Reviewerly/exHarmony huggingface dataset:
Enhancing Aspect-Based Sentiment Analysis with Contextualized Window Attention Mechanism

In recent years, sentiment analysis has been a heated topic across the globe in general and particularly in Vietnam. Sentiment analysis plays an important role in promoting the economic growth. For instance, marketing departments from big corporations rely mostly on analytic of customers's attitude to develop marketing strategy for their company. Knowing its significance, we implement a system specifically for this problem. Most traditional approaches for Vietnamese sentiment analysis learn the context weight of every word in a sentence. However, we discover that in most cases only a part of a sentence, which consists of a group of continuous words actually influences the polarity of an aspect. Hence, we introduce a novel approach called Contextualized Window Attention, which helps our models learn the context of such groups instead of a single word. We experiment with 4 different models and train them on dataset crawled from Shopee and Tiki, which are 2 of the most popular and trusted E-commerce platforms in Vietnam. However, across every aspect, comments that are negative makes up of only 5% approximately of total comments. Hence, we only considerate it valuable when evaluating on the negative label. Our best model obtained the maximum micro- and macro-average performance of 76.66% and 78.62% on the negative label, respectively.
Detection and Recognition of Sino-Nom Characters on Woodblock-Printed Images

There are many documents written in Sino-Nom capturing a wide range of historical, political, and literary facets of Vietnamese culture. To contribute to the process of preserving those documents, we focus on the task of building Optical Character Recognition models for Sino-Nom characters using a semi-supervised approach. Also, we propose a pipeline for collecting needed Sino-Nom document images which are used for training and evaluating our models. Being evaluated on our collected dataset, our OCR baseline achieves F1 scores of over 0.97 in detection and top-l and top-5 accuracies of 80.1% and 90.1% in recognition, respectively.
Increased Query2Label (IQ) for Small Fine-grained Multi-label Classification

Multi-label image classification aims to identify all object labels within an image. Small objects and similar objects are still the primary difficulties of previous related models due to the representative capacity of convolutional kernels. Recent vision transformer networks employ the attention mechanism to extract the spatial feature, which expresses richer local semantic information but is insufficient for creating high performance in fine-grained or small-object scenarios. To overcome these disadvantages, this paper proposes a solution by using the definition of the label as text embedding and adjusting the modification of the decoder stage so the model can acquire more information. The new framework is simple, efficient, and consistently outperforms all preceding works on the FATHOMNET, FAIR1M, and DOTA datasets.
VFFINDER: A Graph-Based Approach for Automated Silent Vulnerability-Fix Identification

The increasing reliance of software projects on third-party libraries has raised concerns about the security of these libraries due to hidden vulnerabilities. Managing these vul-nerabilities is challenging due to the time gap between fixes and public disclosures. Moreover, a significant portion of open-source projects silently fix vulnerabilities without disclosure, impacting vulnerability management. Existing tools like OWASP heavily rely on public disclosures, hindering their effectiveness in detecting unknown vulnerabilities. To tackle this problem, automated identification of vulnerability-fixing commits has emerged. However, identifying silent vulnerability fixes remains challenging. This paper presents VFFINDER, a novel graph-based approach for automated silent vulnerability fix identification. VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and represents them in annotated ASTs. VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features. We conducted experiments to evaluate VFFINDER on a dataset of 36K+ fixing and non-fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30%-109% in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 47% with the same review effort of 5% compared to the existing approaches.
Federated Learning in Smart Agriculture: An Overview

Agriculture is critical to the socioeconomic growth of countries across the world. Agricultural methods in many countries have been affected by current scientific and technical breakthroughs. However, ASEAN nations, which rely largely on agriculture, have an urgent need for smart agricultural research. While detailed assessments have been undertaken throughout the world, a gap in the research of federated learning has been observed. This article intends to fill this need by collecting research on machine learning, deep learning, and federated learning techniques in agriculture from both industrialized countries and those with economic constraints similar to those of ASEAN. Analyses have revealed that the combination of federated learning with IoT devices is the best way to develop smart agriculture.
Experimental Comparison between Neural-Symbolic Question-Answering Methods

This paper presents an experimental comparison and analysis between two representative methods for neural-symbolic question-answering: the Complex Query Decomposition (CQD) method and the Graph Neural Network Question Embedding (GNN-QE) method. Starting with large and complex queries, CQD breaks down the large query into shorter and simpler sub-queries, thus decomposing the initial query into more manageable components. On the other hand, GNN-QE is a recent architecture for neural-symbolic question-answering that uses graph neural networks to encrypt question structures. GNN-QE portrays questions as graphs to capture the innate links between different question elements, allowing for more complex and comprehensive reasoning. This paper examined the main characteristics of CQD and GNN-QE methods and analyse their advantages and drawbacks towards question-answering problem through popular performance metrics, such as MRR and Hits@K. The results show how each method handles complex queries through the use of symbolic and neural representations, and how well it can produce precise and insightful responses.
Digital Image Watermarking Algorithm Using Blockmarking Technique for Copyright Protection

Recently, blockmarking technique [13] is proposed for combination of blockchain and watermarking method. In this model, it not only achieves the purpose of image copyright protection but also stores the image into the blockchain network such as IPFS. In this paper, we propose a new DRM system by inheriting the idea of blockmarking. The copyright contents can be distributed via IPFS, then be restored by using the reconstruction license for each legal user. Also, in our method, based on the reconstruction licenses, the distributed contents can be reconstructed from IPFS with various watermarking patterns. It helps us can manage the legal users and trace the traitor if a dispute occurs. Experimental results show that the proposed method successfully achieves the goal of copyright protection.
Building 8×8 Effective Block Circulant MDS Matrices by Utilizing Hadamard Matrices and Circulant Matrices

The MDS matrix is widely used to design the linear layer of modern block ciphers. A selected MDS matrix needs to meet the safety and efficient installation criteria. In this paper, we propose a type of effective block circulant MDS matrices by utilizing Hadamard matrices and circulant matrices. Based on the special structure of this matrix form, we have stated and proven some theoretical results. Then, we select a number of 8×8 effective block circulant MDS matrices. In addition, we also estimate the installation resources required for these recommendations and make comparisons with previous publications. As a result, we can efficiently implement these MDS matrices for encryption and their inverse matrices.
kNN Join for Dynamic High-Dimensional Data: A Parallel Approach

The k nearest neighbor (kNN) join operation is a fundamental task that combines two high-dimensional databases, enabling data points in the User dataset U to identify their k nearest neighbor points from the Item dataset I. This operation plays a crucial role in various domains, including knowledge discovery, data mining, similarity search applications, and scientific research. However, exact kNN search in high-dimensional spaces is computationally demanding, and existing sequential methods face challenges in handling large datasets. In this paper, we propose an efficient parallel solution for dynamic kNN join over high-dimensional data, leveraging the high-dimensional R tree (HDR Tree) for improved efficiency. Our solution harnesses the power of Simultaneous Multi-Threading (SMT) technologies and Single-Instruction-Multiple-Data (SIMD) instructions in modern CPUs for parallelisation. Importantly, our research is the first to introduce parallel computation for exact kNN join over high-dimensional data. Experimental results demonstrate that our proposed approach outperforms the sequential HDR Tree method by up to 1.2 times with a single thread. Moreover, our solution provides near-linear scalability as the number of threads increases.
A real‐time seismic damage prediction framework based on machine learning for earthquake early warning

Abstract For implementing earthquake early warning (EEW), real‐time parameters of ground motion (GM) which can be obtained at the beginning of earthquake wave arrival (i.e., P ‐wave arrival) are usually used to estimate the intensity measure (IM) of the following GM for supporting alarm decision. However, prior studies have proved that the estimated IM is hard to relate structural damage effectively, thus affecting the decision‐making behavior and resulting in more economic losses and casualties. Although the real‐time parameters may be adopted to directly predict structural damage, it is difficult to construct the accurate mathematical model between the parameters and structural damage index. To solve the above problems, a machine learning‐based real‐time damage prediction framework that can directly predict the structural maximum inter‐story drift ratio (MIDR) after P ‐wave arrival is proposed for use in EEW. Within the framework of the proposed method, the database for training the prediction model come from the accurate nonlinear time‐history analyses of 32 typical building types under the real GM records, where the real‐time characteristic parameters of P ‐wave of the records are taken as the inputs. To achieve accurate and efficient prediction, a total of 32 alternative characteristic parameters of P ‐wave are selected and a parameter optimization approach is developed to remove the redundant and useless parameters for various type of building structure. Moreover, an interpretable approach is adopted to explain the effects of different input parameters on the prediction results and demonstrate the reliability of the proposed method. Finally, the framework is thoroughly assessed and investigated through two seismic damage cases and an occurred seismic event, which has an accuracy rate above 96.4% on average for risk‐consistent early‐warning trigger classification. These results show that the proposed method have the ability of continuously stable and excellent prediction power when using it for EEW.
Evaluation of AES Cryptographic Algorithm on a General-Purpose Map-Scan Accelerator

As one of the most widely used encryption standards, AES must be implemented taking encryption speed into consideration. This paper presents the evaluation of the AES algorithm on a general-purpose Map-Scan Accelerator. AES proves well suited to a Map style of parallelism, with each computational cell processing one block of data. ECB and CTR modes are implemented and evaluated for key sizes of 128, 192, and 256.
YOLO-based Object Detection in Panoramic Images of Smart Buildings

Collecting and analyzing spherical images is one of the crucial fields supporting the digitization of indoor environments, like houses, apartments, offices, or factories, and creating virtual tours of smart buildings. Such images also constitute an important feed for smart indoor devices, like autonomous vacuum cleaners, intelligent production lines, or assistive droids. However, smart devices must correctly identify internal objects on high-resolution spherical images, usually heavily distorted and made with variable lighting conditions. Moreover, the recognition task must be relatively accurate and take place onboard the device, as data transmission to larger analysis centers or workstations does not allow for real-time operation. In this paper, we compare two object detectors from the YOLO family (YOLOv5 and YOLOv8) on the publicly available dataset adjusted to a newer annotation representation that allows for adapting YOLO detectors to spherical images. We verify the effectiveness of these two families of detectors for varying sizes of object detection models, image sizes, and training batch sizes. Our research proves that YOLO models can successfully detect most indoor objects, and their effectiveness is comparable to dedicated detectors having much higher computational complexity.
Text classification is keyphrase explainable! Exploring local interpretability of transformer models with keyphrase extraction

Keyphrase extraction is a widely discussed topic in Natural Language Processing, as it offers a concise summary of the main topics in a document. Interpretability is also an important aspect in Machine Learning as it helps prevent socio-ethical issues, such as bias and discrimination against minorities, or mistakes that may have serious consequences. Interpretability has recently gained prominence in the field of Natural Language Processing, where transformers are the dominant architectures. The goal of interpretability is to provide interpretations that pinpoint the elements of an instance contributing the most to its decision. In this work, we use keyphrase extraction to facilitate the interpretability process, producing smaller, more concise interpretations that also consider word interactions, as keyphrases usually consist of multiple words. Additionally, our technique is based on semantic similarity, making it faster and zero-shot ready, which is ideal for online learning scenarios. We evaluated the effectiveness of our technique through a series of quantitative and qualitative experiments on the well-known BERT model, comparing it against several state-of-the-art competitors.
Designing Concept Drift Detection Ensembles: A Survey

Data streams represent real-world concepts that typically cannot be assumed to be static. Instead, sensors monitoring industrial resources deteriorate, network intrusion attacks exhibit new patterns, consumer behavior or public interest in news topics change. These are examples of a phenomenon commonly known as concept drift. It threatens the performance of estimation models conducting inference on associated data. Explicitly employed concept drift detection is among the most promising approaches to maintain a stable and robust performance of productively used estimators, as it identifies the times when adaptation to new concepts becomes actually necessary. Combining concept drift detectors to form ensembles can additionally increase detection performance as it aggregates their individual specializations and allows advanced processing of concept drift evidence. This survey is the first to systematically retrieve and overview research relevant in the field of concept drift detection ensembles. It identifies the research as being in its initial stages and reveals numerous gaps, which should be addressed to further exploit the substantial performance potential ensembles have over individually applied detectors. Furthermore, it contributes to the literature by discussing and describing key characteristics and principles of their design in a structured manner.
Enhancing recommendation systems with hybrid manifold regularized knowledge graph

Recent advances in graph neural networks have motivated the use of structured information in the form of a knowledge graph in recommendation systems. The most important challenges of real-world recommendation problems are the sparsity issues in which labeled user-item interaction data are sparse compared to the size of the knowledge graph. In this work, we propose a graph-based semi-supervised learning method namely Hybrid Manifold Regularized Knowledge Graph (HMR-KG), to tackle this problem. HMR-KG is an attentive knowledge graph neural network with a lightweight version of manifold regularization to enforce smoothness on the mapping function. Translation-based pre-trained embeddings are also used as initialization for the attentive knowledge graph neural networks. The method is evaluated using three public datasets. Experimental results show that our method not only outperforms state-of-the-art baselines but also yields more consistent results in scenarios with sparsely labeled data. In addition, our lightweight implementation successfully approximates the manifold regularization loss with substantially smaller time complexity.
A Continual Learning System with Self Domain Shift Adaptation for Fake News Detection

Detecting fake news is currently one of the critical challenges facing modern societies. The problem is particularly relevant, as disinformation is readily used for political warfare but can also cause significant harm to the health of citizens, such as by promoting false data on the harmfulness of selected therapies. One way to combat disinformation is to treat fake news detection as a machine learning task. This paper presents such an approach, which additionally addresses an important problem related to the non-stationarity characteristics of the fake news. We elaborated a stream data with the simulation of domain shift based on two popular benchmark datasets dedicated to the fake news classification problem (Kaggle Fake News and Constraint@AAAI2021–COVID19 Fake News Detection). The proposed learning system works in a Continual Learning (CL) framework and integrates a self domain shift adaptation in a machine learning scheme. The method was built following state-of-the-art techniques, that includes Word2Vec as a feature extractor and the LSTM model as a classifier. The performance of the approach has been evaluated over the generated data stream. The convenience of our approach is showed in the results, where the accuracy gain with respect to a CL approach without domain adaptation is observed to be significant.
slidSHAPs – sliding Shapley Values for correlation-based change detection in time series

For volatile multivariate time series, variations in the distributions of the input dimension and the correlation structure present an open challenge. The different distributions before and after a change-point hinder the performance of most of the predictive methods, mostly requiring re-training of the models. The detection of such change points represents a severe problem, as volatile data labeling is often either expensive or delayed in streaming data; Moreover, classical concept drift detectors usually struggle with detecting changes in correlations of multivariate time series’ input variables. We focus on unsupervised change detection, tracking correlation changes in the input variables without class labels. By introducing slidSHAPs, we propose a fully unsupervised change detector for multivariate time series with categorical value domains; our tool detects correlation-based changes through a representation of the correlation structure of the input data. The slidSHAPs series underlines distributional changes even in a few univariate input variables, thus, being more sensitive to changes than any prior change point detection method. In contrast to the well-known application of Shapley values for interpretable machine learning, we use this foundational game-theoretic concept to extrapolate information on the correlation structure of data streams and achieve higher sensitivity towards multiple changes in the empirical evaluation of synthetic and real-world data.
Understanding the Country-Level Security of Free Content Websites and their Hosting Infrastructure

This paper examines free content websites (FCWs) and premium content websites (PCWs) in different countries, comparing them to general websites. The focus is on the distribution of malicious websites and their correlation with the national cyber security index (NCSI), which measures a country’s cyber security maturity and its ability to deter the hosting of such malicious websites. By analyzing a dataset comprising 1,562 FCWs and PCWs, along with Alexa’s top million websites dataset sample, we discovered that a majority of the investigated websites are hosted in the United States. Interestingly, the United States has a relatively low NCSI, mainly due to a lower score in privacy policy development. Similar patterns were observed for other countries With varying NCSI criteria. Furthermore, we present the distribution of various categories of FCWs and PCWs across countries. We identify the top hosting countries for each category and provide the percentage of discovered malicious websites in those countries. Ultimately, the goal of this study is to identify regional vulnerabilities in hosting FCWs and guide policy improvements at the country level to mitigate potential cyber threats.
Adaptive Clustered Federated Learning with Representation Similarity

Federated learning is a promising machine learning paradigm that enables participating clients to train models collaboratively with privacy restrictions. However, one of the most challenging problems in federated learning is that local data on the clients might come from different distributions. Such data heterogeneity among the clients might influence the performance of federated learning methods. In this paper, we propose FedACRS, an algorithm that deals with heterogeneous data by clustering clients with similar data distributions into groups and then performing federated learning within each group. FedACRS measures the similarity between the clients in every round based on the representation similarity and then adaptively discovers the clustering structure among the clients. In order to cluster the clients appropriately, we provide theoretical analysis to help determine the number of potential clusters. The results of extensive experiments in different settings demonstrate the advantage of FedACRS over the compared methods.