Reviewerly/exHarmony huggingface dataset:
The role of social influence and e-service quality in impacting loyalty for online life insurance: a SEM-based study

The aim of this research is to investigate the role of e-service quality, social influence and e-customer satisfaction on e-loyalty relationship. Data was collected from online life insurance policyholders in Delhi/NCR using structured questionnaires and in depth interviews and was then analysed and relationships were examined through structural equation modelling. Service quality and social influence were found to have substantial relationship with customer loyalty and satisfaction in online life insurance. E-customer satisfaction was found to have a mediating role in the relationship of customer loyalty with both service quality and social influence in online medium. The results of this study are estimated to offer significant contributions to academia, policy makers and facilitate a foundation for marketers to further examine various ways of interacting with customers that will aid in enhancing e-customer satisfaction leading to customer retention thereby improving overall business profits of the insurers.
Insulator Defect Detection Based on improved YOLOv5

As an important device for line support and electrical insulation in power system, Insulators play a pivotal role in the high-voltage lines of the power grid. To overcome the shortcomings of insulator aerial photography at present, this paper proposes a deep learning model based on improved YOLOV5 to realize insulator defect detection. Firstly, perform a data enhancement on the existing data set, thereby increasing the number of samples in the database, and making the generalization of the model reach a higher level. Secondly, the Triplet Attention modules introduced into the backbone network of YOLOV5, Improve the feature extraction of the network structure by establishing a certain spatial relationship between the residual transformation and the rotation operation. Thirdly, the Dense Block module is added to alleviate that problem of gradient disappearance and reduce the phenomenon of over-fitting, so as to achieve a good balance among accuracy, speed and storage space. Finally, By comparing the improved network structure with the existing two-stage network model and one-stage network model in terms of test speed, model accuracy and recall rate, the experimental results show that the accuracy rate of the improved network structure reaches 94.5%, and the recall rate reaches 93.1%. which has better detection effect for fuzzy insulators and minimal insulator faults.
dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but an analytic workflow involving local analysis undertaken at individual studies hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers.We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.
An Improved Whale Optimization Algorithm Based on Nonlinear Parameters and Feedback Mechanism

Abstract Whale optimization algorithm, as a relatively novel swarm-based intelligence optimization algorithm, has been extensively utilized in numerous scientific and engineering fields. The intent of this work was to devise a modified WOA based on multi-strategy, named MSWOA, to address somewhat deficiencies of the original WOA, such as converging slowly, stagnating at local minima and poor stability. First, a tent map function is adopted to optimize the distribution of the initial population in problem domain. Second, new iteration-based update strategies of convergence factor and inertia weight are constructed to regulate the balance between global and local search capabilities and improve the optimization ability. Additionally, an optimal feedback strategy is presented in the search for prey stage to enhance the global search ability. Numerical experimental results based on 24 test benchmark functions reveal that the proposed MSWOA significantly improves the standard WOA in terms of solution accuracy and convergence speed, and outperforms the comparison algorithms. Furthermore, the results show that the inertia weight strategy has the greatest effect on the performance of basic WOA performance, followed by the convergence factor, and then the optimal feedback strategy.
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems

Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs. However, they considered only a few settings (e.g., some combinations of source and target speakers), leaving many interesting and important settings in real-world attack scenarios alone. In this work, we present <small>AS2T</small> , the first attack in this domain which covers all the settings, thus allows the adversary to craft adversarial voices using arbitrary source and target speakers for any of three main recognition tasks. Since none of the existing loss functions can be applied to all the settings, we explore many candidate loss functions for each setting including the existing and newly designed ones. We thoroughly evaluate their efficacy and find that some existing loss functions are suboptimal. Then, to improve the robustness of <small>AS2T</small> towards practical over-the-air attack, we study the possible distortions occurred in over-the-air transmission, utilize different transformation functions with different parameters to model those distortions, and incorporate them into the generation of adversarial voices. Our simulated over-the-air evaluation validates the effectiveness of our solution in producing robust adversarial voices which remain effective under various hardware devices and various acoustic environments with different reverberation, ambient noises, and noise levels. Finally, we leverage <small>AS2T</small> to perform thus far the largest-scale evaluation to understand transferability among 14 diverse SRSs. The transferability analysis provides many interesting and useful insights which challenge several findings and conclusion drawn in previous works in the image domain. Our study also sheds light on future directions of adversarial attacks in the speaker recognition domain.
Chaos based image encryption security in cloud computing

In the rapid world of technology, the IT Industry is growing at a rapid speed with Cloud Image Security. In the developing trends, cloud orchestration is becoming impressive and uncertain of dealing a large amount of information. In the last two decades there has been extensive development in image encryption. Chaos based image encryption techniques form framework for a faster algorithm than traditional techniques. Chaos based techniques are highly efficient in case of multimedia data. The proposed algorithm uses the modified logistic map for the generation of chaotic sequence, encrypts the image in single scan, so runs faster and makes the proper use of confusion and diffusion. It dynamically updates the key during execution. This algorithm is secure against the brute force attack and differential attack.
Few-Shot Stance Detection via Target-Aware Prompt Distillation

Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios.
Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology

When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption. Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation. Our codes are available on https://github.com/superjamessyx/robustness_benchmark .
Contribution-Aware Federated Learning for Smart Healthcare

Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications. Existing deployed FL frameworks cannot address the key issues of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this paper, we report our experience developing and deploying the Contribution-Aware Federated Learning (CAFL) framework for smart healthcare. It provides an efficient and accurate approach to fairly evaluate FL participants' contribution to model performance without exposing their private data, and improves the FL model training protocol to allow the best performing intermediate models to be distributed to participants for FL training. Since its deployment in Yidu Cloud Technology Inc. in March 2021, CAFL has served 8 well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations 2.84 times faster than the best existing approach, and has improved the average accuracy of the resulting models by 2.62% compared to the previous system (which is significant in industrial settings). To our knowledge, it is the first contribution-aware federated learning successfully deployed in the healthcare industry.
Perceiving Stroke-Semantic Context: Hierarchical Contrastive Learning for Robust Scene Text Recognition

We introduce Perceiving Stroke-Semantic Context (PerSec), a new approach to self-supervised representation learning tailored for Scene Text Recognition (STR) task. Considering scene text images carry both visual and semantic properties, we equip our PerSec with dual context perceivers which can contrast and learn latent representations from low-level stroke and high-level semantic contextual spaces simultaneously via hierarchical contrastive learning on unlabeled text image data. Experiments in un- and semi-supervised learning settings on STR benchmarks demonstrate our proposed framework can yield a more robust representation for both CTC-based and attention-based decoders than other contrastive learning methods. To fully investigate the potential of our method, we also collect a dataset of 100 million unlabeled text images, named UTI-100M, covering 5 scenes and 4 languages. By leveraging hundred-million-level unlabeled data, our PerSec shows significant performance improvement when fine-tuning the learned representation on the labeled data. Furthermore, we observe that the representation learned by PerSec presents great generalization, especially under few labeled data scenes.
Max-Margin Contrastive Learning

Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence. We suspect this behavior is due to the suboptimal selection of negatives used for offering contrast to the positives. We counter this difficulty by taking inspiration from support vector machines (SVMs) to present max-margin contrastive learning (MMCL). Our approach selects negatives as the sparse support vectors obtained via a quadratic optimization problem, and contrastiveness is enforced by maximizing the decision margin. As SVM optimization can be computationally demanding, especially in an end-to-end setting, we present simplifications that alleviate the computational burden. We validate our approach on standard vision benchmark datasets, demonstrating better performance in unsupervised representation learning over state-of-the-art, while having better empirical convergence properties.
Explanation-guided fairness testing through genetic algorithm

The fairness characteristic is a critical attribute of trusted AI systems. A plethora of research has proposed diverse methods for individual fairness testing. However, they are suffering from three major limitations, i.e., low efficiency, low effectiveness, and model-specificity. This work proposes ExpGA, an explanation-guided fairness testing approach through a genetic algorithm (GA). ExpGA employs the explanation results generated by interpretable methods to collect high-quality initial seeds, which are prone to derive discriminatory samples by slightly modifying feature values. ExpGA then adopts GA to search discriminatory sample candidates by optimizing a fitness value. Benefiting from this combination of explanation results and GA, ExpGA is both efficient and effective to detect discriminatory individuals. Moreover, ExpGA only requires prediction probabilities of the tested model, resulting in a better generalization capability to various models. Experiments on multiple real-world benchmarks, including tabular and text datasets, show that ExpGA presents higher efficiency and effectiveness than four state-of-the-art approaches.
Socially-aware Dual Contrastive Learning for Cold-Start Recommendation

Social recommendation with Graph Neural Networks(GNNs) learns to represent cold users by fusing user-user social relations with user-item interactions, thereby alleviating the cold-start problem associated with recommender systems. Despite being well adapted to social relations and user-item interactions, these supervised models are still susceptible to popularity bias. Contrastive learning helps resolve this dilemma by identifying the properties that distinguish positive from negative samples. In its previous combinations with recommender systems, social relationships and cold-start cases in this context are not considered. Also, they primarily focus on collaborative features between users and items, leaving the similarity between items under-utilized. In this work, we propose socially-aware dual contrastive learning for cold-start recommendation, where cold users can be modeled in the same way as warm users. To take full advantage of social relations, we create dynamic node embeddings for each user by aggregating information from different neighbors according to each different query item, in the form of user-item pairs. We further design a dual-branch self-supervised contrastive objective to account for user-item collaborative features and item-item mutual information, respectively. On one hand, our framework eliminates popularity bias with proper negative sampling in contrastive learning, without extra ground-truth supervision. On the other hand, we extend previous contrastive learning methods to provide a solution to cold-start problem with social relations included. Extensive experiments on two real-world social recommendation datasets demonstrate its effectiveness.
Review for Meta-Heuristic Optimization Propels Machine Learning Computations Execution on Spam Comment Area Under Digital Security Aegis Region

In the redesign field, popularly known as optimization, such difficulties degenerate the exordium of smoothing out appraisals that performed well on benchmark cutoff focuses or essential setting-centered evaluations. To visually examine the commencement of techniques procured for smoothing out those conditions, scientists and experts felt a need to agnize the difficulties and break harmonious changes, modifications, and amendments in the evaluations to oversee such hardships. As of tardy, there has been actuating examination interest in orchestrating machine learning (ML) strategies into meta-heuristics for managing combinatorial smoothing out conditions. This joining betokens meta-heuristics towards a capable, abundant, and exuberant seek after. It also transmutes their exhibition much indistinguishably commensurate to procedure quality, cumulation rate, and energy. Since sundry getting procedures together with sundry purposes, we have incited an objective to review the early advances in utilizing ML techniques to revise meta-heuristics. To fill up this gap survey gives such an audit on the utilization of AI methods in the approach of sundry components of meta-heuristics for purposes behind computation winnows utilizing absolute execution limits. The adequacy of move and quantify is transmuted by refreshing ML evaluations using meta-heuristic redressment computations. In the review, seventeen ML-predicated evaluations we have applied for benchmarking datasets and conspicuous legitimate time tests for tasks and figures we have imparted. Pushing toward portions covers the energy-moving assessment subjects coordinating progressed ML-predicated seventeen evaluations execution and gives examiners some spellbinding pieces of erudition to utilize in their generous applications spaces of pay. This book region causes assembled well-kenned ML approaches to dissever the spam and ham comments. There may be so many obstacles in cyber security, such as Malware, Worm, viruses, SQL Injection, etc. One of them is Spam. It is a typical binary case where the output must be binary. We will examine from a large dataset through the meta-heuristic function, which one among 17 applied algorithms is the optimized one for the binary cases.
Design of a High Throughput Pseudorandom Number Generator Based on Discrete Hyper-Chaotic System

Chaotic systems with non-linear dynamic behaviors enable them to be potential candidates for pseudo-random number generators (PRNGs). Most PRNGs are capable of throughputs in the Gbps range. Here, we propose a discrete hyper-chaotic system with an embedded cross-coupled topological structure, which has two large positive Lyapunov exponents (LEs) exhibit a high level of complexity of chaotic dynamics. The chaotic sequences generated by ours show better randomness quality than the ones generated by the tent and 2D logistic maps when evaluated via Sample Entropy (SE) and Permutation Entropy (PE). Last but not the least, PRNG based on our system reveals good hardware resources utilization on field-programmable gate array (FPGA). The bits throughput of which is up to 10.04 Gbps, and over 3.62 Gbps larger than the highest level of designs by far. And all the generated bits have successfully passed the famous NIST SP800.22 and TestU01 without extra post-processing.
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents

At the heart of improving conversational AI is the open problem of how to evaluate conversations.Issues with automatic metrics are well known (Liu et al., 2016), with human evaluations still considered the gold standard.Unfortunately, how to perform human evaluations is also an open problem: differing data collection methods have varying levels of human agreement and statistical sensitivity, resulting in differing amounts of human annotation hours and labor costs.In this work we compare five different crowdworker-based human evaluation methods and find that different methods are best depending on the types of models compared, with no clear winner across the board.While this highlights the open problems in the area, our analysis leads to advice of when to use which one, and possible future directions.
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features

Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is the co-occurrence of data heterogeneity and long-tail distribution, which frequently appears in real FL applications. In this paper, we reveal an intriguing fact that the biased classifier is the primary factor leading to the poor performance of the global model. Motivated by the above finding, we propose a novel and privacy-preserving FL method for heterogeneous and long-tailed data via Classifier Re-training with Federated Features (CReFF). The classifier re-trained on federated features can produce comparable performance as the one re-trained on real data in a privacy-preserving manner without information leakage of local data or class distribution. Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data. Comparative results with the state-of-the-art FL methods also validate the superiority of CReFF. Our code is available at https://github.com/shangxinyi/CReFF-FL.