Reviewerly/exHarmony huggingface dataset:
Revisiting Embedding-based Entity Alignment: A Robust and Adaptive Method

Entity alignment&#x2014;the discovery of identical entities across different knowledge graphs (KGs)&#x2014;is a critical task in data fusion. In this paper, we revisit existing entity alignment methods in practical and challenging scenarios. Our empirical studies show that current work has a low level of robustness to long-tail entities and the lack of entity names or relation triples. We aim to develop a robust and adaptive entity alignment method, and the availability of relations, attributes, or names is not required. Our method consists of an attribute encoder and a relation encoder, representing an entity by aggregating its attributes or relational neighbors using the attention mechanisms that can highlight the useful attributes and relations in end-to-end learning. To let the encoders complement each other and produce a coherent representation space, we propose adaptive embedding fusion via a gating mechanism. We consider four evaluation settings, i.e., the conventional setting with both relation and attribute triples, as well as three challenging settings without attributes, without relations, without both relations and names, respectively. Results show that our method can achieve state-of-the-art performance. Even in the most challenging setting without relations and names, our method can still achieve promising results while existing methods fail.
Ice hockey player identification via transformers and weakly supervised learning

Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition. Transformers are the existing standard in natural language processing (NLP) and are swiftly gaining traction in computer vision. Motivated by the increasing success of transformers in computer vision, we introduce a transformer network for recognizing players through their jersey numbers in broadcast National Hockey League (NHL) videos. The transformer takes temporal sequences of player frames (called player tracklets) as input and outputs the probabilities of jersey numbers present in the frames. The proposed network performs better than the previous benchmark on the same dataset. We implement a weakly-supervised training approach by generating approximate frame-level labels for jersey number presence and use the frame-level labels for faster training. We also utilize player shifts available in the NHL play-by-play data by reading the game time using optical character recognition (OCR) to get the players on the ice rink at a certain game time. Using player-shifts improved the player identification accuracy by 6%.
A New Dataset and Transformer for Stereoscopic Video Super-Resolution

Stereo video super-resolution (SVSR) aims to enhance the spatial resolution of the low-resolution video by reconstructing the high-resolution video. The key challenges in SVSR are preserving the stereo-consistency and temporal-consistency, without which viewers may experience 3D fatigue. There are several notable works on stereoscopic image super-resolution, but there is little research on stereo video super-resolution. In this paper, we propose a novel Transformer-based model for SVSR, namely Trans-SVSR. Trans-SVSR comprises two key novel components: a spatio-temporal convolutional self-attention layer and an optical flow-based feed-forward layer that discovers the correlation across different video frames and aligns the features. The parallax attention mechanism (PAM) that uses the cross-view information to consider the significant disparities is used to fuse the stereo views. Due to the lack of a benchmark dataset suitable for the SVSR task, we collected a new stereoscopic video dataset, SVSR-Set, containing 71 full high-definition (HD) stereo videos captured using a professional stereo camera. Extensive experiments on the collected dataset, along with two other datasets, demonstrate that the Trans-SVSR can achieve competitive performance compared to the state-of-the-art methods. Project code and additional results are available at https://github.com/H-deep/Trans-SVSR/.
Semantic Segmentation for Thermal Images: A Comparative Survey

Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy of pixel-level classification can be affected by many factors, such as imaging limitations and the ambiguity of object boundaries in an image. Conventional methods exploit three-channel RGB images captured in the visible spectrum with deep neural networks (DNN). Thermal images can significantly contribute during the segmentation since thermal imaging cameras are capable of capturing details despite the weather and illumination conditions. Using infrared spectrum in semantic segmentation has many real-world use cases, such as autonomous driving, medical imaging, agriculture, defense industry, etc. Due to this wide range of use cases, designing accurate semantic segmentation algorithms with the help of infrared spectrum is an important challenge. One approach is to use both visible and infrared spectrum images as inputs. These methods can accomplish higher accuracy due to enriched input information, with the cost of extra effort for the alignment and processing of multiple inputs. Another approach is to use only thermal images, enabling less hardware cost for smaller use cases. Even though there are multiple surveys on semantic segmentation methods, the literature lacks a comprehensive survey centered explicitly around semantic segmentation using infrared spectrum. This work aims to fill this gap by presenting algorithms in the literature and categorizing them by their input images.
Selective label enhancement for multi-label classification based on three-way decisions

Multi-label classification is a challenging issue in the data science community due to the ambiguity of label semantics. Existing studies mainly focus on improving label association with logical labels, but the performance suffers from the threshold setting. Although label distribution learning gains superior discrimination, the expenditure of collecting large-scale fine-grained numerical labels is intolerable. To address the uncertainty of logical label semantics, we propose a novel model called three-way decisions with label enhancement (3WDLE). For unseen instances, we implement a trisecting-acting-outcome framework. In the trisecting stage, an uncertainty measure called global uncertain-prone degree partitions these instances into uncertain and certain regions, where the trisecting procedure is completed from label level to instance level by leveraging the distributions of pseudo-label information. In the acting stage, instances recognized as certain regions directly take the results generated by label-specific learning, whereas the remaining are reclassified by conducting selective label enhancement. The enriched knowledge generated by the label enhancement module is learnt on trustworthy instances only. In the outcome stage, we adopt five evaluation metrics to evaluate the classification performance from the perspectives of both labels and instances. In this way, three-way decisions provide a systematic methodology to deal with uncertainty in multi-label classification, which combines logical label learning with numerical label learning into a unified framework to optimize the performance of the multi-label classification model. Extensive experiments demonstrate the superiority of 3WDLE over state-of-the-art multi-label classifications with logical labels only.
Explainability and Transparency of Classifiers for Air-Handling Unit Faults Using Explainable Artificial Intelligence (XAI)

In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU) faults classifier using a modified XAI technique, such that non-AI expert end-users who require justification for the diagnosis output can easily understand the reasoning behind the decision. The method operates as follows: First, an XGBoost algorithm is used to detect and classify potential faults in the heating and cooling coil valves, sensors, and the heat recovery of an air-handling unit. Second, an XAI-based SHAP technique is used to provide explanations, with a focus on the end-users, who are HVAC engineers. Then, relevant features are chosen based on user-selected feature sets and features with high attribution scores. Finally, a sliding window system is used to visualize the short history of these relevant features and provide explanations for the diagnosed faults in the observed time period. This study aimed to provide information not only about what occurs at the time of fault appearance, but also about how the fault occurred. Finally, the resulting explanations are evaluated by seven HVAC expert engineers. The proposed approach is validated using real data collected from a shopping mall.
DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

A significant aspect of today's digital information is attributed networks, which combine multiple node attributes with the basic network topology in order to extract knowledge.Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in a number of high-impact areas.The majority of current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method.These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources.Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues.However, they suffer from the problem of ignoring the latent codes' embedding distribution, which results in poor representation in many instances.In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks.First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, similar to vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach.An adversarial mechanism makes the encoder make more accurate estimates of how potential features might be distributed.As a result, decoders can make graphs that are more like the original graph.Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm.Lastly, the Gaussian Mixture Model, a distinct estimation network, is used to approximate the latent vector density, resulting in the detection of anomalies from measuring sample energy.They are trained jointly as an end-to-end framework.DVAEGMM helps in the simultaneous optimization of the mixture model, generative adversarial network, and variational autoencoder parameters.The joint optimization balances the reconstruction probability, the latent representation density approximation, and regularization.Extensive experiments on attributed networks prove that DVAEGMM significantly beats the existing methods, proving the efficiency of the presented approach.The AUC scores of our proposed framework for the BlogCatalog, Flickr, Enron, and Amazon datasets are 0.89380, 0.87130, 0.72480, and 0.75102, respectively.
FW-GAN: Underwater image enhancement using generative adversarial network with multi-scale fusion

Underwater robots have broad applications in many fields such as ocean exploration, ocean pasture and environmental monitoring. However, due to the inference of light scattering and absorption, selective color attenuation, suspended particles and other complex factors in the underwater environment, it is difficult for robot vision sensors to obtain high-quality underwater image signal, which is the bottleneck problem that restricts the visual perception of underwater robots. In this paper, we propose a multi-scale fusion generative adversarial network named Fusion Water-GAN (FW-GAN) to enhance the underwater image quality. The proposed model has four convolution branches, these branches refine the features of the three prior inputs and encode the original input, then fuse prior features using the proposed multi-scale fusion connections, and finally use the channel attention decoder to generate satisfactory enhanced results. We conduct qualitative and quantitative comparison experiments on real-world and synthetic distorted underwater image datasets under various degradation conditions. The results show that compared with the recent state-of-the-art underwater image enhancement methods, our proposed method achieves higher quantitative metrics scores and better generalization capability. In addition, the ablation study demonstrated the contribution of each component. • We propose a multi-scale fusion generator network architecture. Based on the analysis of the underwater environment, an adaptive fusion strategy is proposed to fuse the multi-source and multi-scale features, which can effectively correct the color casts and haze of the image and improve its contrast, it can also avoid blind enhancement of the image and improve the generalization capability of the model. • We propose a decoder model combined with channel attention to compute the attention of the prior and decoded feature maps in the fusion process and adjust them adaptively. The aim is to learn the potential associations between the priori features of fusion and the enhanced results. • We conducted qualitative and quantitative evaluations and compared FW-GAN with traditional methods and state-of-the-art models. The results show that FW-GAN has good generalization capability and competitive performance. Finally, we conduct an ablation study to demonstrate the contribution of each core component in our network.
A Discriminative Level Set Method with Deep Supervision for Breast Tumor Segmentation

Breast tumor segmentation in B-mode ultrasound imaging is important for analyzing, identifying, and diagnosing tumors. The level set is an approach most widely used in breast segmentation, and the refinement is still in progress. However, its effectiveness is harmed by a dearth of semantic information. On the other hand, deep networks contain rich semantic information but loss much influential low-level details.This paper proposes a novel deep-feature embedded level set group to exploit semantically enriched features for breast tumor segmentation. First, a UNet-based network is trained to extract different features at different stages. Each stage has unique features depiction. Then, a novel level-set method is integrated at the end of each stage to approach more accurate and precise features maps. A new feature-discriminator is devised in the energy function of the level set method to refine the low confidence pixels at the boundaries. Lastly, the outputs of the level set method at different stages are incorporated into final feature maps to further empower the segmentation process. Two datasets comprising 349 breast ultrasound images from various hospitals have been utilized to assess the proposed approach's performance. The model's effectiveness is estimated on different metrics, including Accuracy, Sensitivity or True Positive rate, Specificity or True Negative rate, False Positive rate Dice, and IoU values for both datasets. Furthermore, the efficiency of the model is investigated by performing a comparison with several state-of-the-art classic segmentation methods and deep learning methods.The proposed method outperformed segmenting breast ultrasound tumors in terms of Dice and IoU for datasets A and B (with p-value < 0.005 against compared methods). Additionally, the performance of the proposed approach is evaluated using the Area Under Receiver Operating Characteristics curve (AUC) and Mean Absolute Error (MAE). Our findings indicate that the proposed method seems to gain superiority over other methods by obtaining a lower MAE rate with the highest value of the AUC.Experiments determine that our method has obtained the best cut-off to deal with the noticeable glitches present in other approaches and generates more accurate segmentation results for tumors in complex images. Hence, the results confirm the proposed method's effectiveness compared to classic segmentation methods over ultrasound images with blurry boundaries, noise, and intensity inhomogeneity. Moreover, our approach gives unprecedented prediction accuracy and similarity for malignant tumors.
Design of English teaching system using Artificial Intelligence

The process of English teaching is significantly affected by various technological advancements like the Internet of Things Video (IoTV). We start with the analysis of the actual teaching process of English and the direction of student feature recognition. We explore the IoTV reform to use the interpolation method for the student image-based feature recognition algorithm . Accordingly, we discover an intelligent algorithm for student feature recognition to improve the effectiveness of the IoTV. The intelligence algorithm for student feature recognition is designed using the Artificial Intelligence (AI) method. The proposed model combines voice recognition and text-to-speech technology to create functional modules of an English teaching system that change the traditional teaching mode and adjust teaching strategies in real-time based on student status based on feature recognition. Finally, this study demonstrates the practical effect of AI on the IoTV system through student feature recognition and classroom instruction assessment. Experiment outcomes reveal the efficiency of the AI-based IoTV system.
Multi Chaotic System to Generate Novel S-Box for Image Encryption

Abstract A novel method on the basis of multi chaos theory is suggested in the presented study. Also, the study used two different dimensions to generate S-Box to get a strong cipher that is difficult to break. The suggested image cryptosystem includes an identical (decryption and encryption) process, which involves a single keystream generator, shifting process (based on 3D Lorenz map) related diffusion operations, and generate S-Box (based on 2D Henon map) that related confusion operation. The comparative analysis and the simulate test show that the suggested image cryptosystem has a few properties, like high-sensitivity, fast encryption/decryption, large keyspace, excellent statistical properties related to the ciphertext, and so on. The suggested cryptosystem is considered as an alternative for practical secure communications.
Approaches for finding Optimal Number of Clusters using K-Means and Agglomerative Hierarchical Clustering Techniques

Machine learning and pattern recognition both benefit greatly from the study of clustering techniques. Choosing the right number of clusters in cluster analysis is quite tough. Quality of the cluster depends on Optimal number clusters. In this paper, we used three methods such as elbow method, gap statistics method and Silhouette method to find the optimal number of clusters. Furthermore, the agglomerative hierarchical clustering (AHC) algorithm and K-means methods has used for calculating the appropriate number of quality clusters for the data set with the optimal k-value. Both algorithms are evaluated on a given data set using the validation measures such as connectivity, Dunn, and Silhouette to find the optimal number of clusters.
Persuasive Schemes for Financial Exploitation in Online Romance Scam: An Anatomy on <i>Sha Zhu Pan</i> (杀猪盘) <i>in</i> China

Sha Zhu Pan (杀猪盘, Pig-Butchering scam) is a new online romance scam targeting mainly Chinese-speaking individuals. While a few studies in Chinese have paid attention to Sha Zhu Pan, no study in English considers the role of scammers’ persuasive techniques in shaping the progress of fraudulent discourse. This study attempts to address this gap by analyzing the rhetorical moves of the Sha Zhu Pan scammers through using data from both victims and offenders. Victims’ testimonial data were collected from Zhihu, a prominent Chinese online platform for posting questions and answers and users’ personal experiences. This study also uses four complete police reports from law enforcement in China to provide objective evidences from offenders’ perspective in a natural setting. This study advances the model of criminal persuasion for Sha Zhu Pan developed from Gardner’s original persuasive framework. Such a model points out the type of persuasive techniques that the Sha Zhu Pan scammers frequently use during different stages of the scam. The results reflect a preliminary understanding of scammers’ behaviors, showing that the language of this financially and emotionally devastated crime indeed follows a premeditated arrangement. Moreover, such evidences can also support the proposal of related theoretical and policy implications for researchers, the government in China and other countries.
Deep learning-based aerial image classification model using inception with residual network and multilayer perceptron

At present times, unmanned aerial vehicles (UAVs) have become popular owing to the advantages such as scalable, versatile, autonomous, and inexpensive. The aerial image classification process has attracted more attention among the UAV-enabled surveillance system facilitating objective recognition, tracking process and considerably improving the visual surveillance results. This paper introduces effective deep learning-based aerial image classification using Inception with Residual Network v2 and multilayer perceptron (DLIRV2-MLP). The presented DLIRV2-MLP method includes a series of operations such as pre-processing, feature extraction, and classification. Primarily, the UAVs are used for the collection of aerial images by the use of imaging sensors exist in it. Followed by, the Inception with ResNet v2 based feature extractor is utilized to derive a useful set of feature vectors from the aerial image. Finally, the classification of aerial images using the derived feature vectors takes place by the use of MLP model. Here, the MLP classifier is used as the final layer of the Inception with ResNet v2 model in place of the softmax layer to improve the classification performance. The efficacy of the DLIRV2-MLP method is validated using a benchmark aerial image dataset and the resultant experimental values highlighted the effectiveness of the DLIRV2-MLP method with 90% and above in accuracy and precision. The problem of existing method faces some issues in computation whereas the aerial image classification with DLIRV2-MLP gives efficient computation while comparing with the existing method.