Reviewerly/exHarmony huggingface dataset:
A Transformer-Based Method for UAV-View Geo-Localization

The geo-localization is the task of matching a query image depicting the ground-view of an unknown location with a group of satellite-view images with GPS tags. UAV-view can mitigate the large visual differences between images from different viewpoints. CNN-based approaches have achieved great success in cross-view geo-localization, but they rely on polar transform and have limited receptive field of convolution. Therefore, we investigate the geo-localization in terms of both feature representation and viewpoint transformation, design an end-to-end multitask jointly trained network model, introduce an efficient Transformer-based lightweight structure, use the strengths of transformer related to global information modeling and explicit position information encoding, and propose an end-to-end geo-localization architecture integrating a geo-localization module and a cross-view synthesis module, called Transformer-Based for UAV-View Geo-Localization (TUL). The geo-localization module combines feature segmentation and region alignment to achieve cross-view image matching. The cross-view synthesis module synthesizes maps close to real satellite images by conditional generative adversarial networks (cGAN). Image matching and synthesis are also considered, using the network to match images over two input domains so that the network is biased to learn potential feature representations that are useful for image synthesis. Furthermore, an image augmentation strategy is proposed for data enhancement to address the problem of sample imbalance due to the difference in the number of satellite images and images from other sources in University-1652 dataset. Experiments show that our method has significant performance improvement and reaches the state-of-the-art application level.
Reliable Framework for Digital Forensics in Medical Internet of Things

Medical Internet of Things (MIoT) involves the use of connected devices and sensors. These devices can include wearable health monitors, smart medical devices, implantable sensors, and remote monitoring systems. These devices can collect a wide range of data, including vital signs, medication adherence, and activity levels, which can be used to diagnose and treat medical conditions remotely and make informed decisions based on real-time information. With the increasing use of medical Internet of Things (MIoT) devices, such as wearable health monitors and implantable medical devices, digital forensics has become a critical component of healthcare security. Digital forensics for medical IoT involves the use of specialized tools and techniques to investigate any suspected security breaches or incidents involving these devices. The main goal of this paper is to provide a reliable blockchain-based digital forensic framework in MIoT.
A New Ant Population Based Improvement Heuristic for Solving Large Scale TSP

Solving large-scale Traveling Salesman Problems (TSPs) has become a new challenge for the Ant Colony Optimization metaheuristic (ACO). Indeed, traditional ACO algorithms suffer from problems of stagnation and premature convergence and their computational cost increases remarkably as the problem size does. In this paper, we propose a new algorithm inspired by the ACO metaheuristic for symmetric large-scale TSPs. The basic idea of this new algorithm, called Ant-IH, is to change the classic role played by ants in order to accelerate the convergence speed. In fact, in our approach artificial ants are not anymore used to construct solutions but to improve the quality of a population of solutions using a guided local search strategy. To validate the performance of Ant-IH, we conduct the experiments on various TSPLIB instances. The experimental results show that our proposed algorithm is more efficient for solving symmetric large-scale TSPs than the compared algorithms.
RaReSi: An Approach Combining Ratings and Reviews to Measure User Similarity in Neighbor-Based Recommender Systems

Neighbor-based recommender systems are highly valued for their interpretability. These systems focus on determining the similarity between users to find neighbor sets. In this paper, we combine observed ratings and reviews from users to calculate their similarity, named RaReSi. This combination is reflected not only in the calculation of user similarity but also in the transformation of the user representation space. Thus, our method effectively addresses the issue of sparse data, a typical challenge in the field of recommender systems. Experimental results on datasets Baby, Tools-Home Improvement, and Beauty show that our proposed method yields better RMSE results than rating-only, review-only, and other combined methods.
Enhancing the Interpretability of Deep Multi-agent Reinforcement Learning via Neural Logic Reasoning

Explaining the decision-making policies of deep reinforcement learning is challenging owing to the black-box nature of neural networks. We address this challenge by combining deep learning models and symbolic structures into a neural-logic model that reasons in the form of neural logic programming. The proposed explainable multi-agent reinforcement learning algorithm performs reasoning in a symbolic-represented environment using multi-hop reasoning, a relational path-searching method that uses prior symbolic knowledge. Furthermore, to alleviate the partial observability problem in multi-agent systems, we devised an explainable history module using an attention mechanism to incorporate past experiences while preserving interpretability. Experimental studies demonstrate that the proposed method can effectively learn close-to-optimal policies while generating expressive rules to explain the decisions. Particularly, it can learn more abstract concepts than conventional neural network approaches.
Empirical Study on the Effect of Residual Networks on the Expressiveness of Linear Regions

Residual networks have achieved success across various industries. Currently, the research on the working mechanism of residual networks mainly focuses on shallow sub-networks, while knowledge about many other aspects remains limited. Deep neural networks based on the ReLU (Rectified Linear Unit) activation function partition the input space into piecewise linear regions, and thus, for a residual network with ReLU activation, the number of linear regions can quantify its expressive power. In this paper, we first visualize the linear regions of residual networks in two dimensions to understand how the number of linear regions evolves in residual networks. Moreover, we aim to compare the actual expressive power and input representation capabilities of residual networks by analyzing the number of linear regions in two-dimensional inputs between residual networks and non-residual networks. Our research findings indicate that, under consistent external parameters and conditions, residual networks generally exhibit stronger linear regions expression and input representation capabilities than non-residual networks in most cases.
Imbalanced Conditional Conv-Transformer for Mathematical Expression Recognition

Mathematical Expression Recognition (MER), which aims to convert images into corresponding LaTeX markup, has been a long-standing research topic. Previous methods employ the paradigm of dense computing in both encoder and decoder would suffer from slow convergence, limited performance, and design complexity of extra backbone network before encoder. To alleviate the above limitation, we propose a fast-converging end-to-end ImBalanced Conditional Conv-Transformer (IBCCT) architecture that combines a light encoder and a heavy decoder. Besides, we extend the traditional encoder-decoder framework by further learning a lightweight network to generate for each image a conditional token to inject global position information. Extensive experiments show that our IBCCT-Base model can achieve better performance with faster convergence speed and the parameter is reduced by 33% compared with the SOTA method. In particular, our IBCCT-Large model has achieved 94.04% and 93.2% in the Match metric, which is 1.34% and 4.12% higher than the SOTA method.
A New Approach for the Diagnosis of Children Personality Disorders Based on Semantic Analysis

Psychology is the scientific study of behavior and experience, of how humans and animals feel, think, learn and adapt to their environment. When psychology meets modern technology in computer science, it creates Psycho-informatics. In this paper, we propose an approach and its tool, for the benefits of children having psychological issues. This approach extracts the different traits from raw documentations of personality disorders based on semantic analysis. These traits are then used to build, automatically, a personalized test depending on the disorder(s) estimated by the psychiatrist. The responses of the child’s parent on this test are then analyzed to generate a report for the psychiatrist, which would be useful in the precise diagnosis of the child.
Anomaly-Based Insider Threat Detection via Hierarchical Information Fusion

Insider threats can cause serious damage to organizations and insider threat detection has received increasing attention from research and industries in recent years. Anomaly-based methods are one of the important approaches for insider threat detection. Existing anomaly-based methods usually detect anomalies in either the entire sample space or the individual user space. However, we argue that whether the behavior is anomalous depends on the corresponding contextual information and the context scope can have more granularities. Overall normal behavior may be anomalous within a specific department, while normal behavior within a department may be anomalous for a specific person. To this end, in this paper, we propose a novel insider threat detection method that explicitly models anomalies with hierarchical context scopes (i.e., organization, department, and person) and fuses them to compute anomaly scores. Comparisons with the unsupervised state-of-the-art approaches on the CMU CERT dataset demonstrate the effectiveness of the proposed method. Our method won the first prize in the CCF-BDCI competition.
NN-Denoising: A Low-Noise Distantly Supervised Document-Level Relation Extraction Scheme Using Natural Language Inference and Negative Sampling

The task of document-level relation extraction (DocRE) is crucial in the field of natural language processing, as it aims to extract semantic relations between entities in a given document to facilitate deeper comprehension. Previous methods have primarily focused on fully supervised learning for DocRE, which requires a large amount of human-annotated training data, making it a tedious and laborious task. Recently, more and more attention has been paid to the incomplete labeling problem in human-annotated data, and it is believed to be the bottleneck of model performance. To address this limitation and mitigate annotation costs, we propose a low-noise distant supervision scheme for DocRE, called NN-Denoising, that combines natural language inference (NLI) models and negative sampling to filter out noise in the training data. The NLI model serves as a pre-filter for denoising the distant supervision (DS) labels, while negative sampling is employed to overcome the false negative problem in the filtered data. Our experimental results on a large-scale DocRE benchmark demonstrate the superiority of the proposed approach over existing baselines in distant supervision learning. Specifically, NN-Denoising achieves an improvement of 15.83 F1 points and 10.34 F1 points compared to the ATLOP and SSR-PU models, respectively.
Using Masked Language Modeling to Enhance BERT-Based Aspect-Based Sentiment Analysis for Affective Token Prediction

Aspect-based sentiment analysis is a challenging yet critical task for recognizing emotions in text, with various applications in social media, commodity reviews, and movie comments. Many researchers are working on developing more powerful sentiment analysis models. Most existing models use the pre-trained language models based fine-tuning paradigm, which only utilizes the encoder parameters of pre-trained language models. However, this approach fails to effectively leverage the prior knowledge revealed in pre-trained language models. To address these issues, we propose a novel approach, Target Word Transferred Language Model for aspect-based sentiment analysis (WordTransABSA), which investigates the potential of the pre-training scheme of pre-trained language models. WordTransABSA is an encoder-decoder architecture built on top of the Masked Language Model of Bidirectional Encoder Representation from Transformers. During the training procedure, we reformulate the previous generic fine-tuning models as a “Masked Language Model” task, which follows the original BERT pre-training paradigm. WordTransABSA takes full advantage of the versatile linguistic knowledge of Pre-trained Language Model, resulting in competitive accuracy compared with recent baselines, especially in data-insufficient scenarios. We have made our code publicly available on GitHub ( https://github.com/albert-jin/WordTransABSA ).
CNN-BiLSTM Model for Arabic Dialect Identification

Dialectal Arabic is a term that covers Arabic dialects, resulting from linguistic interference between the Arabic language and local or neighboring languages. The automatic identification of dialects is a task that allows to recognize automatically the dialect used to write a text. This task is very important for several other NLP tasks, where a priory knowledge of the dialect of an input text can be useful for the rest of the process, such as sentiment analysis, machine translation and hate speech detection. The general objective of this paper is to propose a new approach for the identification of Arabic dialects in textual content. The first obstacle to the development of this task is the lack of resources and especially of balanced dataset. To address this issue and achieve our goal we started by building a balanced dataset by merging together and filtering 7 datasets. Then we proposed a new approach for Arabic dialect identification based on the combination of CNN with BiLSTM. To evaluate our model, we conducted a comparative study of several machine learning and deep learning models using a bunch of features. We considered these models as baseline and compared their results against the ones obtained by our model. The evaluation of our approach showed about 2% improvement in accuracy, compared to the best baseline models.
Self-adapted Positional Encoding in the Transformer Encoder for Named Entity Recognition

The task of named entity recognition (NER) is fundamental to natural language processing (NLP), as it forms the basis for various downstream applications such as question answering, text summarization, and machine translation. With the development of Transformer architecture, it has gained popularity in NLP due to its ability to model parallel distant contextual dependencies. Although positional encoding is crucial in transformer-based NER models for capturing the sequential feature of natural language and improving their accuracy in NER, most approach, which uses a fixed mathematical formula to assign a unique vector to each position, is a hard-coded encoding To address this issue, a self-adapted positional encoding module called self-adapter is proposed in a Transformer model. The proposed self-adapter incorporates two information fusers aimed at enhancing the embedding representational ability of the model. The first information fuser integrates information across different positions, enhancing the embedding representational ability for different ranges. The second information fuser integrates diverse dimensional information for one position, resulting in improved embedding representation. Besides, We modify the calculation of the attention score to enhance the utilization of the self-adapter. A mathematical analysis based on Fourier series is presented to demonstrate the effectiveness of the proposed method. This approach allows for dynamic positional encoding adjustment, facilitating adaptation to varying contextual inputs and more flexibility to capture word relationships. To evaluate the model, four NER datasets, including one English and three Chinese datasets, are used. The results show that the self-adapter substantially improves the Transformer's performance in the NER task.
Comparing Lamarckian and Baldwinian Approaches in Memetic Optimization

Memetic optimization (MO) unifies local and global search in non-monotonic, ’rugged’ search spaces. As an enhancement of genetic optimization, where global search is via crossover and local search through random mutation, MO employs advanced techniques for local search and its integration with the global counterpart. This paper compares two approaches to memetic optimization (MO) based on the Baldwinian and Lamarckian theories of evolution. These theories propose that individuals’ behaviors are enhanced not only through crossover and mutation but also via lifetime learning. The Baldwinian approach suggests that learned behaviors impact genotype-phenotype mappings, leading to changes in individuals’ fitness. In contrast, the Lamarckian approach posits that learned behaviors not only affect individual fitness but also transfer to offspring. Our study demonstrates that genetic algorithms outperform memetic algorithms in general unimodal and multimodal functions, whereas memetic algorithms utilizing the Baldwinian and Lamarckian approaches excel in fixed-dimension multimodal functions, uncovering more global minima and generating superior solutions. These findings, derived from extensive experiments on the CEC-BC-2017 test functions, provide valuable insights for algorithm selection and optimization in various problem domains.
MIPCE: Generating Multiple Patches Counterfactual-Changing Explanations for Time Series Classification

In the development of AI and deep neural networks (DNNs), a growing concern has emerged regarding not only accuracy, but explainability. The corresponding field of research, known as eXplainable AI (XAI), is important because interpreting the predictions of AI helps users make decisions in critical areas such as medicine. XAI has recently gained popularity particularly for counterfactual explanations from a psychological perspective. However, despite recent progress in XAI, few existing methods focus on explaining time series data. We therefore propose Multiple Patches Counterfactual-changing Explanations (MIPCE) for fully convolutional networks (FCNs), which focuses on subsequences of time series, showing the process of change to the counterfactual. First, MIPCE obtains subsequences from features appearing in the FCN, and divides the time series data into patches. Using GPLVM, it then generates the interpretable process of counterfactual change in each patch. We compared our method with other counterfactual methods in terms of proximity, plausibility, and substitutability. These quantitative results indicate that MIPCE outperforms existing methods. In addition, our user test shows that our explanations are useful in helping users understand the decision-making processes of DNNs.
Depthwise Convolution with Channel Mixer: Rethinking MLP in MetaFormer for Faster and More Accurate Vehicle Detection

Vehicle detection is an important task in intelligent traffic monitoring and autonomous driving. However, vehicle detection not only requires fast detection speed, but also presents a challenge due to detection of small targets under occlusion and illumination. Although some current approaches introduce lightweight networks to improve detection speed or Transformer to improve accuracy, they still struggle to achieve an excellent balance between speed and accuracy. To address these issues, this paper proposes a simple yet effective MetaFormerd-based vehicle detection model, which is constructed based on depthwise convolution with channel mixer (DWCM), using depthwise convolution as the token mixer and replacing the MLP residual block with a channel mixer (CM) module to boost feature reuse. The DWCM module enables enhanced feature extraction capabilities and can be applied to different detection models to improve vehicle detection performance. In extensive experiments, the proposed model not only achieves 38.0 $$\%$$ AP on MS COCO, but also achieves 64.2 $$\%$$ AP50 and 45.8 $$\%$$ AP on UA-DETRAC with 278FPS on a RTX 2080Ti, which is significantly faster and more accurate than the state-of-the-art YOLO detectors without bells and whistles. Furthermore, the proposed CM-based model is more promising than the MLP-based model for vehicle detection which shows the superior potential of CM structure.
Ground Truth Data Generator in Automotive Infrared Sensor Vision Problems Using a Minimum Set of Operations

In image vision we call a ground truth data generator any kind of software tool or algorithm that contributes in a semi or fully automatic way to the extraction of ground truth labels from a data set. The main purpose of such automation is to reduce as much as possible the manual effort of labeling a big number of frames. Above all, such a generator must be precise and avoid false positives because its results shall be used as training data for neural networks. In this paper we present a minimum set of operations required for fully automatic generation of labels from existing grayscale images in automotive image vision problems such as eye detection or traffic sign recognition. Multiple configurations based on these operations have been created to fit various desired features. We shifted the focus from algorithms development for ground truth data generation to understanding the particularities of an object or sign in the grayscale spectrum and defining correct configurations to detect them. We will present these configurations and the results obtained in the ground truth data generation process.
Multi-fusion Recurrent Network for Argument Pair Extraction

Argument Pair Extraction (APE) is an extension of argument mining that focuses on identifying argument pairs from two passages that have an intrinsic interaction, such as peer review and rebuttal. Existing studies have divided this task into separate subtasks for argument mining and sentence relation classification, but they overlook the connection between the two subtasks, leading to the accumulation of errors in argument pair extraction. To address this issue, we propose the Multi-fusion Cross-update Recurrent Network (MCRN), which includes two cross-updated units: an argument mining unit and a sentence pairing unit. Specifically, we cross-update the sentence representations of both units to learn the interaction between them, allowing the acquired sentence features to contain both argumentation and sentence relation information. We also designed a recurrent structure to iteratively learn these two units, which improves the utilization of pre-trained features. To evaluate the performance of the model, we conducted extensive experiments on benchmark datasets, which demonstrated that MCRN significantly improves the APE task.
Solving the Hydrophobic-Polar Model with Nested Monte Carlo Search

In this paper we present a new Monte Carlo Search (MCS) algorithm for finding the ground state energy of proteins in the Hydrophobic-Polar-model (HP model). We also compare it to other MCS algorithms not usually used on the HP model as well as to other approaches and provide an overview of the state of the art algorithms used on the HP model.