many other algorithms have clearly illustrated the efficacy of the proposed algorithm for enhancing the classification performance on imbalanced data.
Homomorphic Encryption for AI-Based Applications: Challenges and Opportunities

Homomorphic encryption is a technique that allows computations with encrypted data without revealing the data or requiring decryption. This technique has many potential applications in artificial intelligence (AI), where privacy and security are critical. However, implementing homomorphic encryption for AI also presents many challenges and opportunities from a software engineering perspective. In this paper, we provide a comprehensive overview of the current state of the art in homomorphic encryption for AI-based applications and discuss some important software engineering aspects of implementing homomorphic encryption solutions for AI scenarios. We review recent research challenges in applying homomorphic encryption to AI-based applications and discuss their software engineering aspects. We also compare different homomorphic encryption algorithms and libraries in terms of their security, performance, usability, interoperability, functionality, and scalability. Finally, we highlight some open challenges and future directions for research and development in this area.
A New Method for Vietnamese Text Correction using Sequence Tagging Models

In this paper, we present a new approach for Vietnamese text error correction. A corrector consists of a Transformer to encode the input sequence, and sequence tag-gers to perform both error detection and correction. For the taggers, we apply special tokens to process insertions, deletions, and substitutions. The correction is performed in many steps repeatedly until the stopping criteria are met. At each step, we just correct the source sentence with minimal spans of tokens. These solutions make two advantages including 1) detecting and correcting various error types of Vietnamese texts, and 2) not generating uncontrollable outputs as generative models. As a result, our approach has yielded remarkable performance. On realistic dataset, our proposal model archives 79.5 % errors detected and 62.7% errors corrected; the highest SacreBLEU score is 86.10, that is a promising result.
Public Opinion Mining Using Large Language Models on COVID-19 Related Tweets

Social media platforms have emerged as a significant source of public opinion, offering a massive user-generated data in which user-opinions are valuable if obtainable. Large language models (LLMs) have been in the spotlight recently with suggestions on the emergent abilities to solve tasks that are not explicitly trained for. Thus, this study explores the potential of utilizing LLMs for opinion mining on social media data by asking LLMs difficult questions, instead of simply asking whether the text's sentiment polarity is either positive, negative, or neutral. This study compares the LLM response statistics and the corresponding public surveys related to COVID-19, including the intention to take vaccination and the stress check. The results indicate that it is promising, but also challenging, to utilize LLMs for the tasks.
Bayesian network parameter learning with constraint balance

Maximum likelihood estimation (MLE) is a method for data modeling in artificial intelligence, which is intended to pursue the best fit of data and parameters. When the training data are insufficient, the model parameters that rely only on MLE for learning are unreliable. Similarly, for Bayesian networks (BNs), a crucial tool for uncertainty representation, the problem of low accuracy of parameter learning caused by insufficient data is also faced. Due to their clear structure and interpretability, BN can effectively fuse the parameter constraints transformed from prior knowledge to improve learning accuracy using the maximum a posteriori estimation (MAP). However, balancing data and constraints evolves into a new concern. With too much reliance on constraints, the data loses meaning, and the obtained prior knowledge can not be disregarded. Therefore, this paper proposes a constraint-balanced maximum a posteriori estimation method, which performs parameter learning by balancing the roles played by data and constraints in the parameter learning process. Through experiments, the proposed method can effectively improve the accuracy of parameter learning.
Cinnalyst: A Sales Meeting Analysis System

Analysis of sales meetings is vital in many businesses to obtain insights about clients' needs and evaluate sales representatives' performance. This paper introduces a sales meeting analysis prototype, Cinnalyst, which helps sales managers observe how their employees perform in client meetings. By conducting statistics from meetings in the past, it can reveal many important factors to improve sales efficiency. To automatically analyze the sales meeting content, the proposed system includes three modules: speaking party detection, conversational topic classification, and dialogue act classification. We demonstrate the performance of Cinnalyst in practical cases where users can experience the analyses of conversational topics and dialogue acts in sales meetings. A demonstration video is attached with this paper at https://youtu.be/d7toaexlB0E.
Facial Emotion Recognition by Combining Deep Learning and Averaged Weight of Face-Regions

Expression of the face is one of the most natural features and most impressive signals for humans to convey their state of emotions and intentions. This paper proposes a new face emotion recognition method using a deep learning model along with a weighted average of face-regions. Firstly, the deep learning method is used to identify face image and facial landmarks. Then, the face image is divided into 7 facial regions (2 eyes, nose, forehead, mouth and 2 cheeks). Subsequently, Convolutional Neural Network (CNN) is trained to indicate each emotion on each sub-region of the entire face and a weighting assessment set for each set of identifications is used to combine with the predicted result. The experimental results on the FER2013 dataset has demonstrated that proposed method achieves an accuracy of 74.14 which outperforms the state-of-the-art methods.
The Brukner-Zeilinger invariants in terms of (N, M) − POVMs

Abstract Brukner–Zeilinger invariants, including invariant information and invariant uncertainty, play a remarkable role in quantum information theory. Recently, a broad class of symmetric informationally complete measurements is presented, which contains a general symmetrical information complete POVMs and mutually unbiased measurements (2022, Phys. Rev. A 105 , 042 209). Based on this important measurement and variance, we further analyze the internal relationship between Brukner–Zeilinger invariants in detail. The results maybe provide a method to explain the importance of Brukner–Zeilinger invariants.
HHMC: A Heterogeneous x Homogeneous Graph-Based Network for Multimodal Cross-Selling Recommendation

Currently, research models that effectively predict cross-selling products while utilizing multimodal data sources are limited, and similarly, models focusing on multimodal recommendation do not adequately address cross-selling. To address this gap, our study introduces the model HHMC: A Heterogeneous x Homogeneous Graph-based Network for Multimodal Cross-Selling Recommendation. This innovative approach leverages historical order's data and diverse multimodal data to recommend cross-selling products. The architecture of model HHMC is thoughtfully designed to explore potential relationships between user-item and item-item interactions while also improving the efficiency of item feature representation through the enrichment of multimodal data sources. Due to the scarcity of published datasets for the cross-selling recommendation problem, we utilized the well-known Instacart dataset to define and explore empirical directions for addressing this challenge. Experimental results demonstrate that HHMC surpasses widely used deep learning-based techniques, highlighting its potential to effectively address multimodal cross-selling recommendation problems.
Constructing a Closed-Domain Question Answering System with Generative Language Models

Generative language models such as ChatGPT are good at producing answers for questions whose answers are publicly available. For questions about private organization documents, such models cannot perform well due to the fact that they were not trained on these documents, hence the knowledge was not embedded in their parameters. On the other hand, traditional Question Answering (QA) systems with a Retriever and a Reader model are interpretable and can be trained quickly on private documents, but they require laborious annotation. The output of QA systems is a span of text, which is not friendly to the end users as well. In this paper, we proposed a framework for generating closed-domain QA data set in a semi-automatic manner, reducing human efforts. An organization-specific QA data set was created based on this framework. Additionally, we fine-tuned a traditional open-domain QA model on the newly created data set, and combine the output of this model with recently released language models to improve the naturalness of the response. Finally, we present our results and discuss the findings.
The Making of a Newspaper Interview in Virtual Reality: Realistic Avatars, Philosophy, and Sushi

VR United is a virtual reality application that we have developed to support multiple people simultaneously interacting in the same environment. Each person is represented with a virtual body that looks like themselves. Such immersive shared environments have existed and been the subject of research for the past 30 years. Here, we demonstrate how VR United meets criteria for successful interaction, where a journalist from the Financial Times in London interviewed a professor in New York for two hours. The virtual location of the interview was a restaurant, in line with the series of interviews published as "Lunch with the FT." We show how the interview was successful, as a substitute for a physically present one. The article based on the interview was published in the Financial Times as normal for the series. We finally consider the future development of such systems, including some implications for immersive journalism.
MAUNext: a lightweight segmentation network for medical images

Objective. The primary objective of this study is to enhance medical image segmentation techniques for clinical research by prioritizing accuracy and the number of parameters.Approach. To achieve this objective, a novel codec-based MAUNext approach is devised, focusing on lightweight backbone design and the integration of skip connections utilizing multiscale, attention mechanism, and other strategic components. The approach is composed of three core modules: a multi-scale attentional convolution module for improved accuracy and parameter reduction, a collaborative neighbourhood-attention MLP encoding module to enhance segmentation performance, and a tiny skip-connected cross-layer semantic fusion module to bridge the semantic gap between encoder and decoder.Main results. The study extensively evaluates the MAUNext approach alongside eight state-of-the-art methods on three renowned datasets: Kagglelung, ISIC, and Brain. The experimental outcomes robustly demonstrate that the proposed approach surpasses other methods in terms of both parameter numbers and accuracy. This achievement holds promise for effectively addressing medical image segmentation tasks.Significance. Automated medical image segmentation, particularly in organ and lesion identification, plays a pivotal role in clinical diagnosis and treatment. Manual segmentation is resource-intensive, thus automated methods are highly valuable. The study underscores the clinical significance of automated segmentation by providing an advanced solution through the innovative MAUNext approach. This approach offers substantial improvements in accuracy and efficiency, which can significantly aid clinical decision-making and patient treatment.
Reconstructing Missing Joints in 3D Human Motion with Temporal-Structural Awareness Graph Neural Network

In human motion capture-based applications, the 3D captured pose data usually contains missing joints due to various factors such as environmental conditions, camera quality, and joint occlusions. Since the 3D pose data can be represented as a sequence of graphs, Graph Neural Networks can be used to effectively capture the complex and dynamic relationships among the nodes in the data. In this paper, we model the pose sequence of human motion as a dynamic graph with missing nodes' positions in 3D coordinates and reconstruct it by exploiting the temporal and structural properties of the input graph. Based on this modeling, we propose the TA-WLS architecture, which utilizes concatenate aggregation to preserve the local structural information and an attention mechanism to effectively capture the temporal relations among nodes. The proposed TA-WLS is tested on the Human 3.6M dataset under various missing conditions, and the results show that our TA-WLS can significantly outperform other baselines by a large margin.
Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning

Abstract Offline reinforcement learning agents seek optimal policies from fixed data sets. With environmental interaction prohibited, agents face significant challenges in preventing errors in value estimates from compounding and subsequently causing the learning process to collapse. Uncertainty estimation using ensembles compensates for this by penalising high-variance value estimates, allowing agents to learn robust policies based on data-driven actions. However, the requirement for large ensembles to facilitate sufficient penalisation results in significant computational overhead. In this work, we examine the role of policy constraints as a mechanism for regulating uncertainty, and the corresponding balance between level of constraint and ensemble size. By incorporating behavioural cloning into policy updates, we show empirically that sufficient penalisation can be achieved with a much smaller ensemble size, substantially reducing computational demand while retaining state-of-the-art performance on benchmarking tasks. Furthermore, we show how such an approach can facilitate stable online fine tuning, allowing for continued policy improvement while avoiding severe performance drops.
NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment

In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain. This paper presents NeCo Team's solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on legal domain knowledge acquisition for low-resource languages through data enrichment. Our methods for the legal document retrieval task employ a combination of similarity ranking and deep learning models, while for the second task, which requires extracting an answer from a relevant legal article in response to a question, we propose a range of adaptive techniques to handle different question types. Our approaches achieve outstanding results on both tasks of the competition, demonstrating the potential benefits and effectiveness of question answering systems in the legal field, particularly for low-resource languages,
Post-Quantum Signature Scheme on Commutative Algebras

Using two 3-dimensional finite algebras with commutative and associative multiplication operation as a single algebraic support, a post-quantum digital signature scheme is developed, which is characterized in exploiting the 3-dimensional cyclicity of the multiplicative groups of the algebras. The security of the introduced signature algorithm is based on the computational comlexity of the hidden logarithm problem given simultaneously in two finite algebras. Doubling the signature verification equation and scalar multiplications are used as particular design techniques. The advantage of the developed algorithm is the comparatively small sizes of the public key and signature.
Bi-Directional Machine Translation Between Amharic and Khimtagne Using Deep Learning

Natural Language Processing (NLP) is a specialized field of AI that focuses on the interaction between humans and computers in language usage. One practical application of NLP is machine translation, which allows for automated translation without human intervention. In this study, we aimed to develop a machine translation model for Amharic-Khimtagne language pairs using deep learning approaches. Currently, a vast amount of information in Ethiopia is available in Amharic, including rules and regulations, mass media news, religious books, educational materials, and official documents. With the growing number of language users in Khimtagne, developing machine translation systems between Khimtagne and Amharic is crucial for sharing information. In this study, we experimented with four deep learning encoder-decoder models, including LSTM, LSTM with attention, CNN with attention, and Transformer, using 17,153 parallel sentences for each model in bi-directional translation between Amharic and Khimtagne. Our proposed Transformer model achieved a BLEU score of 4.52 for Amharic to Khimtagne and 4.19 for Khimtagne to Amharic translation. However, the lack of sufficient data is a major weakness of this study, and additional parallel corpora are necessary for further research.
OphNER: Named Entity Recognition for Ophthalmology Newspapers

The Fourth Industrial Revolution has turned electronic devices into the main tools in human daily life. At the same time, the consequences of the developed economy, such as environmental pollution, viruses, and bacterial strains, are also the main causes of eye diseases. Under the development of media, eye diseases have been quickly and fully synthesized through different types of texts. Texts on ophthalmology provide information about symptoms, disease manifestations, agents, prevention, or treatment in great detail. A large amount of information makes it more difficult to find and filter. Since then, it has become more urgent to build both a corpus and a system to identify and categorize the information from official sources so that everyone can easily find relevant information and better understand related terms to ophthalmology. One of the systems to search for information related to keywords is named entity recognition (NER). To help address this problem, we release the OphNER (Ophthalmology Named Entity Recognition) dataset - the first corpus containing nearly 9,000 sentences with more than a total of 17,447 labels of 16 entities. We also conduct experiments with state-of-the-art models. The highest result belongs to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{RoBERTa}_{large}$</tex> , which is better than XLM <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}_{large}$</tex> or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{XLNet}_{large}$</tex> . Our dataset is publicly released on Github <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://github.com/baohl00/OphNER for further reference in the research community.
Contrastive Learning for Vietnamese Abstractive Summarization

Abstractive summarization is an important task and attracts plenty of research in Natural Language Processing (NLP). Today's state-of-the-art (SOTA) solutions for this problem are often implemented using a Sequence-to-Sequence (Seq2Seq) language model, with pre-training then fine-tuning paradigm. However, those Seq2Seq models have been haunted for a long time by two problems: exposure bias [1] and the mismatch between the training objective and the evaluation metric, which is a trade-off of using teacher forcing strategy and maximum likelihood estimation algorithm. In this work, to address that issue for Vietnamese abstractive summarization models, we employed the idea of combining a two-stage training pipeline with contrastive learning by ranking multiple candidates that was introduced recently via the BRIO summarization system [2]. The results show that the new approach improved the performance of all the models in the experiments, with some of them showing a significant gain (4.78 in ROUGE-l score for BARTpho [3] on WikiLingua [4] and 2.1 in ROUGE-1 score for BARTpho [3] on Vietnews [5]). The implementation and models to reproduce the result can be found at github.com/MinhDang685NViSum
Gender Bias Analysis in Commonsense Knowledge Graph Embeddings

In this study, we explore the existence of gender bias in ConceptNet triples by employing knowledge graph embedding models, a crucial aspect that has gained prominence in the artificial intelligence domain. Commonsense knowledge graphs, such as ConceptNet, play a vital role in an array of natural language processing tasks; nonetheless, the potential gender bias embedded within these graphs may considerably influence the fairness of associated applications. To tackle this concern, we introduce a comprehensive gender bias analysis framework tailored for ConceptNet triples, with a focus on the link prediction task utilizing knowledge graph embedding models. By evaluating multiple state-of-the-art knowledge graph embedding models, we assess the degree of inherent gender bias across the models. Furthermore, we investigate mitigation strategies to attain a debiased representation of gender within commonsense knowledge graphs. Our results contribute to a broader comprehension of gender bias in knowledge graphs, and the proposed mitigation strategies lay the groundwork for developing more equitable AI systems that use ConceptNet.
A Dynamic Algorithm for the Linear Layer of SPN Block Ciphers Based on Self-Reciprocal Recursive MDS Matrices

SPN block ciphers are widely used in various fields today. Research on enhancing the dynamism of SPN block ciphers has become a topic of interest among cryptography experts. Self-reciprocal recursive MDS matrices, which possess valuable properties for implementation, are being explored in this regard. In this paper, we propose a dynamic algorithm for the linear layer of SPN block ciphers based on self-reciprocal recursive MDS matrices and this algorithm can generate involutory MDS matrices that are very useful for execution. We enhance the AES block cipher by incorporating dynamic Mix-column transformations using our proposed algorithm. Furthermore, we evaluate the security, assess NIST statistical standards, and measure the execution speed of the improved dynamic AES block cipher. The proposed algorithm shows potential in strengthening the AES algorithm's security.