Reviewerly/exHarmony huggingface dataset:
Acceleration Methods

This monograph covers some recent advances in a range of acceleration techniques frequently used in convex optimization. We first use quadratic optimization problems to introduce two key families of methods, namely momentum and nested optimization schemes. They coincide in the quadratic case to form the Chebyshev method. We discuss momentum methods in detail, starting with the seminal work of Nesterov and structure convergence proofs using a few master templates, such as that for optimized gradient methods, which provide the key benefit of showing how momentum methods optimize convergence guarantees. We further cover proximal acceleration, at the heart of the Catalyst and Accelerated Hybrid Proximal Extragradient frameworks, using similar algorithmic patterns. Common acceleration techniques rely directly on the knowledge of some of the regularity parameters in the problem at hand. We conclude by discussing restart schemes, a set of simple techniques for reaching nearly optimal convergence rates while adapting to unobserved regularity parameters.
Comprehensive and efficient workload compression

This work studies the problem of constructing a representative workload from a given input analytical query workload where the former serves as an approximation with guarantees of the latter. We discuss our work in the context of workload analysis and monitoring. As an example, evolving system usage patterns in a database system can cause load imbalance and performance regressions which can be controlled by monitoring system usage patterns, i.e., a representative workload, over time. To construct such a workload in a principled manner, we formalize the notions of workload representativity and coverage. These metrics capture the intuition that the distribution of features in a compressed workload should match a target distribution, increasing representativity, and include common queries as well as outliers, increasing coverage. We show that solving this problem optimally is computationally hard and present a novel greedy algorithm that provides approximation guarantees. We compare our techniques to established algorithms in this problem space such as sampling and clustering, and demonstrate advantages and key trade-offs.
The effect of digital marketing capabilities on business performance enhancement: Mediating the role of customer relationship management (CRM)

The advent of digital marketing has replaced traditional marketing and shed a great impact on the global economic paradigm. Digital market helps to flourish the service sector by offering the customers desired products. Therefore, the aim of this study is to examine the impact of digital marketing capabilities on business performance enhancement in the United Arab Emirate (UAE) insurance sector. This study used the cross-sectional survey questionnaire method for data collection. This study empirically validated that digital marketing capabilities have a significant impact on business performance enhancement by using the partial least square-structural equation modeling analysis. The findings of this study will be helpful for the regulators and policymakers to design a policy to enhance the business performance by using the means of digital marketing.
How to detect healthcare fraud? “A systematic review”

To identify the method used in detecting fraud cases.Articles searching by using topic-appropriate keywords and incorporated into search engines (data-based) journals Pubmed/Medline, Cochrane, Wiley, ScienceDirect, and secondary data-based Google scholar. Then data extraction is done based on inclusion criteria. The selected articles have the aim of investigating/detecting cases of fraud that have occurred in the health sector or other related sectors that support the study.The findings of the nine reviewed articles have suggested that most of the fraud perpetrators are performed by medical personnel (doctors) and providers. Many types of fraud occur such as insurance claims or medical actions that are completely unadministered nor following the procedure and duplicating claims. The methods that appropriate to be used in detecting fraud are secondary data tracking, information, and technology specialist provision.Secondary data tracking is the most widely used method in fraud detection. Fraud perpetrators are ones who dominated by medical circles with fictitious claim cases. Perpetrators tend not to act themselves but in organizations with network.
Deep-QPP

Motivated by the recent success of end-to-end deep neural models for ranking tasks, we present here a supervised end-to-end neural approach for query performance prediction (QPP). In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven. Further, in contrast to weakly supervised approaches, our method also does not rely on the outputs from different QPP estimators. In particular, our model leverages information from the semantic interactions between the terms of a query and those in the top-documents retrieved with it. The architecture of the model comprises multiple layers of 2D convolution filters followed by a feed-forward layer of parameters. Experiments on standard test collections demonstrate that our proposed supervised approach outperforms other state-of-the-art supervised and unsupervised approaches.
Continuous-variable quantum key distribution over a 15 km multi-core fiber

Abstract The secure transfer of information is critical to the ever-increasing demands of the digital world. Continuous-variable quantum key distribution (CV-QKD) is a promising technology that can provide high secret key rates over metropolitan areas, using conventional telecom components. In this study, we demonstrate the utilization of CV-QKD over a 15 km multi-core fiber (MCF), in which we take advantage of one core to remotely frequency lock Bob's local oscillator with Alice's transmitter. We also demonstrate the capacity of the MCF to boost the secret key rate by parallelizing CV-QKD across multiple cores. Our results indicate that MCFs are promising for the metropolitan deployment of QKD systems.
Current State and General Perceptions of the Use of Extended Reality (XR) Technology at the University of Newcastle: Interviews and Surveys From Staff and Students

Extended reality (XR) technology is an emerging teaching tool within the higher education sector. Many institutions are currently running pilot projects, primarily assessing individual XR teaching tools typically being led by innovative/technology-driven teaching staff, which may introduce a self-selection bias and may not represent the general attitudes of the broader staff and student population. We applied a mixed-methods approach to gain insight into end-user acceptability, value areas, barriers, and opportunities for the adoption of XR in teaching at an Australian University. A university-wide online survey and targeted interview sessions with XR technology users show a general readiness for broad adoption of XR technologies in university education. Whilst existing XR teaching applications were described as “successful,” relatively few applications were sustainably integrated into the curriculum. Our data highlights the existing barriers for the successful transition from individual use-cases of XR tools to broader adoption across university institutions.
RETRACTED ARTICLE: An ensemble deep learning classifier for sentiment analysis on code-mix Hindi–English data

Code-mixing on social media is a trend in many countries where people speak multiple languages, such as India, where Hindi and English are major communication languages. Sentiment analysis is beneficial in understanding users’ opinions and thoughts on social, economic, and political issues. It eliminates the manual monitoring of each and every review, which is a cumbersome task. However, performing sentiment analysis on code-mix data is challenging, as it involves various out of vocabulary terms and numerous issues, making it a new field in natural language processing. This work includes dealing with such text and ensembling a classifier to detect sentiment polarity. Our classifier ensembles a multilingual variant of RoBERTa and a sentence-level embedding from Universal Sentence Encoder to identify the sentiments of these code-mixed tweets with higher accuracy. This ensemble optimises the classifier's performance by using the strength of both for transfer learning. Experiments were conducted on real-life benchmark datasets and revealed their sentiment. The performance of the proposed classifier framework is compared with other baselines and deep learning models on five datasets to show the superiority of our results. Results showed improved and increased performance in the proposed classifier's accuracy, precision, and recall. The accuracy achieved by our classifier on code-mix datasets is 66% on Joshi et al. 2016, 60% on SAIL 2017, and 67% on SemEval 2020 Task-9 dataset, which is on average around 3% as compared to contemporary baselines.
Path Following of a Quadrotor With a Cable-Suspended Payload

This article addresses the design of a path following controller for a payload tethered to a quadrotor. Specifically, the goal is to design a smooth dynamic feedback controller that forces the suspended payload to converge and follow a large class of embedded curves. The given curve is treated as a smooth manifold, and set stabilization is used to find the maximal control invariant manifold. The controller design guarantees that once the aerial robot approaches the path, the payload never leaves the path. The performance of the proposed controller is verified through simulations with practical sensor noise and parametric uncertainties. Moreover, experimental implementation and verification of the proposed control scheme are performed on a Quanser UAV platform in an indoor flying arena.
CrowdFormer: An Overlap Patching Vision Transformer for Top-Down Crowd Counting

Crowd counting methods typically predict a density map as an intermediate representation of counting, and achieve good performance. However, due to the perspective phenomenon, there is a scale variation in real scenes, which causes the density map-based methods suffer from a severe scene generalization problem because only a limited number of scales are fitted in density map prediction and generation. To address this issue, we propose a novel vision transformer network, i.e., CrowdFormer, and a density kernels fusion framework for more accurate density map estimation and generation, respectively. Thereafter, we incorporate these two innovations into an adaptive learning system, which can take both the annotation dot map and original image as input, and jointly learns the density map estimator and generator within an end-to-end framework. The experimental results demonstrate that the proposed model achieves the state-of-the-art in the terms of MAE and MSE (e.g., it achieved a MAE of 67.1 and MSE of 301.6 on NWPU-Crowd dataset.), and confirm the effectiveness of the proposed two designs. The code is https://github.com/special-yang/Top_Down-CrowdCounting.
An enhanced honey badger algorithm based on Lévy flight and refraction opposition-based learning for engineering design problems

Honey badger algorithm (HBA) is a recently developed meta-heuristic algorithm, which mainly simulates the dynamic search behavior of honey badger in wild nature. Similar to other basic algorithms, HBA may suffer from the weakness of poor convergence accuracy, inadequate balance between exploration and exploitation, and ease of getting trapped into the local optima. In order to address these drawbacks, this paper proposes an enhanced honey badger algorithm (EHBA) to improve the search quality of the basic method from three aspects. First, we introduce the highly disruptive polynomial mutation to initialize the population. This is considered from increasing the population diversity. Second, Lévy flight is integrated into the position update formula to boost search efficiency and balance exploration and exploitation capabilities of the algorithm. Furthermore, the refraction opposition-based learning is applied to the current global optimum of the swarm to help the population jump out of the local optima. To validate the function optimization performance, the proposed EHBA is comprehensively analyzed on 18 standard benchmark functions and IEEE CEC2017 test suite. Compared with the basic HBA and seven state-of-the-art algorithms, the experimental results demonstrate that EHBA can outperform other competitors on most of the test functions with superior solution accuracy, local optima avoidance, and stability. Additionally, the applicability of the proposed method is further highlighted by solving four engineering design problems. The results indicate that EHBA also has competitive performance and promising prospects for real-world optimization tasks.
Learning to rank method combining multi-head self-attention with conditional generative adversarial nets

The existing methods of learning to rank often ignore the relationship between ranking features. If the relationship between them can be fully utilized, the performance of learning to rank methods can be improved. Aiming at this problem, an approach of learning to rank that combines a multi-head self-attention mechanism with Conditional Generative Adversarial Nets (CGAN) is proposed in this paper, named *GAN-LTR. The proposed approach improves some design ideas of Information Retrieval Generative Adversarial Networks (IRGAN) framework applied to web search, and a new network model is constructed by integrating convolution layer, multi-head self-attention layer, residual layer, fully connected layer, batch normalization, and dropout technologies into the generator and discriminator of Conditional Generative Adversarial Nets (CGAN). The convolutional neural network is used to extract the ranking feature representation of the hidden layer and capture the internal correlation and interactive information between features. The multi-head self-attention mechanism is used to fuse feature information in multiple vector subspaces and capture the attention weight of features, so as to assign appropriate weights to different features. The experimental results on the MQ2008-semi learning to rank dataset show that compared with IRGAN, our proposed learning to rank method *GAN-LTR has certain performance advantages in various performance indicators on the whole.
Training a Disaster Victim Detection Network for UAV Search and Rescue Using Harmonious Composite Images

Human detection in images using deep learning has been a popular research topic in recent years and has achieved remarkable performance. Training a human detection network is useful for first responders to search for trapped victims in debris after a disaster. In this paper, we focus on the detection of such victims using deep learning, and we find that state-of-the-art detection models pre-trained on the well-known COCO dataset fail to detect victims. This is because all the people in the training set are shown in photos of daily life or sports activities, while people in the debris after a disaster usually only have parts of their bodies exposed. In addition, because of the dust, the colors of their clothes or body parts are similar to those of the surrounding debris. Compared with collecting images of common objects, images of disaster victims are extremely difficult to obtain for training. Therefore, we propose a framework to generate harmonious composite images for training. We first paste body parts onto a debris background to generate composite victim images and then use a deep harmonization network to make the composite images look more harmonious. We select YOLOv5l as the most suitable model, and experiments show that using composite images for training improves the AP (average precision) by 19.4% (15.3%→34.7%). Furthermore, using the harmonious images is of great benefit to training a better victim detector, and the AP is further improved by 10.2% (34.7%→44.9%). This research is part of the EU project INGENIOUS. Our composite images and code are publicly available on our website.
PMAL: Open Set Recognition via Robust Prototype Mining

Open Set Recognition (OSR) has been an emerging topic. Besides recognizing predefined classes, the system needs to reject the unknowns. Prototype learning is a potential manner to handle the problem, as its ability to improve intra-class compactness of representations is much needed in discrimination between the known and the unknowns. In this work, we propose a novel Prototype Mining And Learning (PMAL) framework. It has a prototype mining mechanism before the phase of optimizing embedding space, explicitly considering two crucial properties, namely high-quality and diversity of the prototype set. Concretely, a set of high-quality candidates are firstly extracted from training samples based on data uncertainty learning, avoiding the interference from unexpected noise. Considering the multifarious appearance of objects even in a single category, a diversity-based strategy for prototype set filtering is proposed. Accordingly, the embedding space can be better optimized to discriminate therein the predefined classes and between known and unknowns. Extensive experiments verify the two good characteristics (i.e., high-quality and diversity) embraced in prototype mining, and show the remarkable performance of the proposed framework compared to state-of-the-arts.
Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

Abstract Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.
User-Aware Multi-Interest Learning for Candidate Matching in Recommenders

Recommender systems have become a fundamental service in most E-Commerce platforms, in which the matching stage aims to retrieve potentially relevant candidate items to users for further ranking. Recently, some efforts on extracting multi-interests from user's historical behaviors have demonstrated superior performance. However, the historical behaviors are not noise-free due to the possible misclicks or disturbances. Existing works mainly overlook the fact that the interests of a user are not only reflected by the historical behaviors, but also inherently regulated by the profile information. Hence, we are interested in exploiting the benefit of user profile in multi-interest learning to enhance candidate matching performance. To this end, a user-aware multi-interest learning framework (named UMI) is proposed in this paper to exploit both user profile and behavior information for candidate matching. Specifically, UMI consists of two main components: dual-attention routing and interest refinement. In the dual-attention routing, we firstly introduce a user-guided attention network to identify the important historical items with respect to the user profile. Then, the resultant importance weights are leveraged via the dual-attentive capsule network to extract the user's multi-interests. Afterwards, the extracted interests are utilized to highlight the corresponding user profile features for interest refinement, such that different user profiles can be incorporated into interest learning for diverse user preference understanding. Besides, to improve the model's discriminative capacity, we further devise a harder-negatives strategy to support model optimization. Extensive experiments show that UMI significantly outperforms state-of-the-art multi-interest modeling alternatives. Currently, UMI has been successfully deployed at Taobao App in Alibaba, serving hundreds of millions of users.
Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain

Multi-robot systems are popularly distributed in logistics, transportation, and other fields. We propose a distributed multi-mobile robot obstacle-avoidance algorithm to coordinate the path planning and motion navigation among multiple robots and between robots and unknown territories. This algorithm fuses the ant colony optimization (ACO) and the dynamic window approach (DWA) to coordinate a multi-robot system through a priority strategy. Firstly, to ensure the optimality of robot motion in complex terrains, we proposed the dual-population heuristic functions and a sort ant pheromone update strategy to enhance the search capability of ACO, and the globally optimal path is achieved by a redundant point deletion strategy. Considering the robot’s path-tracking accuracy and local target unreachability problems, an adaptive navigation strategy is presented. Furthermore, we propose the obstacle density evaluation function to improve the robot’s decision-making difficulty in high-density obstacle environments and modify the evaluation function coefficients adaptively by combining environmental characteristics. Finally, the robots’ motion conflict is resolved by combining our obstacle avoidance and multi-robot priority strategies. The experimental results show that this algorithm can realize the cooperative obstacle avoidance of AGVs in unknown environments with high safety and global optimality, which can provide a technical reference for distributed multi-robot in practical applications.