Reviewerly/exHarmony huggingface dataset:
Pacesetter Learning for Large Scale Cooperative Multi-Agent Reinforcement Learning

In complex multi-agent reinforcement learning environments, such as Starcraft II, most existing algorithms struggle to scale up to large-scale collaboration tasks. This is partly because learning to precisely control each agent to maximize its contribution to the team becomes increasingly difficult as the number of agents and the dimensionality of the input grow. In this paper, we propose the novel Pacesetter LeARning (PLAR) method, which builds and learns pacesetters to enable agents to consider the situation of neighboring agents and cooperate more effectively in complex, large-scale environments. By leveraging the comprehensive outlook provided by the pacesetters, agents are able to coordinate their actions more finely and achieve better overall performance. To demonstrate the effectiveness of our algorithm, we conduct experiments on both existing and newly generated scenarios. Specifically, we compare our algorithm to existing multi-agent algorithms on the existing scenarios, and we also optimize the StarCraft Multi-Agent Challenge (SMAC) from square complexity of memory to linear, enabling us to construct a larger-scale map for further evaluation. The experimental results demonstrate that PLAR outperforms existing algorithms in large-scale settings.
Multi-label Image Deep Hashing with Hybrid Loss of Global Center and Local Alignment

Deep hashing algorithms are widely used in large-scale image retrieval tasks. Based on deep neural network as the backbone, combined with the design of loss function, deep hashing can transform high-dimensional image inputs into binary hash codes, which better improves the efficiency of image retrieval and reduces the storage space. Most of the existing methods use image pairs or triplets for local similarity constraints, and recently hashing methods based on the global hash centers have been proposed. In this paper, we propose a novel deep hashing method that combines the global and local constraints in order to further improve the effect of deep hashing in image retrieval task. For multi-label images, we extend the global hash center generation method so that each image has multiple hash centers, represented by binary hash codes, with the same number of image categories. Then, multiple global hash central binary codes corresponding to the images are used as anchors, and dissimilar image pairs are selected to construct the triplet loss constraint linking global and local features. Moreover, we construct a partially similar loss function for the images where only part of the classification labels are similar, making more use of multiple labels. Furthermore, we combine the global and local loss functions and propose a novel hybrid loss function for multi-label image deep hashing. Extensive experiments on four multi-label image datasets for image retrieval demonstrate that the proposed method achieves substantial improvement over state-of-the-art hashing methods.
Prompt-Oriented Fine-Tuning Dual Bert for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task that aims to predict sentiment polarity towards a specific aspect occurring in the given sentence. Recently, pre-trained language models such as BERT have shown great progress in this regard. However, due to the mismatch between pre-training and fine-tuning, dealing with informal expressions and complex sentences is facing challenges and it is worthwhile devoting much effort to this. To tackle this, in this paper, we propose a Prompt-oriented Fine-tuning Dual BERT (PFDualBERT) model that considers the complex semantic relevance and the scarce data samples simultaneously. To reduce the impact of such mismatches, we design a ProBERT influenced by the idea of prompt Learning. Specifically, we design a SemBERT module to capture semantic correlations. We refit SemBERT with aspect-based self-attention. The experimental results on three datasets certify that our PFDualBERT model outperforms state-of-the-art methods, and our further analysis substantiates that our model can exhibit stable performance in low-resource environments.
Evaluating Web Crawlers with Machine Learning Algorithms for Accurate Location Extraction from Job Offers

This article focuses on systems designed to extract the location of a job listing from a job advertisement on web pages. It presents the use of classifiers to improve the reliability of automata used to collect this information. Three different algorithms - SVM, Random Forest and XGBoost - were used for this purpose. Accuracy, precision, recall and F1 score were used to evaluate the performance of each algorithm. While XGBoost performed best with an accuracy and F1 score of nearly 94%, all three algorithms showed very similar results. This suggests that each of the three algorithms can be effectively used to improve the accuracy of indexing robots in identifying jobs in job listings.
Towards a Hybrid Document Indexing Approach for Arabic Documentary Retrieval System

We present in this paper, a semantic indexing approach based on the meanings of words, or concepts, in the representation of documents. These concepts are identified by the word sense disambiguation, using in jointly the Arabic Wordnet linguistic resource. Subsequently, to calculate the score and to evaluate the semantic proximity between the concepts, we based on the measures of similarities between the different meanings of the terms (candidate concepts). In addition, we propose an association rule-based model for extracting concepts belonging to the contextual domain that are hidden and implicitly defined. The evaluation of the proposed method shows the advantage of the contextual relations in the quality of the semantic indexing We obtained a high precision by validating 80.2% of rules and detecting other semantic and contextual relations between concepts. Intended to be generic because it can be used both in an documentary retrieval system.
Efficient Pruning Strategy for Mining High Utility Quantitative Itemsets

High Utility Quantitative Itemset Mining (HUQIM), which is an extension of the original High Utility Itemset Mining (HUIM), has become an important research area that answers the ever-growing need for useful information from the copious pool of data in reality. Due to the nature of the HUQIM problem, its search space is huge and could heavily affect the execution time. Thus, the recently proposed FHUQI-Miner has overcome these limits with novel pruning strategies to narrow the space and outperform previously introduced algorithms. However, there are certain shortcomings that the algorithm still faces. One of the limitations is that the proposed strategies would not operate as efficiently on dense datasets as they would on sparse datasets, resulting from the similarity in structure of the transactions and thus increasing the number of join operations in progress. To address this limitation, this work introduces an enhanced version of the FHUQI-Miner algorithm with an improved TQCS structure to reduce mining time and the number of joins performed.
Initial Approach to Pharmaceutical Opinion Search in Polish Language

In recent years, the Internet has tended to speed up the publication of opinions on almost every topic. The volume of published texts results in a huge amount of data that the average Internet user is not able to analyze. At the same time, it can be observed that a very important topic that is becoming increasingly popular on the web is drugs and pharmaceuticals. Taking these two facts into account, we can assume that the customer of websites providing and reviewing medical and pharmaceutical services and products is also becoming a victim of information overload. In this situation, artificial intelligence solutions become helpful. Thanks to modern solutions, an appropriate algorithm is able to perform Opinion Mining, that is, analyze a significant amount of texts, posts and opinions and then aggregate their content into a form suitable for the average person. The following article shows how novel methods of Machine Learning are able to perform analysis of texts in polish language related to the medical and pharmaceutical industries, and then extract key information on a given topic.
Attention-Based Feature Interaction Deep Factorization Machine for CTR Prediction

Click-Through Rate (CTR) prediction is widely used in many fields, such as web search, recommender systems, etc. Recently, the CTR prediction model using deep learning and attention mechanism technology has achieved remarkable success. However, there are some problems with previous work: (1) Most of the previous works usually consider the significance of features statically, which is not conducive to building personalized recommender systems. (2) Most of the previous works usually used shallow interaction, which was not enough to fully fit each feature to produce better prediction performance. In this paper, a novel model named Attention-based Feature Interaction Deep Factorization Machine (AFI-DeepFM) is proposed for learning more useful features and more comprehensive and rich feature interactions. Specifically, the AFI-DeepFM leverages the convolutional squeeze network to extract the feature information to better dynamically learn more useful features. Besides, the model can also effectively capture the interaction between features using the Kronecker product method. Extensive experiments conducted on the two public datasets demonstrate that the AFI-DeepFM is productive and outperforms the baseline models.
Graph Federated Learning Based on the Decentralized Framework

Graph learning has a wide range of applications in many scenarios, which require more need for data privacy. Federated learning is an emerging distributed machine learning approach that leverages data from individual devices or data centers to improve the accuracy and generalization of the model, while also protecting the privacy of user data. Graph-federated learning is mainly based on the classical federated learning framework i.e., the Client-Server framework. However, the Client-Server framework faces problems such as a single point of failure of the central server and poor scalability of network topology. First, we introduce the decentralized framework to graph-federated learning. Second, determine the confidence among nodes based on the similarity of data among nodes, subsequently, the gradient information is then aggregated by linear weighting based on confidence. Finally, the proposed method is compared with FedAvg, Fedprox, GCFL, and GCFL+ to verify the effectiveness of the proposed method. Experiments demonstrate that the proposed method outperforms other methods.
Interaction of Generalization and Out-of-Distribution Detection Capabilities in Deep Neural Networks

Current supervised deep learning models are shown to achieve exceptional performance when data samples used in evaluation come from a known source, but are susceptible to performance degradations when the data distribution is even slightly shifted. In this work, we study the interaction of two related aspects in this context: (1) out-of-distribution (OOD) generalization ability of DNNs to successfully classify samples from unobserved data distributions, and (2) being able to detect strictly OOD samples when observed at test-time, finding that acquisition of these two capabilities can be at odds. We experimentally analyze the impact of various training data related texture and shape biases on both abilities. Importantly, we reveal that naive outlier exposure mechanisms can help to improve OOD detection performance while introducing strong texture biases that conflict with the generalization abilities of the networks. We further explore the influence of such conflicting texture bias backdoors, which lead to unreliable OOD detection performance on spurious OOD samples observed at test-time.
Siamese Network Based on MLP and Multi-head Cross Attention for Visual Object Tracking

Visual object tracking is an important prerequisite in many applications. However, the performance of the tracking system is often affected by the quality of the visual object’s feature representation and whether it can identify the best match of the target template in the search area. To alleviate these challenges, we propose a new method based on Multi-Layer Perceptron (MLP) and multi-head cross attention. First, a new MLP-based module is designed to enhance the input features, by refining the internal association between the spatial and channel dimensions of these features. Second, an improved head network is constructed for predicting the location of the target, in which the multi-head cross attention mechanism is used to find the optimal matching between the template and the search area. Experiments on four datasets show that the proposed method offers competitive tracking performance as compared with several recent baseline methods. The codes will be available at https://github.com/SYLan2019/MLP-MHCA .
OTSummarizer an Optimal Transport Based Approach for Extractive Text Summarization

Automatic text summarization (ATS) consists of automatically generating a coherent and concise summary of the original document. It is a fundamental task in Natural Language Processing (NLP) with various applications, including news aggregation and social media analysis. The most recent approaches are based on transformer architecture, such as BERT and its different descendants. However, these promising approaches face input length limitations, such as 512 tokens for the BERT-base model. To alleviate these issues, we propose an Optimal Transport (OT) based approach for ATS called OTSummarizer. It represents a sentence by a distribution over words and then applies an OT solver to get similarities between the original document and a candidate summary. We design a Beam Search (BS) strategy to efficiently explore the summary search space and get the optimal summary. We develop theoretical results to justify the use of OT in ATS. Empirically, we evaluate the model on the CNN Daily Mail and PubMed datasets, ensuring a ROUGE score of 41.66%. The experimental results show that the OTSummarizer performs better than previous extractive summarization state-of-the-art approaches in terms of ROUGE-1, ROUGE-2 and ROUGE-L scores.
Active Learning with AraGPT2 for Arabic Named Entity Recognition

Arabic Named Entity Recognition (ANER) is a crucial task in natural language processing that aims to identify and classify named entities in text into predefined categories such as person, location, and organization. In this study, we propose an active learning approach for Arabic Named Entity Recognition (ANER) using the pre-trained language model AraGPT2. Our approach utilizes the model’s uncertainty in making predictions to select the most informative examples for annotation, reducing the need for a large annotated dataset. We evaluated our approach on three datasets: AQMAR, NEWS, and TWEETS. The results demonstrate that our active learning approach outperforms state-of-the-art models and tools such as MADAMIRA, FARASA, and Deep Co-learning methods on AQMAR and NEWS datasets. Additionally, our approach demonstrates robustness on TWEETS dataset, which primarily contains text written in the Egyptian dialect and often includes mistakes or misspellings. Our findings suggest that the active learning approach can significantly improve the performance of ANER models, particularly when dealing with noisy or dialectal text.
Multi-Timestep-Ahead Prediction with Mixture of Experts for Embodied Question Answering

In this study, we propose a method that integrates visual field predictions with different time scales and investigates its effectiveness for embodied question answering (EQA). In EQA, it is desirable to be able to automatically select a prediction time scale according to the situation, as the path to the target object depends on the instructions provided. However, previous studies have only investigated subtask learning with a limited prediction timescale and target. We propose a mixed expert model in which multiple expert networks predict future images at different time steps, and a higher-level gating network estimates the distribution of each expert’s output. By sequentially adjusting the output of the expert network, the proposed method enables robot navigation considering multi-timestep-ahead prediction. Comparison experiments on the EQA MP3D dataset show that the proposed method improves the prediction accuracy of the model regardless of the distance to the target.
Self-Supervised Graph Convolution for Video Moment Retrieval

Video Moment Retrieval is a task locating a moment from an untrimmed video that are relevant to a given query. It is a highly challenging multi-modal task due to biased annotations and complex cross-model interaction. In this paper, we propose Self-Supervised Graph Convolution Network (SSGCN) for video moment retrieval. For biased annotations, we design a self-supervised auxiliary task to mine feature representation of inherit video and text information by randomly dropout moment-text relation. For complex cross-modal interaction, we use two Graph Convolutional Networks to obtain feature representations of both the video and text modalities. The feature representations of the two modalities are then used to acquire cross-modal information through cross-attention layers, which is treated as implicit graph matching edges to update the graph neural network. The effectiveness of the proposed model is validated through extensive experiments.
Cross Attention with Deep Local Features for Few-Shot Image Classification

Traditional few-shot learning methods that rely on image-level features have been widely adopted, but they may not be effective in representing the local information of images. Recently, some methods have introduced deep local features that are semantically rich and achieved promising results. However, these methods typically take all local features into consideration, ignoring that some of them, such as sky and grass, are task-irrelevant and may affect the accuracy of image classification. In this thesis, we propose a novel Local Cross Attention Network (LCAN) that aims to learn the query local features that are most relevant to each task. Specifically, we designed a local cross attention mechanism composed of two modules: a query local attention module and a class relevant module. The former is used to determine what kind of query local features to attend by using the spatial and channel information in the query feature, while the latter utilizes the local relationship between the query feature and the support feature to determine which query local features to attend. Extensive experimental on three widely used few-shot classification benchmarks (miniImageNet, tieredImageNet and CUB-200) demonstrate that our proposed method achieves state-of-the-art performance.
Graph Neural Network-Based Representation Learning for Medical Time Series

The ability to analyze and predict medical time series data is crucial for enhancing healthcare decision-making and improving patient outcomes. Currently, the algorithms used for classification and prediction of medical time series data are limited in their capabilities and may not be reliable enough to meet the demands of practical applications. The purpose of this paper is to promote the representation learning of complex data primarily comprised of medical time series, in order to facilitate various downstream tasks. Under the framework of graph neural networks (GNN), we present indegree regularized neural message passing to reflect the dependencies between different sequences. Our approach also leverages representation learning to convert multivariate time series (MTS) and static features into nodes of GNN. Moreover, we propose a dynamic loss function to encourage the consistent learning of sensor dependency graphs across models. Based on these proposals, our method can effectively capture not only the temporal dependencies among variables, but also the multidimensional dependencies among MTS and static features. We classify time series on two medical challenge and a human activity datasets. The results show that our approach can significantly improve downstream task performance across various metrics. Code is available at https://github.com/Zzzoptimus/GICG .
FreMPhone: A French Mobile Phone Corpus for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) task is one of the Natural Language Processing (NLP) research fields that has seen considerable scientific advancements over the last few years. This task aims to detect, in a given text, the sentiment of users towards the different aspects of a product or service. Despite the big number of annotated corpora that have been produced to perform the ABSA task in the English language, resources are still stingy, for other languages. Due to the lack of French corpora created for the ABSA task, we present in this paper the French corpus for the mobile phone domain “FreMPhone”. The constructed corpus consists of 5217 mobile phone reviews collected from the Amazon.fr website. Each review in the corpus was annotated with its appropriate aspect terms and sentiment polarity, using an annotation guideline. The FreMPhone contains 19257 aspect terms divided into 13259 positives, 5084 negatives, and 914 neutrals. Moreover, we proposed a new architecture “CBCF” that combines the deep learning models (LSTM and CNN) and the machine learning model CRF and we evaluated it on the FreMPhone corpus. The experiments were performed on the subtasks of the ABSA: Aspect Extraction (AE) and Sentiment Classification (SC). These experiments showed the good performance of the CBCF architecture which overrode the LSTM, CNN, and CRF models and achieved an F-measure value equal to 95.96% for the Aspect Extraction (AE) task and 96.35% for the Sentiment Classification (SC) task.
Human-Related Security Threats and Countermeasures of Electronic Banking and Identity Services - Polish Case Study

The paper is a synthesis of findings on the financial sector’s cybersecurity analysis and e-identity management from the perspective of human-related vulnerabilities. Extensive desk research was conducted to gain a complete picture of the coexisting digital services and their relationship. Based on that, we defined challenges and synthesized our observations, identifying the most severe security risks associated with digital banking and identity services. Our study points out that human errors are the weakest link in the security chain. COVID-19 has accelerated the development of the digital revolution, thus forcing users to use digital services with greater commitment. The acceleration of digital transformation has not been indifferent to hackers, who have quickly adapted to the changing environment. Thus, the risk of exposing inexperienced users to cyberattacks has increased. Human-led attacks, based on naivety, emotion, and lack of relevant knowledge, have posed the most significant threat. Adversaries skillfully maneuver between the offline and online worlds by using fake IDs and sending fake emails. Only proactive countermeasures can stop attacks. Using our expertise, we identified best practices and defined recommendations for the issues discussed, which is our most significant contribution to this study.
Cross-Graph Transformer Network for Temporal Sentence Grounding

Temporal sentence grounding aims to retrieve moments associated with the given sentences in untrimmed videos, which is a multi-modal problem and needs the adequate understanding of the sentence and video structure as well as the accurate interaction of the two modals. In this paper, we propose a cross-graph Transformer network (CGTN) model to address this problem, where the sentence is taken as a dependency tree and the video as a graph, according to their non-linear structures. Based on the graph structures, we design the self-graph attention and cross-graph attention to model the relationship between the nodes in the graph and cross the graphs. We test the proposed model on two challenging datasets. Extensive experiments demonstrate the strength of our method.