Reviewerly/exHarmony huggingface dataset:
Design Space Exploration for Efficient Quantum Most-Significant Digit-First Arithmetic

Quantum computing has been considered as an emerging approach in addressing problems which are not easily solvable using classical computers. In parallel to the physical implementation of quantum processors, quantum algorithms have been actively developed for real-life applications to show quantum advantages, many of which benefit from quantum arithmetic algorithms and their efficient implementations. As one of the most important operations, quantum addition has been adopted in Shor's algorithm and quantum linear algebra algorithms. Although various least-significant digit-first quantum adders have been introduced in previous work, interest in investigating the efficient implementation of most-significant digit-first addition is growing. In this work, we propose a novel design method for most-significant digit-first addition with several quantum circuit optimisations to reduce the number of quantum bits (i.e. qubits), quantum gates, and circuit depth. An open-source library of different arithmetic operators based on our proposed method is presented, where all circuits are implemented on IBM Qiskit SDK. Extensive experiments demonstrate that our proposed design, together with the optimisation techniques, reduces T-depth by up-to 4.0×, T-count by 3.5×, and qubit consumption by 1.2×.
Trans-SBLGCN: A Transfer Learning Model for Event Logic Knowledge Graph Construction of Fault Diagnosis

Taking fault diagnosis corpus as the research object, an event logic knowledge graph construction method is proposed in this paper. Firstly, we propose a data labeling strategy based on a constructed event logic ontology model, then collect large-scale robot transmission system fault diagnosis corpus, and label part of the data according to the strategy. Secondly, we propose a transfer learning model called Trans-SBLGCN for event argument entity and event argument relation joint extraction. A language model is trained based on large-scale unlabeled fault diagnosis corpus and transferred to a model based on stacked bidirectional long short term memory (BiLSTM) and bidirectional graph convolutional network (BiGCN). Experimental results show that the method is superior to other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed to provide decision support for autonomous robot transmission system fault diagnosis.
Effects of Auxiliary Knowledge on Continual Learning

In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information without forgetting old knowledge (i.e., Catastrophic Forgetting). Most existing CL approaches focus on finding solutions to preserve acquired knowledge, so working on the "past" of the model. However, we argue that as the model has to continually learn new tasks, it is also important to put focus on the "present" knowledge that could improve following tasks learning. In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones. More specifically, our approach combines the main data stream with a secondary, diverse and uncorrelated stream, from which the network can draw auxiliary knowledge. This helps the model from different perspectives, since auxiliary data may contain useful features for the current and the next tasks and incoming task classes can be mapped onto auxiliary classes. Furthermore, the addition of data to the current task is implicitly making the classifier more robust as we are forcing the extraction of more discriminative features. Our method can outperform existing state-of-the-art models on the most common CL Image Classification benchmarks.
A Camera-based Deep-Learning Solution for Visual Attention Zone Recognition in Maritime Navigational Operations

The visual attention of navigators is imperative to understand the logic of navigation as well as the surveillance of navigators' status and operation. Current studies are implemented with the help of wearable eye-tracker glasses; yet, the high expenditure demanded by such equipment and service and its limitations on usability have impeded related research from further development. In this letter, the authors propose a framework, which is the first attempt in the maritime domain, to provide a camera-based deep-learning (CaBDeeL) visual attention recognition solution that outperforms the intrusive eye tracker regarding its shortcomings. A wide-angle camera is configured in front of the navigator in the advanced ship-bridge simulator in a way that visual attention reflected by their facial and head movements is captured in the front view. A pair of eye-tracker glasses is used to classify the captured visual attention images, which then form the primary database. During the process of classifying camera-captured images, a convolutional neural network (CNN) is built as an automatic classifier. The CNN is applied to two scenarios, and it shows an overall 95 % accuracy.
Structured Deep Unfolding Network for Optical Remote Sensing Image Super-Resolution

Single image super-resolution technology is critical in remote sensing, effectively improving the resolution of target images, with super-resolution algorithms based on deep learning demonstrating superior performance. However, most neural networks present shortcomings, such as lack of interpretability and requiring a long training time, limiting them in some application scenarios. Moreover, due to multi-degradation factors, tasks put forward higher requirements for the adaptability of algorithms. Therefore, this work develops a structured deep unfolding network (SDUNet), which is adaptable and requires a lower training time by cascading multiple small network modules. Additionally, the unfolding strategy proposed deals with multiple degradations, fully exploiting prior knowledge. The suggested method is challenged against state-of-the-art neural network methods on one optical remote sensing image dataset and one natural image dataset. The experimental results demonstrate our method’s effectiveness in requiring less training time, involving fewer parameters, and achieving a higher reconstruction performance for optical remote sensing image super-resolution.
Improving Graph Neural Network For Session-based Recommendation System Via Time Sessions

Session-based recommendations (SBR) have attracted much attention because of their high commercial value. SBR aims to predict a user's next click based on the sequence of previous sessions. However, existing graph neural network (GNN)-based recommendation methods only focus on session graphs and ignore the time frame in the session which contains valuable temporal information for reducing the impact of user's unintentional clicks. Meanwhile, the expression ability of hidden factors in GNNs is neglected. In order to solve the above limitations, this paper propose an improving Graph Neural Network via Time sessions (TGNN) model. For the data preprocessing aspect, TGNN builds temporal sessions from the time frame in the dataset. Dwell time recurrent network explores complex temporal influence on items by learning time features. Multi-head attention is introduced to enhance the expression ability of hidden factors. Target-aware attention is utilized to activate different user interests for different target items adaptively, which can obtain interest expression changes of target items. Comparative experiments are conducted on two public datasets, and experimental results show that our method outperforms state-of-the-art methods. Ablation experiments demonstrate the effectiveness of the proposed model.
Adaptive Neural Network-based Visual Servoing with Integral Sliding Mode Control for Manipulator

It is difficult to estimate the relationship between the motion of joint and the motion of image features, making the Calibration-free visual servoing control challenging. In traditional methods, the hand-eye relationship is usually approximated in purely online or offline ways. A practical scheme for robot arm manipulation with both online and offline learning is proposed in this paper. The hand-eye relationship is formulated in a local linear format with Jacobian matrix, which is approximated by radial-basis function network (RBFN). Primitively, the RBFN is trained offline to form a relatively appropriate estimation of the Jacobian matrix, which is the beginning of the online step. Then, an online modification of the RBFN is executed, compensating the error caused by changes of camera's position and pose or insufficient training. The simulation experiments show that the proposed scheme can provide a reliable offline trained model and can adapt well to the changes of camera's position and pose due to the online update law.
Motion Modeling and State Estimation in Range-Squared Coordinate

In some target tracking applications, angle measurements are absent due to sensor characteristics, which makes Cartesian states unobservable for a single stationary sensor. When multi-sensor target tracking techniques with only range or Doppler measurements are applied to produce Cartesian state estimates, their performance and efficiency can be benefited from accurate range and Doppler estimates obtained from single sensors. This paper investigates the range and Doppler estimation problem based on motion modeling in range-squared coordinate considering range-squared as a generic coordinate. A pseudostate vector consisting of range squared and its derivatives is defined to completely summarize the dynamic of the Cartesian nearly constant velocity (NCV) motion and it is found time evolution of the pseudostate is linear based on the methodology of state space representation. To estimate the pseudostate from range-Doppler or range-only measurements, a recursive filter is developed using the decorrelated unbiased converted measurement (DUCM) method. Finally, the pseudostate estimates in the range-squared coordinate are converted into the range coordinate using the scaled unscented transformation (SUT) method to produce range and Doppler estimates. Although the conversion is nonlinear, it is performed outside the filtering recursions without propagating nonlinearity approximation errors in the filtering recursions. Numerical experiments with various conditions of measurements demonstrate the superior performance benefits from the proposed accurate linear state equation and estimation method.
CF4FL: A Communication Framework for Federated Learning in Transportation Systems

Federated Learning (FL) is a promising technique to enhance the safety and efficiency of intelligent transportation systems. While FL has been extensively studied, the communication and networking challenges related to the operations of FL in dynamic yet dense vehicular networks remain under-explored. Limited storage and communication capacities of individual vehicles throttle the timely training of an FL model in distributed vehicular networks. In this paper, we present a communication framework for FL (CF4FL) in transportation systems. CF4FL aims to accelerate the convergence of FL training process through the innovation of two complementary networking components: (i) a deadline-driven vehicle scheduler (DDVS), and (ii) a concurrent vehicle polling scheme (CVPS). DDVS identifies a subset of vehicles for local model training in each iteration of FL, with the aim of minimizing data loss while respecting the deadline constraints derived from vehicles’ storage, computation, and energy budgets. CVPS takes advantage of multiple antennas on an edge server to enable concurrent local model transmissions in dynamic vehicular networks, thereby reducing the airtime overhead of each FL iteration. We have evaluated CF4FL through a blend of experimentation and simulation. Trace-driven simulation shows that, compared to existing scheduling and transmission schemes, CF4FL reduces the convergence time of FL training by 39%.
Virtual Mouse Using YOLO

The growth of ubiquitous computing in the current era has made the usage of natural user interfaces extremely important. The existence of computers and the utilization of Human Computer Interaction (HCI) in our civilizations will undoubtedly have a beneficial influence in our society. Hand gesture research publications have used a variety of methodologies, including those that enable computer vision and sensor technology, and the hand signs are frequently categorised under numerous topics, such as posture and gesture. This project focus on Artificial Intelligence (AI) for the hand gesture recognition to make the HCI more effective using the YOLOv5 algorithm.
Security Analysis of a Privacy-Preserving Identity-Based Encryption Architecture

Identity-Based Encryption (IBE) has seen limited adoption, largely due to the absolute trust that must be placed in the private key generator (PKG)-an authority that computes the private keys for all the users in the environment.Several constructions have been proposed to reduce the trust required in the PKG (and thus preserve the privacy of users), but these have generally relied on unrealistic assumptions regarding non-collusion between various entities in the system.Unfortunately, these constructions have not significantly improved IBE adoption rates in real-world environments.In this paper, we present a construction that reduces trust in the PKG without unrealistic non-collusion assumptions.We achieve this by incorporating a novel combination of digital credential technology and bilinear maps, and making use of multiple randomly-chosen entities to complete certain tasks.The main result and primary contribution of this paper are a thorough security analysis of this proposed construction, examining the various entity types, attacker models, and collusion opportunities in this environment.We show that this construction can prevent, or at least mitigate, all considered attacks.We conclude that our construction appears to be effective in preserving user privacy and we hope that this construction and its security analysis will encourage greater use of IBE in real-world environments.
Stereo Depth Estimation with Echoes

Stereo depth estimation is particularly amenable to local textured regions while echoes have good depth estimations for global textureless regions, thus the two modalities complement each other. Motivated by the reciprocal relationship between both modalities, in this paper, we propose an end-to-end framework named StereoEchoes for stereo depth estimation with echoes. A Cross-modal Volume Refinement module is designed to transfer the complementary knowledge of the audio modality to the visual modality at feature level. A Relative Depth Uncertainty Estimation module is further proposed to yield pixel-wise confidence for multimodal depth fusion at output space. As there is no dataset for this new problem, we introduce two Stereo-Echo datasets named Stereo-Replica and Stereo-Matterport3D for the first time. Remarkably, we show empirically that our StereoEchoes, on Stereo-Replica and Stereo-Matterport3D, outperforms stereo depth estimation methods by $$25\%/13.8\%$$ RMSE, and surpasses the state-of-the-art audio-visual depth prediction method by $$25.3\%/42.3\%$$ RMSE.
Targeted Data Poisoning Attacks Against Continual Learning Neural Networks

Continual (incremental) learning approaches are designed to address catastrophic forgetting in neural networks by training on batches or streaming data over time. In many real-world scenarios, the environments that generate streaming data are exposed to untrusted sources. These untrusted sources can be exposed to data poisoned by an adversary. The adversaries can manipulate and inject malicious samples into the training data. Thus, the untrusted data sources and malicious samples are meant to expose the vulnerabilities of neural networks that can lead to serious consequences in applications that require reliable performance. However, recent works on continual learning only focused on adversary agnostic scenarios without considering the possibility of data poisoning attacks. Further, recent work has demonstrated there are vulnerabilities of continual learning approaches in the presence of backdoor attacks with a relaxed constraint on manipulating data. In this paper, we focus on a more general and practical poisoning setting that artificially forces catastrophic forgetting by clean-label data poisoning attacks. We proposed a task targeted data poisoning attack that forces the neural network to forget the previous-learned knowledge, while the attack samples remain stealthy. The approach is benchmarked against three state-of-the-art continual learning algorithms on both domain and task incremental learning scenarios. The experiments demonstrate that the accuracy on targeted tasks significantly drops when the poisoned dataset is used in continual task learning.
GAN with ASPP for SAR Image to Optical Image Conversion

Researchers can gain more intuitive information by converting synthetic aperture radar (SAR) images to optical images using generative adversarial networks (GANs). However, their GANs have poor feature extraction ability, which leads to color conversion errors and loss of details. Therefore, to solve this problem, we add an atrous spatial pyramid pooling (ASPP) module to GAN to enhance the feature extraction ability, i.e., ASPP-GAN. ASPP module can extract multi-resolution feature responses, enabling the network to focus on both overall and detailed features for better feature extraction ability. The experimental results on public SEN1-2 datasets show that ASPP-GAN has a significant improvement over the traditional GAN, i.e., Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) values are improved by about 20%.
Threats From Unintentional Insiders: An Assessment of an Organization’s Readiness Using Machine Learning

Today's organisations are facing a number of challenges, one of the most significant of which is ensuring the safety of their digital data.This is as a result of the fact that they are frequently faced with internal and external threats that can put the data they have been entrusted with in jeopardy of being compromised.As a result of this, this study investigates the dimension of threats associated to unintentional internal user of an organisation and utilises NARX to model and test a detection scheme associated to the menace.In addition, this study aims to provide a better understanding of the current state of the threat landscape.The data adopted for this research is primarily a "user activity logs" dataset from CERT (release version r4.2).From the data, the study conceptualized "Access", "Motivation", and "Action" to be the key dimensions influencing "insider", whereas "Intent", "+Action", "Method", and "knowledge" are the key dimension influencing "threats".Experimental analyses conducted by NARX within several numbers of partitions of the data point to a good detection capacity, with the greatest value of R 2 coming in at 0.97.This indicates that NARX was able to detect the crucial dimension that was formulated for by the research to be the detections parameter of an inadvertent insider threat when operating under the best partition.In light of these findings, organisations can use the proposed approach to assess their preparedness for Insider attacks.
VCL-PL:Semi-Supervised Learning from Noisy Web Data with Variational Contrastive Learning

We address the problem of web supervised learning, in particular for face attribute classification. Web data suffers from image set noise, due to unrelated images that may be retrieved in response to the query. We propose a semi-supervised pseudo-labeling approach where the embedding space distribution is learnt via variational contrastive learning. We use 40 Gaussian sampling heads for the 40 attributes in the CelebA dataset and apply supervised contrastive learning over a limited amount of labelled data, to address the multi-label face attribute classification problem. Soft pseudo-labeling is then used to label the unlabelled data at attribute level, followed by two-stage domain adaptation. We show that the proposed method using noisy web data brings improvements in accuracy over supervised multi-label face attribute classification in all experimental settings (over 2% points for very low-data setting). We suggest that learning the embedding distribution and the subsequent soft pseudo-labeling according to the nearest neighbors help in overcoming the noise in the unlabeled data.
An End-to-End Natural Language Processing Application for Prediction of Medical Case Coding Complexity: Algorithm Development and Validation

Medical coding is the process that converts clinical documentation into standard medical codes. Codes are used for several key purposes in a hospital (eg, insurance reimbursement and performance analysis); therefore, their optimization is crucial. With the rapid growth of natural language processing technologies, several solutions based on artificial intelligence have been proposed to aid in medical coding by automatically suggesting relevant codes for clinical documents. However, their effectiveness is still limited to simple cases, and it is not yet clear how much value they can bring in improving coding efficiency and accuracy.This study aimed to bring more efficiency to the coding process to improve the selection of codes by medical coders. To achieve this, we developed an innovative multimodal machine learning-based solution that, instead of predicting codes, detects the degree of coding complexity before coding is performed. The notion of coding complexity was used to better dispatch work among medical coders to eventually minimize errors and improve throughput.To train and evaluate our approach, we collected 2060 cases rated by coders in terms of coding complexity from 1 (simplest) to 4 (most complex). We asked 2 expert coders to rate 3.01% (62/2060) of the cases as the gold standard. The agreements between experts were used as benchmarks for model evaluation. A case contains both clinical text and patient metadata from the hospital electronic health record. We extracted both text features and metadata features, then concatenated and fed them into several machine learning models. Finally, we selected 2 models. The first used cross-validated training on 1751 cases and testing on 309 cases aiming to assess the predictive power of the proposed approach and its generalizability. The second model was trained on 1998 cases and tested on the gold standard to validate the best model performance against human benchmarks.Our first model achieved a macro-F1-score of 0.51 and an accuracy of 0.59 on classifying the 4-scale complexity. The model distinguished well between the simple (combined complexity 1-2) and complex (combined complexity 3-4) cases with a macro-F1-score of 0.65 and an accuracy of 0.71. Our second model achieved 61% agreement with experts' ratings and a macro-F1-score of 0.62 on the gold standard, whereas the 2 experts had a 66% (41/62) agreement ratio with a macro-F1-score of 0.67.We propose a multimodal machine learning approach that leverages information from both clinical text and patient metadata to predict the complexity of coding a case in the precoding phase. By integrating this model into the hospital coding system, distribution of cases among coders can be done automatically with performance comparable with that of human expert coders, thus improving coding efficiency and accuracy at scale.