Reviewerly/exHarmony huggingface dataset:
Depth dynamic center difference convolutions for monocular 3D object detection

Accurate 3D information is essential in the fields of autonomous driving and mobile robotics. Monocular 3D object detection provides a more economical solution than traditional LiDAR-dependent methods. Due to the lack of depth cues, monocular 3D object detection is extremely challenging to efficiently detect objects in 3D space from a single image. To mitigate this issue, we first identify how the depth information on surrounding pixels provides additional support for depth map estimation in the 3D detection of driving scenes. Based on this observation, we design Depth Dynamic Center Difference Convolution (DDCDC), which introduces surrounding pixel cues in depth estimation and has different convolution kernels weights for each pixel of all examples. This module not only overcomes the limitations of conventional 2D convolution, but also highlights the differences in depth information between the target and the background, so more attention is paid to interesting objects. Finally, we design an end-to-end monocular 3D object detection network with proposed DDCDC convolution modules. As a demonstration of the effectiveness of our method, our module is validated on two datasets: KITTI and nuScenes. The DDCDC achieves the most significant improvement in a simple setup compared to existing methods. Our evaluation results for the KITTI split1/split2 set are 23.83/21.48,16.00/13.92,12.04/10.59 (based on easy, medium, and hard), while the results for the nuScenes test set are mAP = 0.364 and NDS = 0.434.
Reducing the CNOT Count for Clifford+T Circuits on NISQ Architectures

While mapping a quantum circuit to the physical layer one has to consider the numerous constraints imposed by the underlying hardware architecture. Connectivity of the physical qubits is one such constraint that restricts two-qubit operations, such as CNOT, to “connected” qubits. SWAP gates can be used to place the logical qubits on admissible physical qubits, but they entail a significant increase in CNOT-count. In this article, we consider the problem of reducing the CNOT-count in Clifford+T circuits on connectivity-constrained architectures, like noisy intermediate-scale quantum (NISQ) computing devices. We “slice” the circuit at the position of Hadamard gates and “build” the intermediate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\{\text {CNOT},{T}\}$ </tex-math></inline-formula> subcircuits using Steiner trees, significantly improving on previous methods. We compared the performance of our algorithms while mapping different benchmark and random circuits to some well-known architectures, such as 9-qubit square grid, 16-qubit square grid, Rigetti 16-qubit Aspen, 16-qubit IBM QX5, and 20-qubit IBM Tokyo. Our methods give less CNOT-count compared to Qiskit and TKET transpiler as well as using SWAP gates. Assuming most of the errors in an NISQ circuit implementation are due to CNOT errors, then our method would allow circuits with a few times more CNOT gates be reliably implemented than the previous methods would permit.
Target-aware U-Net with fuzzy skip connections for refined pancreas segmentation

Medical image segmentation is one of the important steps in the computer-aided diagnosis of pancreas diseases. Although some models have been proposed to deal with the task of automatic pancreas segmentation, it is still challenging due to the small size, variable shape and unclear boundary of pancreas. In this paper, we propose a target-aware U-Net (tU-Net) using fuzzy skip connection for pancreas segmentation. Through adding a fuzzy skip connection module into the U-Net architecture, the low-level features can be transformed into the high-level semantic features, which facilitates the segmentation of small and changeable targets of pancreas. Based on the fuzzy feature mapping, we also design a target attention mechanism consists of global average pooling and depthwise convolution. It makes the decoder of the network more sensitive to target features by increasing weights of important channels. The proposed method is evaluated on the NIH dataset of 82 CT volumes, and the pancreas Medical Segmentation Decathlon (MSD) challenge dataset of 281 CT volumes. The proposed model achieves better results comparing with other state-of-the-art models. • Propose a target-aware U-Net for refined pancreas segmentation. • Design the fuzzy skip connection to obtain high-level semantics from the feature map. • Add target attention mechanism through combing global average pooling and depthwise convolution operations. • Experimental results on pancreas NIH dataset of 82 CT volumes and pancreas Medical Segmentation Decathlon (MSD) challenge dataset of 281 CT volumes validate the effectiveness of the proposed method.
Triplet Contrastive Learning for Aspect Level Sentiment Classification

The domain of Aspect Level Sentiment Classification, in which the sentiment toward a given aspect is analyzed, attracts much attention in NLP. Recently, the state-of-the-art Aspect Level Sentiment Classification methods are devised by using the Graph Convolutional Networks to deal with both the semantics and the syntax of the sentence. Generally, the parsing of syntactic structure inevitably incorporates irrelevant information toward the aspect. Besides, the syntactic and semantic alignment and uniformity that contribute to the sentiment delivery is currently neglected during processing. In this work, a Triplet Contrastive Learning Network is developed to coordinate the syntactic information and the semantic information. To start with, the aspect-oriented sub-tree is constructed to replace the syntactic adjacency matrix. Further, a sentence-level contrastive learning scheme is proposed to highlight the features of sentiment words. Based on The Triple Contrastive Learning, the syntactic information and the semantic information are thoroughly interacted and coordinated whilst the global semantics and syntax can be exploited. Extensive experiments are performed on three benchmark datasets and achieve accuracies (BERT-based) of 87.40, 82.80, 77.55 on Rest14, Lap14, and Twitter datasets, which demonstrate that our approach achieves state-of-the-art results in Aspect Level Sentiment Classification task.
LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.
Applying Artificial Intelligence in Cryptocurrency Markets: A Survey

The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.
Emerging Research Directions of Deep Learning for Pathology Image Analysis

Digital pathology image analysis has become a new emerging research trend in the medical domain. AI methods have been shown their effectiveness on conventional vision applications. However, applying AI methods on pathology image analysis is far from easy. Many practical challenging issues arise including pathology image analysis under insufficient and inaccurate annotations, recognizing pathology images of different data distributions, and training AI models based on decentralized data sources. In this paper, we focus on discussing these challenging issues of AI approaches for pathology image analysis. A survey of relevant pathology applications will be also conducted. The research directions of these techniques for future development in pathology image analysis are also presented in this paper.
Information loss challenges in surgical navigation systems: From information fusion to AI-based approaches

Surgical navigation technology provides minimally invasive surgery (MIS) with the relative pose relationships amongst medical images, surgical instruments, and lesions. On the other hand, traditional operation procedures depend heavily on direct surgical field exposure. Consequently, introducing surgical navigation can enable surgeons to operate more accurately and efficiently. A tracking system is a core enabling technology of a surgical navigation system. In this paper, after reviewing the tracking technologies, we compare and analyze their pros and cons, and find that information loss is a common challenge. The information loss problem is an inherent drawback in mono-modality surgical navigation systems. It is characterized by physical constraints, attenuation, breakdown of signal, and accuracy instability of the tracking algorithms. This review focuses on the information loss problem in tracking technologies for surgical navigation systems. Furthermore, we survey the existing solutions that aim at tackling the information loss problem, especially in the information fusion of surgical tracking technologies, and we also summarize their key improvements and limitations. Particular attention has been given to the modalities, approaches, objectives, and surgical application scenarios, which can improve the accuracy, precision, and stability of surgical navigation systems. Finally, future research trends directed at improving the information loss problem are discussed, i.e., tight integration of sensing technology, augmented reality for visualization in surgical tracking, stable high-speed 5G networks for telesurgery, strong intelligence and affordable service.
Does the Vividness of Imagination Influence Illusory Self-Motion in Virtual Reality?

The illusory sensation of self-motion is defined as vection. Vection research can help enhance Virtual Reality applications and improve simulator fidelity as vection appears to be a desired sensation in motion simulators. The experience of vection can be modulated by cognitive factors and potentially personal traits, such as the vividness of imagination. Previous research investigating the relationship between auditory vection and kinesthetic imagery presented conflicting findings. However, the relationship between visually-induced vection and imagination has not been investigated. Herein we investigated the relationship between kinesthetic imagery and unimodal visual and bimodal visual-auditory vection. Twenty-nine participants were visually and audibly immersed in virtual environments with varying degrees of ecological relevance wherein they reported on their vection experience. No differences were found for vection intensity and latency measures between participants with high and low kinesthetic imagery. We conclude that imagery does not appear to play a role in the experience of visually-induced vection.
ASCAM-Former: Blind image quality assessment based on adaptive spatial &amp; channel attention merging transformer and image to patch weights sharing

Blind Image Quality Assessment (BIQA) is a challenging, unsolved research topic which is crucial for analyzing, understanding, and improving visual experience. Recently, transformer-based BIQA models are drawing increasing attention due to their powerful capacity in modeling global dependencies amongst tokens. However, existing works tend to apply self-attention mechanism for exploring the spatial dependencies whilst neglecting the impact of channel-wise self-attention. In this paper, we explore the feasibility of incorporating attention mechanism in a channel-wise manner for BIQA. By systematically studying the interactions between channel-wise and spatial-wise attention, an adaptive spatial and channel attention merging Transformer (ASCAM-Former) is then proposed for aggregating both the spatial-wise and channel-wise attention information. In addition, to accommodate IQA datasets containing both image and patch quality labels, an image to patch weights sharing (I2PWS) scheme is designed to take advantage of local quality learning tasks for reinforcing the learning of global quality, and vice versa. The experimental results indicate that channel-wise attention mechanism is as competitive as spatial-wise for IQA tasks, and the proposed ASCAM-Former yield accurate prediction on both authentically and synthetically distorted image quality datasets.
Feature pyramid network with multi-scale prediction fusion for real-time semantic segmentation

Feature pyramid network (FPN) is constructed from a bottom-up pathway and a top-down pathway. The method involves multi-scale features, so it can obtain rich contextual information from lower scales and high resolution from the largest scale. Additionally, different receptive fields are effective to capture both thin and large objects in image scenes. All feature maps concatenate together to predict the targets. However, the average pooling method yields the problem of combining the best predictions with poorer ones. In this paper, we proposed a dual prediction to leverage the useful characteristics of each FPN feature map. A low scale prediction attains good precision for large objects. The other one suitably segments narrow objects. Finally, a multi-scale fusion is deployed with an attention part. The attention module finds pixels of a low scale having high probabilities of wrong labels, and then requires the supplements from a high scale. A multi-scale fusion allows the network to learn across the different scales of predictions. We have achieved good Results 77.9% mIoU at 62 FPS on Cityscapes and 44.1% mIoU on Mapillary Vistas.
JRA-Net: Joint representation attention network for correspondence learning

In this paper, we propose a Joint Representation Attention Network (JRA-Net), an end-to-end network, to establish reliable correspondences for image pairs. The initial correspondences generated by the local feature descriptor usually suffer from heavy outliers, which makes the network unable to learn a powerful enough representation for distinguishing inliers and outliers. To this end, we design a novel attention mechanism. The proposed attention mechanism not only takes into account the correlations between global context and geometric information, but also introduces the joint representation of different scales to suppress trivial correspondences and highlight crucial correspondences. In addition, to improve the generalization ability of attention mechanism, we present an innovative weight function, to effectively adjust the importance of the attention mechanism in a learning manner. Finally, by combining the above components, the proposed JRA-Net is able to effectively infer the probabilities of correspondences being inliers. Empirical experiments on challenging datasets demonstrate the effectiveness and generalization of JRA-Net. We achieve remarkable improvements compared with the current state-of-the-art approaches on outlier rejection and relative pose estimation.
EEG-based covert speech decoding using random rotation extreme learning machine ensemble for intuitive BCI communication

Brain–computer interfaces (BCI) assist communication for the handicapped and disabled. Previous work has shown that it is usually non-intuitive, and it frequently employs Electroencephalogram (EEG) based motor imagery. Ensemble learning has been shown to be reliable in a variety of BCI classification tasks, including motor imagery and P300 event-related potential. The purpose of ensemble learning, according to past research, is to get trained accurate but diverse base classifiers to increase generalization performance. Extreme Learning Machine (ELM) with fast learning speed has been proven effective in classification applications and suitable for ensemble learning due to its randomness. The suitability of ensemble methods for intuitive BCI communication using EEG data of different covert speech words is investigated in this work. To achieve this goal, we construct a random rotation based kernel ELM ensemble, resulting in very diverse classifiers which can learn through transformed spaces. Minimize error ensemble pruning is used to prune low complementarity classifiers, and consequently, combining the remaining high complementarity classifiers to get the final ensemble classifier. Eight subjects performed covert speech tasks which involved mentally repeating five words namely; ‘left’, ‘right’, ‘up’, ‘down’, and ‘stop’. We achieved classification accuracy with pruning and without pruning of 63.67% and 57.13% respectively. Experimental results show that, in comparison to other classification techniques, the proposed technique produces competitive results. Our findings indicate the potential of random rotation kernel ELM ensemble pruning to effectively classify EEG-based covert speech signals. This proposed research involving an ensemble method for the classification of covert speech words can make a significant contribution towards intuitive BCI development using silent speech. • This paper presents EEG-based Covert Speech decoding for Intuitive BCI communication. • We model the system using Random Rotation Extreme Learning Machine Ensemble technique. • The system is examined with pruning and without pruning for Ensemble techniques. • The proposed technique produces competitive results in comparison to previous classification techniques.
WMTDBC: An unsupervised multivariate analysis model for fraud detection in health insurance claims

Fraud is an aggravating problem in the health insurance system, causing a substantial increase in the cost of medical services. Many models have been developed using data mining or machine learning techniques to lessen the impact of fraud on healthcare system. However, achieving good accuracy is still challenging as the claims data is multivariate with multiple class overlappings. In this paper, we propose a novel approach of unsupervised multivariate analysis for healthcare claims submitted by the providers. Our proposed model analyzes multivariate categorical data and continuous data in two stages to observe providers' behaviors. The first stage constructs Weighted MultiTree (WMT) for categorical data to analyze similarity among provider profiles, the relation among profiles, and rendered services to identify false services. The second stage detects false claims by developing a univariate fraud detection model using different Density Based Clustering (DBC) techniques on continuous data of claims such as service counts and service charges. The performance of our proposed WMTDBC is measured by conducting experiments on the claims within various medical specialties or provider types of CMS part B program. Our empirical results evidence that the detection performance is enhanced with our WMTDBC approach when compared with the state-of-the-art models.
A novel automated SuperLearner using a genetic algorithm-based hyperparameter optimization

Industrial revolution 4.0 has pushed industries worldwide to use machine learning (ML) models to address real-world engineering problems. The industry generally faces two main challenges in ML applications: the lack of skilled data scientists and the cost of obtaining large labeled datasets. These challenges need to be addressed to unlock the full potential of ML. In this work, a novel automated SuperLearner (SL) model using a genetic algorithm (AutoSL-GA) based hyperparameter optimization (HPO) is introduced to address the aforementioned challenges and assist scientists and engineers. Detailed comparisons are performed between AutoSL-GA, SuperLearner using Bayesian-based HPO (AutoSL-BO), and another well-known automated machine learning (AutoML) algorithm called Tree-based pipeline optimization tool (TPOT). Six different benchmark datasets were used to compare the performance and computational times of the models. AutoSL-GA resulted in higher performance with lower computational time than other models for all six benchmark datasets. Finally, a sensitivity analysis for dataset size was performed, in which AutoSL-GA also outperformed the other models across the dataset sizes while consuming the least computational resources.
The Impact of Sharing Gaze Behaviours in Collaborative Mixed Reality

In a remote collaboration involving a physical task, visualising gaze behaviours may compensate for other unavailable communication channels. In this paper, we report on a 360° panoramic Mixed Reality (MR) remote collaboration system that shares gaze behaviour visualisations between a local user in Augmented Reality and a remote collaborator in Virtual Reality. We conducted two user studies to evaluate the design of MR gaze interfaces and the effect of gaze behaviour (on/off) and gaze style (bi-/uni-directional). The results indicate that gaze visualisations amplify meaningful joint attention and improve co-presence compared to a no gaze condition. Gaze behaviour visualisations enable communication to be less verbally complex therefore lowering collaborators' cognitive load while improving mutual understanding. Users felt that bi-directional behaviour visualisation, showing both collaborator's gaze state, was the preferred condition since it enabled easy identification of shared interests and task progress.