Reviewerly/exHarmony huggingface dataset:
AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices

As data privacy becomes increasingly important, federated learning applied to the training of deep learning models while ensuring the data privacy of devices is entering the spotlight. Federated learning makes it possible to process all data at once while processing data independently from various devices without collecting distributed local data in a central server. However, there are still challenges to overcome for the system of devices in federated learning such as communication overheads and the heterogeneity of the system. In this paper, we propose the Adjusting Mini-Batch and Local Epoch (AMBLE) approach, which adaptively adjusts the local mini-batch and local epoch size for heterogeneous devices in federated learning and updates the parameters synchronously. With AMBLE, we enhance the computational efficiency by removing stragglers and scaling the local learning rate to improve the model convergence rate and accuracy. We verify that federated learning with AMBLE is a stably trained model with a faster convergence speed and higher accuracy than FedAvg and adaptive batch size scheme for both identically and independently distributed (IID) and non-IID cases.
An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques

Considering customer reviews is one of the challenges of real-world decision models. These reviews can be on different platforms and include a large amount of information and may also include incomprehensible and unrelated phrases. The main advantage of customer reviews is the realistic view that it provides a realistic view of the product or service. Therefore, converting unstructured and incoherent customer-based reviews into machine learning language and ultimately turning it into a decision model is very important. In this paper, we propose an end-to-end ranking method for integrating mechanisms such as text processing, sentiment analysis and the multi-criteria decision-making technique. The proposed ranking method relies on the integration of three methods, namely, the aspect-based sentiment analysis (ABSA) method, the Dawid-Skene algorithm and the Best Worst Method (BWM). In other words, the proposed work encompasses four major steps: i) crawling customer reviews, ii) preprocessing, iii) aspect term extraction, aspect category detection and polarity detection, and iv) designing a decision-making model. The main contribution of this study is to consider ABSA at three levels simultaneously and integrate ABSA and BWM in designing an end-to-end ranking method for ranking the quality of hotel services, facilities and amenities based on customer reviews. The ability of the proposed end-to-end ranking framework is evaluated using a real data set of user reviews of Sydney hotels.
Identification, cost evaluation, and prioritization of urban traffic congestions and their origin

The increasing urbanization in the last decades results in significant growth in urban traffic congestion around the world. This leads to enormous time people spent on roads and thus significant money waste and air pollution. Here, we present a novel methodology for identification, cost evaluation, and thus, prioritization of congestion origins, i.e., their bottlenecks. The presented work is based on network analysis of the entire road network from a global point of view. We identify and prioritize traffic bottlenecks based on big data of traffic speed retrieved in near-real-time. Our approach highlights the bottlenecks that have the most significant effect on the global urban traffic flow. We follow the evolution of every traffic congestion in the entire urban network and rank all the congestions, based on the cost they cause (in Vehicle Hours units). We show that the macro-stability that represents the seeming regularity of traffic load both in time and space, overshadows the existence of meso-dynamics, where the bottlenecks that create these congestions usually do not reappear on different days or hours. Thus, our method enables to identify in near-real-time both recurrent and nonrecurrent congestions and their sources.
Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using YOLOv3 algorithm

The eruption of COVID-19 pandemic has led to the blossoming usage of face masks among individuals in the communal settings. To prevent the transmission of the virus, a mandatory mask-wearing rule in public areas has been enforced. Owing to the use of face masks in communities at different workplaces, an effective surveillance seems essential because several security analyses indicate that face masks may be used as a tool to hide the identity. Therefore, this work proposes a framework for the development of a smart surveillance system as an aftereffect of COVID-19 for recognition of individuals behind the face mask. For this purpose, transfer learning approach has been employed to train the custom dataset by YOLOv3 algorithm in the Darknet neural network framework. Moreover, to demonstrate the competence of YOLOv3 algorithm, a comparative analysis with YOLOv3-tiny has been presented. The simulated results verify the robustness of YOLOv3 algorithm in the recognition of individuals behind the face mask. Also, YOLOv3 algorithm achieves a mAP of 98.73% on custom dataset, outperforming YOLOv3-tiny by approximately 62%. Moreover, YOLOv3 algorithm provides adequate speed and accuracy on small faces.
Survey of Graph Neural Networks and Applications

The advance of deep learning has shown great potential in applications (speech, image, and video classification). In these applications, deep learning models are trained by datasets in Euclidean space with fixed dimensions and sequences. Nonetheless, the rapidly increasing demands on analyzing datasets in non-Euclidean space require additional research. Generally speaking, finding the relationships of elements in datasets and representing such relationships as weighted graphs consisting of vertices and edges is a viable way of analyzing datasets in non-Euclidean space. However, analyzing the weighted graph-based dataset is a challenging problem in existing deep learning models. To address this issue, graph neural networks (GNNs) leverage spectral and spatial strategies to extend and implement convolution operations in non-Euclidean space. Based on graph theory, a number of enhanced GNNs are proposed to deal with non-Euclidean datasets. In this study, we first review the artificial neural networks and GNNs. We then present ways to extend deep learning models to deal with datasets in non-Euclidean space and introduce the GNN-based approaches based on spectral and spatial strategies. Furthermore, we discuss some typical Internet of Things (IoT) applications that employ spectral and spatial convolution strategies, followed by the limitations of GNNs in the current stage.
Multi-objective pruning of dense neural networks using deep reinforcement learning

Network pruning aims to reduce the inference cost of large models and enable neural architectures to run on end devices such as mobile phones. We present NEON, a novel iterative pruning approach using deep reinforcement learning (DRL). While most reinforcement learning-based pruning solutions only analyze the one network they aim to prune, we train a DRL agent on a large set of randomly-generated architectures. Therefore, our proposed solution is more generic and less prone to overfitting. To avoid the long-running times often required to train DRL models for each new dataset, we train NEON offline on multiple datasets and then apply it to additional datasets without additional training. This setup makes NEON more efficient than other DRL-based pruning methods. Additionally, we propose a novel reward function that enables users to clearly define their pruning/performance trade-off preferences. Our evaluation, conducted on a set of 28 diverse datasets, shows that the proposed method significantly outperforms recent top-performing solutions in the pruning of fully-connected networks. Specifically, our top configuration reduces the average size of the pruned architecture by ×24.59, compared to ×13.26 by the leading baseline, while actually improving accuracy by 0.5%.
Visualizing and quantifying small and nonstationary structural motions in video measurement

Abstract Rapid advancement in vision recording technologies is increasing the importance and production of video data in a wide range of applications. This paper proposed a novel perspective of multifrequency phase inference for characterizing especially challenging nonstationary and often small motions in optical measurement. The model estimates and adjusts the phase information by the multi‐frequency phase retrieval, which is derived from the maximum likelihood formulation with block matching 3D sparsity priors. Estimated phase jumps are removed by a robust solution of the 2D phase unwrapping problem. These considerations are supported by applications of dynamic response identification in structural health monitoring. When compared to state‐of‐the‐art techniques, the proposed method readily yielded high‐quality magnifications on real videos, with less noise and better anti‐noise performance. The proposed method also demonstrated uniformly high skill in extracting clearer time‐domain motion estimation of video components.
A hybrid model for classification of medical data set based on factor analysis and extreme learning machine: FA + ELM

Data mining techniques such as classification, clustering, and prediction are used extensively for medical diagnosis in epidemiological fields. A hybrid model based on Factor Analysis (FA) and Extreme Learning Machine (ELM) was proposed in this study for diagnosing breast cancer, Lymphography, and erythemato-squamous diseases. The proposed hybrid model consists of two stages. Firstly, FA was used for preprocessing the medical dataset, and then, the factors obtained using FA were used as input features for the ELM model. Dermatology, Lymphography, and Wisconsin Breast Cancer real datasets obtained from the UCI machine learning database were used to test the proposed model. An average success rate of 96.39 % and 96.94 % was obtained after classifying the dermatology dataset with ELM and FA + ELM models. While the success rate obtained by classifying the lymphography data set using ELM is 84.50 %, the result obtained with FA + ELM is 85.10 %. The success rates of 97.10 % and 97.25 % are achieved respectively for Wisconsin Breast Cancer (WBC) using ELM and FA + ELM. As a result, it was observed that preprocessing of the data increased the average classification success in three different medical datasets used for the classification problem. It is considered that the proposed hybrid model will be helpful for the decision-making stage in medical diagnosis systems.
Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms

A hybrid variant of the Fuzzy C-Means and K-Means algorithms is proposed to solve large datasets such as those presented in Big Data. The Fuzzy C-Means algorithm is sensitive to the initial values of the membership matrix. Therefore, a special configuration of the matrix can accelerate the convergence of the algorithm. In this sense, a new approach is proposed, which we call Hybrid OK-Means Fuzzy C-Means (HOFCM), and it optimizes the values of the membership matrix parameter. This approach consists of three steps: (a) generate a set of n solutions of an x dataset, applying a variant of the K-Means algorithm; (b) select the best solution as the basis for generating the optimized membership matrix; (c) resolve the x dataset with Fuzzy C-Means. The experimental results with four real datasets and one synthetic dataset show that HOFCM reduces the time by up to 93.94% compared to the average time of the standard Fuzzy C-Means. It is highlighted that the quality of the solution was reduced by 2.51% in the worst case.
Label prompt for multi-label text classification

Multi-label text classification has been widely concerned by scholars due to its contribution to practical applications. One of the key challenges in multi-label text classification is how to extract and leverage the correlation among labels. However, it is quite challenging to directly model the correlations among labels in a complex and unknown label space. In this paper, we propose a Label Prompt Multi-label Text Classification model (LP-MTC), which is inspired by the idea of prompt learning of pre-trained language model. Specifically, we design a set of templates for multi-label text classification, integrate labels into the input of the pre-trained language model, and jointly optimize by Masked Language Models (MLM). In this way, the correlations among labels as well as semantic information between labels and text with the help of self-attention can be captured, and thus the model performance is effectively improved. Extensive empirical experiments on multiple datasets demonstrate the effectiveness of our method. Compared with BERT, LP-MTC improved 3.4% micro-F1 on average over the four public datasets.
Sarve: synthetic data and local differential privacy for private frequency estimation

The collection of user attributes by service providers is a double-edged sword. They are instrumental in driving statistical analysis to train more accurate predictive models like recommenders. The analysis of the collected user data includes frequency estimation for categorical attributes. Nonetheless, the users deserve privacy guarantees against inadvertent identity disclosures. Therefore algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their values. We propose Sarve, a frequency oracle that used Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR) and Hadamard Response (HR) for randomization in combination with fake data. The design of a service-oriented architecture must consider two types of complexities, namely computational and communication. The functions of such systems aim to minimize the two complexities and therefore, the choice of privacy-enhancing methods must be a calculated decision. The variant of RAPPOR we had used was realized through bloom filters. A bloom filter is a memory-efficient data structure that offers time complexity of O(1). On the other hand, HR has been proven to give the best communication costs of the order of log(b) for b-bits communication. Therefore, Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original data. Sarve also implemented an adaptive solution enhanced from the work of Arcolezi et al. The use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacy regimes.
Domain Generalization for Activity Recognition via Adaptive Feature Fusion

Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in real applications, the training and testing datasets may have totally different distributions due to various reasons such as different body shapes, acting styles, and habits, damaging the model’s generalization performance. While such a distribution gap can be reduced by existing domain adaptation approaches, they typically assume that the test data can be accessed in the training stage, which is not realistic. In this article, we consider a more practical and challenging scenario: domain-generalized activity recognition (DGAR) where the test dataset cannot be accessed during training. To this end, we propose Adaptive Feature Fusion for Activity Recognition (AFFAR) , a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model’s generalization performance. AFFAR takes the best of both worlds where domain-invariant representations enhance the transferability across domains and domain-specific representations leverage the model discrimination power from each domain. Extensive experiments on three public HAR datasets show its effectiveness. Furthermore, we apply AFFAR to a real application, i.e., the diagnosis of Children’s Attention Deficit Hyperactivity Disorder (ADHD), which also demonstrates the superiority of our approach.
Is Alexa female, male, or neutral? A cross-national and cross-gender comparison of perceptions of Alexa's gender and status as a communicator

The gendering of machine agents risks further complicating the social framework in ways that reverberate to humans' relationships and identities. This paper explores how users perceive the gender and status of Amazon's Alexa. We argue that voice-based assistants, particularly those with feminine names and voices, may contribute to reinforcing a retrieval ideology of the feminine as the place of social subordination and contempt. We conducted an online survey of women and men in the US (n = 322) and Italy (n = 333). Most (80%) identified Alexa as "female." However, there was a lack of concordance between the gender respondents ascribed to Alexa and their spontaneous use of pronouns in writing. In terms of status, over half of the sample perceived Alexa as an inferior communicator (for Italian respondents, inferiority was associated with perceiving Alexa as female), while over one-third rated Alexa as equal or superior to humans, evidencing the change happening in the ontological order. The few respondents who noticed gender differences in people's interactions with Alexa perceived women to be more courteous, serious and accommodating in their use. Respondent gender and culture comparisons are presented, implications of the findings are discussed, and future research is proposed to reduce harmful impact.
BA-GNN: On Learning Bias-Aware Graph Neural Network

Graph Neural Networks (GNNs) show promising results for semi-supervised learning tasks on graphs, which become favorable comparing with other approaches. However, similar to other machine learning models, GNN s might suffer from the bias issue because of the distribution shift between training and testing node distributions. More importantly, the test node distribution in the graph is generally unknown during model training in practice. In this paper, we focus on how to address the bias issue on graphs and learn a graph neural network model that is robust to arbitrary unknown distribution shifts. To address this problem, we propose a novel Bias-Aware Graph Neural Network (BA-GNN) framework by learning node representations that are invariant across different distributions for invariant prediction. Specifically, our BA-GNN framework contains two interactive parts, one for bias identification and the other for invariant prediction. To learn invariant feature and aggregated representation, our BA-GNN learns multiple biased graph partitions and selects feature, neighbor, and propagation steps for nodes under multiple biased graph partitions. Extensive experiments show that our proposed BA-G NN framework can significantly improve different GNNs backbones such as GCN, GAT, APPNP and GraphSAGE on different datasets.
Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging

Prostate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting and data system (PI-RADS) is widely used for mpMRI scoring to detect PCa. There is low-level agreement among interpreters and also subjectivity associated with PI-RADS scoring. Hence, in this study, a hybrid model has been proposed to accurately interpret mpMRI examination and predict PI-RADS scores. In the proposed systems, feature maps of mpMR images were extracted using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. Then, the feature maps obtained using these three architectures were combined. The merged feature maps are subjected to neighborhood components analysis (NCA) to eliminate redundant features. The proposed system provided 96.09% accuracy.
EFINet: Restoration for Low-Light Images via Enhancement-Fusion Iterative Network

The lighting environment in the real world is so complex that most existing low-light image restoration methods suffer from color cast and local over-exposure. In order to solve these problems, this paper proposes the enhancement-fusion iterative network (EFINet) for low-light image enhancement. Within each iteration of EFINet, a stretching coefficient estimation network based enhancement module is designed to adjust the input image pixel-wisely to obtain the initial enhancement result with the estimated coefficient maps. Then, an encoder-decoder based fusion network is devised to extract the deep features and combine the well-exposed local areas in both the input image and the initially enhanced image, to obtain a visually pleasing, high-quality image enhancement result. The coefficient estimation network and the fusion network are weight shared among all iterations. What’s more, most of the low-light image datasets are generated through illumination reduction in a global way. To better simulate the diverse illumination distribution in the real world, we put forward a new low-light image synthesis method to produce the low-light images with non-uniform illumination for the network training purpose. After conducting extensive experiments on both synthetic and real-world low-light images, the results verify the superiority of our algorithm over the state-of-the-art (SOTA) methods, especially in balancing the brightness difference and preventing over-enhancement.
Evaluation and Application of College English Mixed Flipping Classroom Teaching Quality Based on the Fuzzy Judgment Model

With the development of big data technology, there are more and more evaluation models for college English teaching quality, which can better promote the improvement of college English teaching quality. Based on the fuzzy decision model, this paper describes the algorithm establishment process of the model in detail. Then, the fuzzy evaluation model between teachers and students according to different grades is established. Finally, aiming at the problems in college English teaching, this paper obtained the corresponding countermeasures for the college English teaching platform based on fuzzy judgment model. The results indicate the model improves the quality of college English teaching in different grades and promotes the development of college English. In addition, the model platform can also well predict the measures to improve the quality. In short, this paper provides some theoretical and experimental support for the quality of college English hybrid flipping classroom.