Reviewerly/exHarmony huggingface dataset:
Hierarchical Decoding Network Based on Swin Transformer for Detecting Salient Objects in RGB-T Images

Although conventional deep convolutional neural networks are effective for contextual semantic segmentation of objects, recent vision transformers can capture global information of an image and are better at capturing semantic associations over longer ranges. In addition, some existing saliency detection methods disregard the guidance of high-level semantic information for low-level features during decoding, and only use layer-by-layer transmission for encoding. Therefore, we propose a hierarchical decoding network based on a swin transformer to perform red&#x2013;green&#x2013;blue and thermal (RGB-T) salient object detection (SOD). First, a sine&#x2013;cosine fusion module performs multimodality intersections and exploits complementarity. As a second fusion stage, an advanced semantic information guidance module adjusts high-level semantic information and low-level detailed characteristics. Finally, a global saliency perception module fuses cross-layer information in a top-down path. Comprehensive experiments demonstrate that the proposed network outperforms 12 state-of-the-art methods on three RGB-T SOD datasets.
The Use of Artificial Intelligence and Audit Quality: An Analysis from the Perspectives of External Auditors in the UAE

This paper aims to explore external auditors’ perception of the use of artificial intelligence (AI) in the United Arab Emirates (UAE). It investigates whether there is a perception among external auditors toward the contribution of AI to audit quality. It also aims to test whether the perception of AI usage and its impact on audit quality differs between local and international external auditors. Data were collected using an online survey from 22 local and 41 international audit firms to achieve these research objectives. Participants were either the auditing manager, audit partners, senior auditors or other personnel who may have experience in the field of accounting and auditing. To test our hypotheses, data analysis was undertaken using reliability and validity tests, descriptive analysis and independent samples t-test. We found that the analysis shows that there is a non-significant difference in the perceived contribution of AI to audit quality between local and international audit firms. All the audit firms, whether local or international, have equal perceived contributions with regard to the audit quality.
Completely quantum neural networks

Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a state-of-the-art quantum annealer, we develop three crucial ingredients: binary encoding the free parameters of the network, polynomial approximation of the activation function, and reduction of binary higher-order polynomials into quadratic ones. Together, these ideas allow encoding the loss function as an Ising model Hamiltonian. The quantum annealer then trains the network by finding the ground state. We implement this for an elementary network and illustrate the advantages of quantum training: its consistency in finding the global minimum of the loss function and the fact that the network training converges in a single annealing step, which leads to short training times while maintaining a high classification performance. Our approach opens a novel avenue for the quantum training of general machine learning models.
An Efficient Method for Generating Synthetic Data for Low-Resource Machine Translation

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Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection

Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust and explainable unsupervised auto encoder frameworks that decompose an input time series into a clean time series and an outlier time series using autoencoders. Improved explainability is achieved because clean time series are better explained with easy-to-understand patterns such as trends and periodicities. We provide insight into this by means of a post-hoc explainability analysis and empirical studies. In addition, since outliers are separated from clean time series iteratively, our approach offers improved robustness to outliers, which in turn improves accuracy. We evaluate our approach on five real-world datasets and report improvements over the state-of-the-art approaches in terms of robustness and explainability.
Deep Feature Correlation Learning for Multi-Modal Remote Sensing Image Registration

Deep descriptors have advantages over handcrafted descriptors on local image patch matching. However, due to the complex imaging mechanism of remote sensing images and the significant differences in appearance between multi-modal images, existing deep learning descriptors are unsuitable for multi-modal remote sensing image registration directly. To solve this problem, this paper proposes a deep feature correlation learning network (Cnet) for multi-modal remote sensing image registration. Firstly, Cnet builds a feature learning network based on the deep convolutional network with the attention learning module, to enhance the feature representation by focusing on meaningful features. Secondly, this paper designs a novel feature correlation loss function for Cnet optimization. It focuses on the relative feature correlation between matching and non-matching samples, which can improve the stability of network training and decrease the risk of overfitting. Additionally, the proposed feature correlation loss with a scale factor can further enhance the network training and accelerate the network convergence. Extensive experimental results on image patch matching (Brown, HPatches), cross-spectral image registration (VIS-NIR), multi-modal remote sensing image registration, and single-modal remote sensing image registration have demonstrated the effectiveness and robustness of the proposed method.
NFTs in Healthcare: Vision, Opportunities, and Challenges

Nonfungible tokens (NFTs) have emerged as one of the most popular digital assets in recent years. They represent ownership of unique items that are stored on a blockchain and cannot be changed, replaced, or copied. Digital exchanges sell and trade NFT-minted assets that can only have one official owner at a time. NFTs are entering all sectors, from art to real estate, and healthcare is the next sector. In this article, we envision using NFTs in healthcare. We present the main components along with key features of NFTs and their benefits. We discuss the potential opportunities brought about by NFTs in different healthcare areas, such as supply chain management, patient-centric data management, digital twins, clinical trial management, and genomics. We discuss some important challenges hindering the adoption of NFTs in healthcare, serving as future research directions.
Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook

Abstract Benchmark datasets and benchmark problems have been a key aspect for the success of modern machine learning applications in many scientific domains. Consequently, an active discussion about benchmarks for applications of machine learning has also started in the atmospheric sciences. Such benchmarks allow for the comparison of machine learning tools and approaches in a quantitative way and enable a separation of concerns for domain and machine learning scientists. However, a clear definition of benchmark datasets for weather and climate applications is missing with the result that many domain scientists are confused. In this paper, we equip the domain of atmospheric sciences with a recipe for how to build proper benchmark datasets, a (nonexclusive) list of domain-specific challenges for machine learning is presented, and it is elaborated where and what benchmark datasets will be needed to tackle these challenges. We hope that the creation of benchmark datasets will help the machine learning efforts in atmospheric sciences to be more coherent, and, at the same time, target the efforts of machine learning scientists and experts of high-performance computing to the most imminent challenges in atmospheric sciences. We focus on benchmarks for atmospheric sciences (weather, climate, and air-quality applications). However, many aspects of this paper will also hold for other aspects of the Earth system sciences or are at least transferable. Significance Statement Machine learning is the study of computer algorithms that learn automatically from data. Atmospheric sciences have started to explore sophisticated machine learning techniques and the community is making rapid progress on the uptake of new methods for a large number of application areas. This paper provides a clear definition of so-called benchmark datasets for weather and climate applications that help to share data and machine learning solutions between research groups to reduce time spent in data processing, to generate synergies between groups, and to make tool developments more targeted and comparable. Furthermore, a list of benchmark datasets that will be needed to tackle important challenges for the use of machine learning in atmospheric sciences is provided.
Using intelligent multi-objective optimization and artificial neural networking to achieve maximum solar radiation with minimum volume in the archetype urban block

Nowadays, with the rapid development of cities, the demand for efficient and renewable energy, particularly solar radiation, is growing. Thus, urban planners and designers should employ solar energy analysis as a requirement to propose efficient solutions. This research aims to determine the maximum annual solar radiation with the least volume in various patterns of urban blocks and convert solar radiation modeling and analysis from simulation to emulation for types of urban blocks. As a result, simulation approaches such as Multi-Objective Optimization and Artificial Neural Networking were presented to characterize the influence of different urban scales on solar access and reach the optimal dimensions of the block. In Kermanshah, different patterns of urban blocks were investigated for the study on the urban block scale. The height, direction, number of plots, and scales impact access solar radiation energy and optimize the block’s dimensions. A study of 34 different urban blocks found that the ideal size is roughly 100∗100 m, while the best height is between 10 and 15 meters. Furthermore, the findings of predicting the data training models revealed that three outputs, the rate of solar radiation absorption, volume, and area of the urban block, can be predicted with an R2 score of 98% using the research input variables. Thus, radiation absorption estimations utilizing AI algorithms can ultimately replace traditional approaches and time-consuming large-scale energy estimates. The ANN methodology outperforms other machine learning techniques in predicting the objective function, which contains the amount of solar radiation absorption, volume, and surface area, according to the results of this research. Also, this research demonstrates how a simulation technique based on NSGA-II and ANN algorithms may drastically decrease computation time and costs while predicting optimum states prior to getting a real sample.
Explainable Artificial Intelligence: Evaluating the Objective and Subjective Impacts of xAI on Human-Agent Interaction

Intelligent agents must be able to communicate intentions and explain their decision-making processes to build trust, foster confidence, and improve human-agent team dynamics. Recognizing this need, academia and industry are rapidly proposing new ideas, methods, and frameworks to aid in the design of more explainable AI. Yet, there remains no standardized metric or experimental protocol for benchmarking new methods, leaving researchers to rely on their own intuition or ad hoc methods for assessing new concepts. In this work, we present the first comprehensive (n = 286) user study testing a wide range of approaches for explainable machine learning, including feature importance, probability scores, decision trees, counterfactual reasoning, natural language explanations, and case-based reasoning, as well as a baseline condition with no explanations. We provide the first large-scale empirical evidence of the effects of explainability on human-agent teaming. Our results will help to guide the future of explainability research by highlighting the benefits of counterfactual explanations and the shortcomings of confidence scores for explainability. We also propose a novel questionnaire to measure explainability with human participants, inspired by relevant prior work and correlated with human-agent teaming metrics.
Joint Knowledge Graph Completion and Question Answering

Knowledge graph reasoning plays a pivotal role in many real-world applications, such as network alignment, computational fact-checking, recommendation, and many more. Among these applications, knowledge graph completion (KGC) and multi-hop question answering over knowledge graph (Multi-hop KGQA) are two representative reasoning tasks. In the vast majority of the existing works, the two tasks are considered separately with different models or algorithms. However, we envision that KGC and Multi-hop KGQA are closely related to each other. Therefore, the two tasks will benefit from each other if they are approached adequately. In this work, we propose a neural model named BiNet to jointly handle KGC and multi-hop KGQA, and formulate it as a multi-task learning problem. Specifically, our proposed model leverages a shared embedding space and an answer scoring module, which allows the two tasks to automatically share latent features and learn the interactions between natural language question decoder and answer scoring module. Compared to the existing methods, the proposed BiNet model addresses both multi-hop KGQA and KGC tasks simultaneously with superior performance. Experiment results show that BiNet outperforms state-of-the-art methods on a wide range of KGQA and KGC benchmark datasets.
NeuroScaler

High-definition live streaming has experienced tremendous growth. However, the video quality of live video is often limited by the streamer's uplink bandwidth. Recently, neural-enhanced live streaming has shown great promise in enhancing the video quality by running neural super-resolution at the ingest server. Despite its benefit, it is too expensive to be deployed at scale. To overcome the limitation, we present NeuroScaler, a framework that delivers efficient and scalable neural enhancement for live streams. First, to accelerate end-to-end neural enhancement, we propose novel algorithms that significantly reduce the overhead of video super-resolution, encoding, and GPU context switching. Second, to maximize the overall quality gain, we devise a resource scheduler that considers the unique characteristics of the neural-enhancing workload. Our evaluation on a public cloud shows NeuroScaler reduces the overall cost by 22.3× and 3.0--11.1× compared to the latest per-frame and selective neural-enhancing systems, respectively.
An Improved YOLOv5s Algorithm for Object Detection with an Attention Mechanism

To improve the accuracy of the You Only Look Once v5s (YOLOv5s) algorithm for object detection, this paper proposes an improved YOLOv5s algorithm, CBAM-YOLOv5s, which introduces an attention mechanism. A convolutional block attention module (CBAM) is incorporated into the YOLOv5s backbone network to improve its feature extraction ability. Furthermore, the complete intersection-over-union (CIoU) loss is used as the object bounding-box regression loss function to accelerate the speed of the regression process. Experiments are carried out on the Pascal Visual Object Classes 2007 (VOC2007) dataset and the Microsoft Common Objects in Context (COCO2014) dataset, which are widely used for object detection evaluations. On the VOC2007 dataset, the experimental results show that compared with those of the original YOLOv5s algorithm, the precision, recall and mean average precision (mAP) of the CBAM-YOLOv5s algorithm are improved by 4.52%, 1.18% and 3.09%, respectively. On the COCO2014 dataset, compared with the original YOLOv5s algorithm, the precision, recall and mAP of the CBAM-YOLOv5s algorithm are increased by 2.21%, 0.88% and 1.39%, respectively.
Multimodal fusion methods with deep neural networks and meta-information for aggression detection in surveillance

Multimodal fusion has become one of the hottest topics in affective computing and other research areas. Yet, this topic is less studied in surveillance systems. In general, it focused only on a single modality mainly video or audio. Nevertheless, the fusion of multiple data from different sources and modalities is challenging especially for the detection of aggression because of its complexity. Indeed, aggression is difficult to be defined due to its ambiguity. Therefore, this high level concept is hard to be described through simple rules or trivial algorithms and classifiers. This article presents an approach based on four multimodal fusion methods using the audio, video and text modalities, as well as extra-information with Deep Learning techniques to detect aggression in surveillance. We investigate the combination of acoustic, visual and text-based features, as well as a set of five meta-features which have an influence on the fusion process and the aggression intensity. The proposed architectures use Deep Neural Networks (DNNs) although the dataset is relatively small. The neural network models show their effectiveness not only in prediction tasks, but also in feature extraction and dimension reduction. The first multimodal fusion method is based on an intermediate level that contains the predictions of audio, video and the five meta-features by implementing multiple DNNs. As our previous works on aggression detection based on an intermediate level and deep neural networks on a restricted subset of data also indicate the same findings, we extend these works by working on the whole dataset. The second fusion method uses the concatenation of the different features with the meta-features as labels without the intermediate level. The other two fusion methods are based on element-wise operations as a first step then on the concatenation as a second one. One method uses the element-wise product and the other one uses the element-wise addition. Among these proposed models, we find that the second method surpasses the performance of the other methods with an unweighted average accuracy of 85.66%, and weighted average accuracy of 86.35%, as well as the existing similar approach, for the detection of aggression, that provided accuracies of 75% and 77% for the unweighted average and weighted average. Overall, we think that each fusion method has its advantages and shortcomings for the prediction of different aggression intensities. Furthermore, we observe that meta-features are very informative and show significant improvements for the prediction performance of all the fusion methods.
Long-tailed visual recognition with deep models: A methodological survey and evaluation

In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed distribution, where only a few classes contain adequate samples but the others have (much) fewer samples. With the advancement of data-hungry deep models for visual recognition, the low-tail power-law data distribution that biases the model training has attracted significant attention. When training with the long-tailed data, the majority classes dominate the training procedure, resulting in poor performance in instance-scarce classes. To tackle this problem, numerous strategies, such as re-sampling, cost-sensitive loss, meta-learning and transfer learning, have been proposed. This paper systematically reviews contemporary approaches for the long-tailed visual recognition task and categorizes these methods based on the stage applied as training, fine-tuning, and inference. Furthermore, we categorize training stage methods into data augmentation, re-sampling strategy, cost-sensitive loss, as well as multiple experts and transfer learning. Next, comprehensive comparisons are made in the balanced test set performance of long-tailed benchmarks and method robustness in diverse test distributions using metrics including top-1 accuracy, per-class accuracy, multi-class ROC AUC and Expected Calibration Error (ECE). At last, we outline the challenges in this field and future research trends. Our reviews and intriguing findings can be a tutorial for researchers working in the field of open-world deep learning.
Taxonomy of Anomaly Detection Techniques in Crowd Scenes

With the widespread use of closed-circuit television (CCTV) surveillance systems in public areas, crowd anomaly detection has become an increasingly critical aspect of the intelligent video surveillance system. It requires workforce and continuous attention to decide on the captured event, which is hard to perform by individuals. The available literature on human action detection includes various approaches to detect abnormal crowd behavior, which is articulated as an outlier detection problem. This paper presents a detailed review of the recent development of anomaly detection methods from the perspectives of computer vision on different available datasets. A new taxonomic organization of existing works in crowd analysis and anomaly detection has been introduced. A summarization of existing reviews and datasets related to anomaly detection has been listed. It covers an overview of different crowd concepts, including mass gathering events analysis and challenges, types of anomalies, and surveillance systems. Additionally, research trends and future work prospects have been analyzed.
Virtual Stain Transfer in Histology via Cascaded Deep Neural Networks

Pathological diagnosis relies on the visual inspection of histologically stained thin tissue specimens, where different types of stains are applied to bring contrast to and highlight various desired histological features. However, the destructive histochemical staining procedures are usually irreversible, making it very difficult to obtain multiple stains on the same tissue section. Here, we demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN) to digitally transform hematoxylin and eosin (H&E) stained tissue images into other types of histological stains. Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E and then performs stain transfer from H&E to the domain of the other stain in a cascaded manner. This cascaded structure in the training phase allows the model to directly exploit histochemically stained image data on both H&E and the target special stain of interest. This advantage alleviates the challenge of paired data acquisition and improves the image quality and color accuracy of the virtual stain transfer from H&E to another stain. We validated the superior performance of this C-DNN approach using kidney needle core biopsy tissue sections and successfully transferred the H&E-stained tissue images into virtual PAS (periodic acid-Schiff) stain. This method provides high-quality virtual images of special stains using existing, histochemically stained slides and creates new opportunities in digital pathology by performing highly accurate stain-to-stain transformations.