Reviewerly/exHarmony huggingface dataset:
Towards Fair Federated Learning

Federated learning has become increasingly popular as it facilitates collaborative training of machine learning models among multiple clients while preserving their data privacy. In practice, one major challenge for federated learning is to achieve fairness in collaboration among the participating clients, because different clients' contributions to a model are usually far from equal due to various reasons. Besides, as machine learning models are deployed in more and more important applications, how to achieve model fairness, that is, to ensure that a trained model has no discrimination against sensitive attributes, has become another critical desiderata for federated learning. In this tutorial, we discuss formulations and methods such that collaborative fairness, model fairness, and privacy can be fully respected in federated learning. We review the existing efforts and the latest progress, and discuss a series of potential directions.
A data-scattering-preserving adaptive self-organizing map

We propose a novel improved adaptive version of the Self-Organizing Map (SOM). Our enhancement of SOM adapts to the input data scattering, in this way, preserving the principal input data structure. In order to capture and extract the data scattering from the input dataset, we propose carrying out a preliminary input data clustering. As a result of forming the clusters in the input data space, the inner-cluster variances are calculated. The inner-cluster variances are subsequently employed as the basis for the determination of the neighborhood widths of the SOM's Best Matching Units (BMUs). The method, introduced in this paper, has been a subject to the empirical evaluation carried out using the three real-world datasets of different size, dimensionality, and data type, representing three various experimental fields. Our proposal has been assessed on the basis of a comparison with seven reference data visualization approaches. The introduced method appeared to be superior over the remaining investigated data visualization techniques, and consequently, its effectiveness, usefulness, and accuracy have been verified and confirmed.
Learning Deformable and Attentive Network for image restoration

Image restoration (IR) aims to recover image quality from various degradations. Existing convolutional neural networks (CNN) based IR methods try to improve performance by enlarging the model receptive field with the sacrifice of fine spatial details and extra artifacts. This paper proposes a Deformable and Attentive Network (DANet) to address these problems. In DANet, we propose two novel blocks: Attentive DEformable-convolution Block (ADEB) and Attentive Recurrent Offset Block (AROB). In ADEB, deformable convolution is collaborated with various attention modules to generate more adaptive receptive fields. AROB transfers more attentive texture information among different scales during the encoding/decoding process for ADEB. To further refine DANet, we propose a knowledge distillation scheme to train a light-weighted DANet (DANet-S) with limited performance loss. Extensive experiments on several image benchmark datasets demonstrate that our method achieves state-of-the-art (SOTA) results for various IR tasks, including image denoising, JPEG artifacts removal, and real-world super resolution.
Optimization of deep neural network-based human activity recognition for a wearable device

Human activity recognition (HAR) attempts to classify performed activities from data retrieved from different sensors attached to the body. Most publications pertaining to HAR based on deep neural networks (DNNs) report the development of a suitable architecture to improve recognition accuracy by increasing the parameters of the architecture. Our work follows a different approach by attempting to optimise DNN-based HAR by reducing the dimensions of acceleration data, by finding a suitable sample size for processing by the DNN and by reducing the parameters of the proposed architecture. The experiments rely on employing two previously presented DNN-based HAR architectures as the baselines and starting points to create our candidate architectures. The variations in the dimensions of acceleration data, i.e., {xy, yz, xz, x, y, z}, and the sample size, i.e. {4, 6, 8} s duration, to these candidate architectures are experimented to produce the winner architecture which takes the shortest sample size and the minimal dimensions of acceleration data while preserving the recognition precision. The results indicate that despite the number of parameters is approximately half of the baseline architecture with two dimensions of acceleration data and shorter sample size (i.e., using a sample of 4 s duration instead of 8 s and only the xy axes of acceleration data), the resulting DNN-based HAR classifiers can produce comparable or better recognition precision than the baseline classifiers. The experimental results were obtained using three different popular datasets: the WISDM, the UCI HAR, and the Real World 2016. The proposed classifiers with optimised settings are useful as they require less processing time and reduce power consumption, both in terms of retrieving acceleration data from the sensor and the CPU processing time. Furthermore, they reduce the memory requirements for parameter storing and are suitable for incorporation in a wearable device.
"A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak

The COVID-19 pandemic has disrupted people's lives driving them to act in fear, anxiety, and anger, leading to worldwide racist events in the physical world and online social networks. Though there are works focusing on Sinophobia during the COVID-19 pandemic, less attention has been given to the recent surge in Islamophobia. A large number of positive cases arising out of the religious Tablighi Jamaat gathering has driven people towards forming anti-Muslim communities around hashtags like #coronajihad, #tablighijamaatvirus on Twitter. In addition to the online spaces, the rise in Islamophobia has also resulted in increased hate crimes in the real world. Hence, an investigation is required to create interventions. To the best of our knowledge, we present the first large-scale quantitative study linking Islamophobia with COVID-19.
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary

A major roadblock in machine learning for healthcare is the inability of data to be shared broadly, due to privacy concerns. Privacy preserving synthetic data generation is increasingly being seen as a solution to this problem. However, since healthcare data often has significant site-specific biases, it has motivated the use of federated learning when the goal is to utilize data from multiple sites for machine learning model training. Here, we introduce FELICIA (FEderated LearnIng with a CentralIzed Adversary), a generative mechanism enabling collaborative learning. It is a generalized extension of the (local) PrivGAN mechanism allowing to take into account the diversity (non-IID) nature of the federated sites. In particular, we show how a site with limited and biased data could benefit from other sites while keeping data from all the sources private. FELICIA works for a large family of Generative Adversarial Networks (GAN) architectures including vanilla and conditional GANs as demonstrated in this work. We show that by using the FELICIA mechanism, a site with a limited amount of images can generate high-quality synthetic images with improved utility, while none of the sites need to provide access to their real data. The sharing happens solely through a central discriminator with access limited to synthetic data. We demonstrate these benefits on several realistic healthcare scenarios using benchmark image datasets (MNIST, CIFAR-10) as well as on medical images for the task of skin lesion classification. We show that the utility of synthetic images generated by FELICIA surpasses that of the data available locally and we demonstrate that it can correct the reduced utility of a biased subgroup within a class.
VP-NIQE: An opinion-unaware visual perception natural image quality evaluator

The opinion-unaware (OU) blind image quality assessment (IQA) method shows broad application prospects due to the explosive growth of unlabelled images. Under a local quality perception framework, many works have focused on extracting more complex natural scene statistical (NSS) features to boost the performance. The framework, however, is seldom studied. In addition, NSS features are designed using the prior knowledge of the human visual system (HVS), which is insufficiently powerful for broad types of distortions. Therefore, this paper proposes a visual perception nature image quality evaluation (VP-NIQE) model. Specifically, we propose an understanding-based global–local structure IQA model to simulate the top-down structure of the HVS in image perception. Furthermore, we enriched the feature types from a more comprehensive perspective in local distortion detection. In addition to the NSS features, histogram and deep-learned features are incorporated to detect the distortions. NSS and histogram features represent the low-level prior knowledge of the HVS, while deep-learned features represent the high-level information behind the data. The proposed method is evaluated on six benchmark databases. The evaluation results demonstrate that the proposed method achieves state-of-the-art performance compared with existing OU methods and even generates competitive performance compared with classical full-reference methods.
Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies

Purpose The tourism and hospitality sectors are experiencing radical innovation boosted by the advancements in Information and Communication Technologies. Increasingly sophisticated chatbots are introducing novel approaches, re-shaping the dynamics among tourists and service providers, and fostering a remarkable behavioral change in the overall sector. Therefore, the objective of this paper is two-folded: (1) to highlight the academic and industrial standing points with respect to the current chatbots designed/deployed in the tourism sector and (2) to develop a proof-of-concept embodying the most prominent opportunities in the tourism sector. Design/methodology/approach This work elaborates on the outcomes of a Systematic Literature Review (SLR) and a Focus Group (FG) composed of experts from the tourism industry. Moreover, it presents a proof-of-concept relying on the outcomes obtained from both SLR and FG. Eventually, the proof-of-concept has been tested with experts and practitioners of the tourism sector. Findings Among the findings elicited by this paper, we can mention the quick evolution of chatbot-based solutions, the need for continuous investments, upskilling, system innovation to tackle the eTourism challenges and the shift toward new dimensions (i.e. tourist-to-tourist-to-chatbot and personalized multi-stakeholder systems). In particular, we focus on the need for chatbot-based activity and thematic aggregation for next-generation tourists and service providers. Originality/value Both academic- and industrial-centered findings have been structured and discussed to foster the practitioners' future research. Moreover, the proof-of-concept presented in the paper is the first of its kind, which raised considerable interest from both technical and business-planning perspectives.
Making smart contract development more secure and easier

With the rapid development of distributed applications, smart contracts have attracted more and more developers' attentions. However, developers or domain experts have different levels of familiarity with specific programming languages, like Solidity, and those vulnerabilities hidden in the code would be exploited and result in huge property losses. Existing auxiliary tools lack security considerations. Most of them only provide word completion based on fuzzy search and detection services for limited types of vulnerabilities, which results in the manpower waste during coding and potential vulnerability threats after deployment.
Differential evolution method integrated with a fuzzy decision-making mechanism and Virtual Mutant agent: Theory and application

The current study deals with upgrading the Differential Evolution (DE) method by equipping it with a novel fuzzy decision-making strategy and a new auxiliary agent called Virtual Mutant. The new algorithm is called Fuzzy Differential Evolution incorporated Virtual Mutant (FDEVM) The presented fuzzy strategy employs two nine-rule mapping mechanisms to adjust the internal parameters of the algorithm considering governing conditions of the current problem. Also, the Virtual Mutant providing reasonable attractive and compulsive effects on the other agents, plays important role in increasing the efficiency of the algorithm. These two new enhancements (i.e., adding the fuzzy module and Virtual Mutant) converts the conventional DE algorithm to a self-adaptive and parameter-free approach which can dynamically adjust its search behavior during the optimization process. Consequently, the search performance of the presented FDEVM is tested on a suite of unconstrained mathematical functions and constrained engineering problems. The outcomes are reported and compared with those obtained by six other well-established search techniques. The acquired results indicate that the proposed method could improve the search process in the terms of computational cost, stability and accuracy.
A Robust Data-Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

Internet of Things (IoT) is emerging, and 5G enables much more data transport from mobile and wireless sources. The data to be transmitted is too much compared to link capacity. Labelling data and transmit only useful part of the collected data or their features is a promising solution for this challenge. Abnormal data are valuable due to the need to train models and to detect anomalies when being compared to already overflowing normal data. Labelling can be done in data sources or edges to balance the load and computing between sources, edges, and centres. However, unsupervised labelling method is still a challenge preventing to implement the above solutions. Two main problems in unsupervised labelling are long-term dynamic multiseasonality and heteroscedasticity. This paper proposes a data-driven method to handle modelling and heteroscedasticity problems. The method contains the following main steps. First, raw data are preprocessed and grouped. Second, main models are built for each group. Third, models are adapted back to the original measured data to get raw residuals. Fourth, raw residuals go through deheteroscedasticity and become normalized residuals. Finally, normalized residuals are used to conduct anomaly detection. The experimental results with real-world data show that our method successfully increases receiver-operating characteristic (AUC) by about 30%.
Augmented Noise Learning Framework for Enhancing Medical Image Denoising

Deep learning attempts medical image denoising either by directly learning the noise present or via first learning the image content.We observe that residual learning (RL) often suffers from signal leakage while dictionary learning (DL) is prone to Gibbs (ringing) artifacts.In this paper, we propose an unsupervised noise learning framework that enhances denoising by augmenting the limitation of RL with the strength of DL and vice versa.To this end, we propose a ten-layer deep residue network (DRN) augmented with patch-based dictionaries.The input images are presented to patch-based DL to indirectly learn the noise via sparse representation while given to the DRN to directly learn the noise.An optimum noise characterization is captured by iterating DL/DRN network against proposed loss.The denoised images are obtained by subtracting the learned noise from available data.We show that augmented DRN effectively handles high-frequency regions to avoid Gibbs artifacts due to DL while augmented DL helps to reduce the overfitting due to RL. Comparative experiments with many state-of-the-arts on MRI and CT datasets (2D/3D) including low-dose CT (LDCT) are conducted on a GPU-based supercomputer.The ablation studies are carried out that demonstrate enhanced denoising performance with minimal signal leakage and least artifacts by proposed augmented approach.
Generalized Zero-Shot Learning Using Multimodal Variational Auto-Encoder With Semantic Concepts

With the ever-increasing amount of data, the central challenge in multimodal learning involves limitations of labelled samples For the task of classification, techniques such as meta-learning, zero-shot learning, and few-shot learning showcase the ability to learn information about novel classes based on prior knowledge. Recent techniques try to learn a cross-modal mapping between the semantic space and the image space. However, they tend to ignore the local and global semantic knowledge. To overcome this problem, we propose a Multimodal Variational Auto-Encoder (M-VAE) which can learn the shared latent space of image features and the semantic space. In our approach we concatenate multimodal data to a single embedding before passing it to the VAE for learning the latent space. We propose the use of a multi-modal loss during the reconstruction of the feature embedding through the decoder. Our approach is capable to correlating modalities and exploit the local and global semantic knowledge for novel sample predictions. Our experimental results using a MLP classifier on four benchmark datasets show that our proposed model outperforms the current state-of-the-art approaches for generalized zero-shot learning.
Deep Context-Encoding Network For Retinal Image Captioning

Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency. In this work, we propose a new context-driven encoding network to automatically generate medical reports for retinal images. The proposed model is mainly composed of a multi-modal input encoder and a fused-feature decoder. Our experimental results show that our proposed method is capable of effectively leveraging the interactive information between the input image and context, i.e., keywords in our case. The proposed method creates more accurate and meaningful reports for retinal images than baseline models and achieves state-of-the-art performance. This performance is shown in several commonly used metrics for the medical report generation task: BLEUavg (+16%), CIDEr (+10.2%), and ROUGE (+8.6%).
The dark side of informal institutions: How crime, corruption, and informality influence foreign firms' commitment

Abstract Research Summary This study focuses on three prevalent societal issues—crime, corruption, and informal sector—that constitute the dark side of informal institutions in developing countries. We argue that the dark side of informal institutions has the potential to impede foreign firms' desire and ability to commit to the host countries. The effects of these three forms on foreign firms differ depending on the type of local commitment. Analyzing the World Bank data of foreign firms in 36 developing countries, we find that (a) host country corruption is stronger in deterring foreign firms' long‐term investment, (b) host country informality is stronger in obstructing foreign firms' innovation output, and (c) host country crime is stronger in undermining foreign firms' production capacity utilization. Our analysis also shows that host country's efficient regulatory institutions and foreign firms' non‐market‐seeking motive are two important countervailing forces that attenuate the negative effects of the dark side of informal institutions. Managerial Summary Foreign firms investing in developing countries need to deal with some illegal yet widespread practices in these countries. Specifically, crime, corruption, and informal sector represent the most prevalent and important societal issues that exert informal constraints on foreign firms. This study examines how the negative informal institutions influence foreign firms. Our cross‐country analysis using the World Bank data shows that, among the three negative informal institutions, corruption has a stronger effect in deterring foreign firms' investment commitment, informal sector has a stronger effect in obstructing foreign firms' innovation commitment, and crime has a stronger effect in undermining foreign firms' production commitment. Moreover, these negative effects will be alleviated when the developing country has efficient regulatory institutions or when the foreign firm has a low market dependence on the developing country. Our findings provide implications for managers of multinational enterprises (MNEs) investing in developing countries and offer suggestions for policymakers on how to improve the institutional environment for foreign investment.
On the Experimental Feasibility of Quantum State Reconstruction via Machine Learning

We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on an IBM Q quantum computer, and compare against both unconstrained and constrained MLE state reconstruction.
Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning

Shill Bidding (SB) occurs when the fake bidders are introduced by the seller's side to increase the final price. SB is a crime committed during the e-Auction, and it is pretty difficult to detect because of its normal bidding behavior. The bidder gets a lot of loss because he pays extra money, and the sellers benefit from shill bidding, so this article proposed a fusion base model. This proposed model is split into two parts training and validation, into 70 and 30 percent. This model has been divided into three sub-modules; the first module, two machine learning algorithms named Support vector machine (SVM), and Artificial neural network (ANN) trained parallel on the same dataset and predicting the bidding fraud. The prediction of these models becomes the input of the fuzzy-based fussed module, and fuzzy decide the actual output based on SVM and ANN predictions. On every bid, it predicts whether the fraud is committed or not. If the bidding behavior is normal, continue the bidding; otherwise, cancel the bid and block the user. The prediction accuracy of the proposed fussed machine learning approach is 99.63%. Simulation results have shown that the proposed fussed machine learning approach gives more attractive results than state-of-the-art published methods.