Reviewerly/exHarmony huggingface dataset:
MUS-CDB: Mixed Uncertainty Sampling With Class Distribution Balancing for Active Annotation in Aerial Object Detection

Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects. Active learning effectively reduces the data labeling cost by selectively querying the informative and representative unlabelled samples. However, existing active learning methods are mainly with class-balanced settings and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenarios due to the long-tailed class distribution and dense small objects in aerial scenes. In this paper, we propose a novel active learning method for cost-effective aerial object detection. Specifically, both object-level and image-level informativeness are considered in the object selection to refrain from redundant and myopic querying. Besides, an easy-to-use class-balancing criterion is incorporated to favor the minority objects to alleviate the long-tailed class distribution problem in model training. We further devise a training loss to mine the latent knowledge in the unlabeled image regions. Extensive experiments are conducted on the DOTA-v1.0 and DOTA-v2.0 benchmarks to validate the effectiveness of the proposed method. For the ReDet, KLD, and SASM detectors on the DOTA-v2.0 dataset, the results show that our proposed MUS-CDB method can save nearly 75% of the labeling cost while achieving comparable performance to other active learning methods in terms of mAP. Code is publicly online.
RS-TTS: A Novel Joint Entity and Relation Extraction Model

Joint extraction of entity and relation is a basic task in the field of natural language processing. Existing methods have achieved good result, but there are still some limitations, such as span-based extraction cannot solve overlapping problems well, and redundant relation calculation leads to many invalid operations. To solve these problems, we propose a novel RelationSpecific Triple Tagging and Scoring Model (RS-TTS) for the joint extraction of entity and relation. Specifically, the model is composed of three parts: we use a relation judgment module to predict all potential relations to prevent computational redundancy; then a boundary smoothing mechanism is introduced to the entity pair extraction, which reallocates the probability of the ground truth entity to its surrounding tokens, thus preventing the model from being overconfident; finally, an efficient tagging and scoring strategy is used to decode entity. Extensive experiments show that our model performs better than the state-of-the-art baseline on the public benchmark dataset. F1-scores on the four datasets are improved, especially on WebNLG and WebNLG <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> , which are improved by 1.7 and 1.1 respectively.
Are we ready for hotel robots after the pandemic? A profile analysis

COVID-19 has changed many aspects of the hospitality and tourism industry, including technology-oriented and contactless solutions. Despite the increasing number of service companies using robots on their premises, most of the previous attempts and practices of adoption have remained unsuccessful. Prior research hints that socioeconomic factors could influence the successful adoption of these emerging technologies. Nevertheless, these studies ignore the role of profile factors and assume a homogenous response to using robots in service operations during the pandemic. Based on the theory of diffusion of innovation and a sample of 525 participants, this study investigates the differences in customers' attitudes, their level of involvement, and optimism for service robots as well as their intentions to use service robots in the five main areas of hotel operations (front desk, concierge, housekeeping, room service, and food and beverage) based on five profile factors (age, gender, income level, education, and purpose of trip). MANOVA tests show significant differences in all variables based on demographic factors; male, younger, more educated, higher income, and leisure travelers show more positive attitudes, higher involvement, optimism, and intention to use service robots across various hotel departments. In particular, mean scores were found to be smaller for the traditionally human-oriented functional areas of the hotel operations. We also clustered the participants based on their level of comfort and optimism about using service robots in hotels. Given the rapid changes in the service industry and the increasing adoption of service robots, this paper adds a much-needed contribution to the ongoing research on service robots in the service industry by investigating the impact of profile factors on guests’ behavior towards service robots.
COBATS: A Novel Consortium Blockchain-Based Trust Model for Data Sharing in Vehicular Networks

Achieving efficient and secure shared data in vehicular networks is important for the development of smart transportation. Sharing data among intelligent vehicles not only enriches vehicle services but also improves traffic safety and efficiency. However, due to the specific nature of vehicular networks, security and privacy concerns prevent data providers from participating in the data sharing process. In addition, the quality of the data shared in the vehicular network is uneven and unreliable, and the reliability and authenticity of data sharing need to be further improved. In this paper, we propose a novel consortium blockchain-based trust model in vehicular networks (COBATS) to achieve secure storage and data sharing. To improve the quality of data sharing, we also design a trust management model capable of filtering malicious recommendations, which reduces the hazard of malicious nodes and ensures high-quality data sharing among vehicles. Moreover, we present a consensus mechanism with joint Proof-of-Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) to reduce resource consumption and improve the algorithm’s efficiency. The simulation results show that COBATS can improve the security and quality of data sharing. Furthermore, our model also can effectively handle certain attacks.
A Simple Baseline for Multi-Camera 3D Object Detection

3D object detection with surrounding cameras has been a promising direction for autonomous driving. In this paper, we present SimMOD, a Simple baseline for Multi-camera Object Detection, to solve the problem. To incorporate multiview information as well as build upon previous efforts on monocular 3D object detection, the framework is built on sample-wise object proposals and designed to work in a twostage manner. First, we extract multi-scale features and generate the perspective object proposals on each monocular image. Second, the multi-view proposals are aggregated and then iteratively refined with multi-view and multi-scale visual features in the DETR3D-style. The refined proposals are endto-end decoded into the detection results. To further boost the performance, we incorporate the auxiliary branches alongside the proposal generation to enhance the feature learning. Also, we design the methods of target filtering and teacher forcing to promote the consistency of two-stage training. We conduct extensive experiments on the 3D object detection benchmark of nuScenes to demonstrate the effectiveness of SimMOD and achieve competitive performance. Code will be available at https://github.com/zhangyp15/SimMOD.
LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classification

The success of convolutional neural network-based architecture on image classification in learning and extracting features has made them very popular recently, but the task of image classification becomes more challenging when we apply state-of-the-art models to classify noisy and low-quality images. It is still difficult for models to extract meaningful features from this type of image due to its low resolution and lack of meaningful global features. Moreover, high-resolution images need more layers to train, which means they take more time and computational power. Our method also addresses the problem of vanishing gradients as the layers become deeper in the deep neural networks that we mentioned earlier. In order to address all of these issues, we developed a novel image classification architecture composed of blocks that are designed to learn both low-level and global features from blurred and noisy low-resolution images. Our design of the blocks was heavily influenced by Residual Connections and Inception modules in order to increase performance and reduce parameter sizes. We also assess our work using the MNIST family datasets, with a particular emphasis on the Oracle-MNIST dataset, which is the most difficult to classify due to its low-quality and noisy images. We have performed in-depth tests that demonstrate that the presented architecture is faster and more accurate than existing cutting-edge convolutional neural networks. Furthermore, due to the unique properties of our model, it can produce a better result with fewer parameters. The source code of the project is available at this GitHub repository.
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users’ multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC) , which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data.
Traffic Sign Recognition with Deep Learning: Vegetation Occlusion Detection in Brazilian Environments

Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it's possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies.
Text-DIAE: A Self-Supervised Degradation Invariant Autoencoder for Text Recognition and Document Enhancement

In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OCR
Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data

Abstract When clinicians assess the prognosis of patients in intensive care, they take imaging and non-imaging data into account. In contrast, many traditional machine learning models rely on only one of these modalities, limiting their potential in medical applications. This work proposes and evaluates a transformer-based neural network as a novel AI architecture that integrates multimodal patient data, i.e., imaging data (chest radiographs) and non-imaging data (clinical data). We evaluate the performance of our model in a retrospective study with 6,125 patients in intensive care. We show that the combined model (area under the receiver operating characteristic curve [AUROC] of 0.863) is superior to the radiographs-only model (AUROC = 0.811, p &lt; 0.001) and the clinical data-only model (AUROC = 0.785, p &lt; 0.001) when tasked with predicting in-hospital survival per patient. Furthermore, we demonstrate that our proposed model is robust in cases where not all (clinical) data points are available.
Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis

Recently, convolutional neural networks (CNNs) directly using whole slide images (WSIs) for tumor diagnosis and analysis have attracted considerable attention, because they only utilize the slide-level label for model training without any additional annotations. However, it is still a challenging task to directly handle gigapixel WSIs, due to the billions of pixels and intra-variations in each WSI. To overcome this problem, in this paper, we propose a novel end-to-end interpretable deep MIL framework for WSI analysis, by using a two-branch deep neural network and a multi-scale representation attention mechanism to directly extract features from all patches of each WSI. Specifically, we first divide each WSI into bag-, patch- and cell-level images, and then assign the slide-level label to its corresponding bag-level images, so that WSI classification becomes a MIL problem. Additionally, we design a novel multi-scale representation attention mechanism, and embed it into a two-branch deep network to simultaneously mine the bag with a correct label, the significant patches and their cell-level information. Extensive experiments demonstrate the superior performance of the proposed framework over recent state-of-the-art methods, in term of classification accuracy and model interpretability. All source codes are released at: https://github.com/xhangchen/MRAN/.
Evaluating innovation performance for financial technology within the national innovation system: A case study of the Kingdom of Bahrain

Objective: This article aims to identify the role of ICT infrastructure and knowledge creation on innovation outcomes based on the technology innovation system (TIS) framework to stimulate innovation in financial technology (fintech) in Bahrain. Research Design & Methods:This study employed the framework of the technology innovation system (as a subset of the national innovation system (NIS)) as a theoretical framework, with variables that were extracted from four major innovation theories.The study evaluated the fintech innovation sector in several commercial banks and government institutions in the Kingdom of Bahrain using a survey questionnaire.The data were collected from 119 respondents, and analyzed through partial least squares structural equation modelling(PLS-SEM).Findings: The results showed that knowledge creation, ICT infrastructure, and knowledge diffusion impacted innovation outputs through knowledge impact.Moreover, knowledge creation did not affect innovation outputs directly but through knowledge creation.Implications & Recommendations: The study would be invaluable for the financial managers of fintech industries in implementing strategies as well as for the policymakers in integrating financial technology innovations into the financial system. Contribution & Value Added:The study uniquely explains the role of knowledge impact between knowledge creation, ICT infrastructure, knowledge diffusion, and innovation outputs.
Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning

The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high.
Biasing the quantum vacuum to control macroscopic probability distributions

Quantum field theory suggests that electromagnetic fields naturally fluctuate, and these fluctuations can be harnessed as a source of perfect randomness. Many potential applications of randomness rely on controllable probability distributions. We show that vacuum-level bias fields injected into multistable optical systems enable a controllable source of quantum randomness, and we demonstrated this concept in an optical parametric oscillator (OPO). By injecting bias pulses with less than one photon on average, we controlled the probabilities of the two possible OPO output states. The potential of our approach for sensing sub–photon-level fields was demonstrated by reconstructing the temporal shape of fields below the single-photon level. Our results provide a platform to study quantum dynamics in nonlinear driven-dissipative systems and point toward applications in probabilistic computing and weak field sensing.
FairRec: Fairness Testing for Deep Recommender Systems

Deep learning-based recommender systems (DRSs) are increasingly and widely deployed in the industry, which brings significant convenience to people’s daily life in different ways. However, recommender systems are also shown to suffer from multiple issues, e.g., the echo chamber and the Matthew effect, of which the notation of “fairness” plays a core role. For instance, the system may be regarded as unfair to 1) a specific user, if the user gets worse recommendations than other users, or 2) an item (to recommend), if the item is much less likely to be exposed to the users than other items. While many fairness notations and corresponding fairness testing approaches have been developed for traditional deep classification models, they are essentially hardly applicable to DRSs. One major challenge is that there still lacks a systematic understanding and mapping between the existing fairness notations and the diverse testing requirements for deep recommender systems, not to mention further testing or debugging activities. To address the gap, we propose FairRec, a unified framework that supports fairness testing of DRSs from multiple customized perspectives, e.g., model utility, item diversity, item popularity, etc. We also propose a novel, efficient search-based testing approach to tackle the new challenge, i.e., double-ended discrete particle swarm optimization (DPSO) algorithm, to effectively search for hidden fairness issues in the form of certain disadvantaged groups from a vast number of candidate groups. Given the testing report, by adopting a simple re-ranking mitigation strategy on these identified disadvantaged groups, we show that the fairness of DRSs can be significantly improved. We conducted extensive experiments on multiple industry-level DRSs adopted by leading companies. The results confirm that FairRec is effective and efficient in identifying the deeply hidden fairness issues, e.g., achieving ∼95% testing accuracy with ∼half to 1/8 time.
Global path planning for multiple AUVs using GWO

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path.The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost.The path is defined as a set of segments, passing through selected nodes known as waypoints.For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving.Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean.This paper addresses the GPP problem for multiple AUVs in formation.Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation.The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem.GA concept is simple to understand, easy to implement and supports multi-objective optimization.It is robust to local minima and have wide applications in various fields of science, engineering and commerce.Hence, GA is used for this comparative study.The performance analysis is based on computational time, length of the path generated and the total path cost.The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time.Thus, GWO is used as the GPP algorithm for three AUVs in formation.The formation follows leader-follower topography.A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists.The stability of the sliding surface is verified by Lyapunov stability analysis.With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses.Thus, lower path cost leads to less expensive underwater missions.