Reviewerly/exHarmony huggingface dataset:
MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images

One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. Our code is available at: \url{https://github.com/yaxingwang/MineGAN}.
Bayesian networks in healthcare: Distribution by medical condition

Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare. Hitherto, research works have not investigated the types of medical conditions being modelled with BNs, nor whether there are any differences in how and why they are applied to different conditions. This research seeks to identify and quantify the range of medical conditions for which healthcare-related BN models have been proposed, and the differences in approach between the most common medical conditions to which they have been applied. We found that almost two-thirds of all healthcare BNs are focused on four conditions: cardiac, cancer, psychological and lung disorders. We believe there is a lack of understanding regarding how BNs work and what they are capable of, and that it is only with greater understanding and promotion that we may ever realise the full potential of BNs to effect positive change in daily healthcare practice.
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness

Face image quality is an important factor to enable high-performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for the purpose of recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mechanisms are error prone as they do not rely on a clear definition of quality and may not know the best characteristics for the utilized face recognition system. Avoiding the use of inaccurate quality labels, we proposed a novel concept to measure face quality based on an arbitrary face recognition model. By determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated. The experiments are conducted in a cross-database evaluation setting on three publicly available databases. We compare our proposed solution on two face embeddings against six state-of-the-art approaches from academia and industry. The results show that our unsupervised solution outperforms all other approaches in the majority of the investigated scenarios. In contrast to previous works, the proposed solution shows a stable performance over all scenarios. Utilizing the deployed face recognition model for our face quality assessment methodology avoids the training phase completely and further outperforms all baseline approaches by a large margin. Our solution can be easily integrated into current face recognition systems, and can be modified to other tasks beyond face recognition.
Complex-Valued Networks for Automatic Modulation Classification

Deep learning (DL) has been recognized as an effective solution for automatic modulation classification (AMC). However, most recent DL based AMC works are based on real-valued operations and representations. In this correspondence, we aim to demonstrate the high potential of complex-valued networks for AMC. We present the design of several key building blocks for complex-valued networks, such as complex convolution, complex batch-normalization, complex weight initialization, and complex dense strategies. We then provide a comparison study of three different neural network models and their complex-valued counterparts using the RadioML 2016.10 A dataset. Our results validate the superior performance in AMC achieved by the complex-valued networks.
Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. Feature selection is a popular problem in Machine learning with the goal of finding optimal features with increase accuracy. As a result, several studies have been conducted on multi-objective feature selection through numerous multi-objective techniques and algorithms. The objective of this paper is to present a systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem. The conducted review covered all related studies published since 2012 up to 2019. The outcomes of the reviewed of these studies clearly showed that no perfect solution to the multi-objective feature selection problem yet. The authors believed that the conducted review would serve as the main source of the techniques and methods used to resolve the problem of multi-objective feature selection. Furthermore, current challenges and issues are deliberated to find promising research domains for further study.
Regulatory, safety, and privacy concerns of home monitoring technologies during COVID-19

There has been increasing interest in the use of home monitoring technologies during the COVID-19 pandemic to decrease interpersonal contacts and the resultant risks of exposure for people to the coronavirus SARS-CoV-2. This Perspective explores how the accelerated development of these technologies also raises major concerns pertaining to safety and privacy. We make recommendations for needed interventions to ensure safety and review best practices and US regulatory requirements for privacy and security. We discuss, among other topics, Emergency Use Authorizations for medical devices and privacy laws of the USA and Europe. Home monitoring technologies are being rushed to market during the COVID-19 pandemic; here, safety and privacy considerations are discussed.
Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing

Federated learning (FL) is a promising new technology in the field of IoT intelligence. However, exchanging model-related data in FL may leak the sensitive information of participants. To address this problem, we propose a novel privacy-preserving FL framework based on an innovative chained secure multiparty computing technique, named chain-PPFL. Our scheme mainly leverages two mechanisms: 1) single-masking mechanism that protects information exchanged between participants and 2) chained-communication mechanism that enables masked information to be transferred between participants with a serial chain frame. We conduct extensive simulation-based experiments using two public data sets (MNIST and CIFAR-100) by comparing both training accuracy and leak defence with other state-of-the-art schemes. We set two data sample distributions (IID and NonIID) and three training models (CNN, MLP, and L-BFGS) in our experiments. The experimental results demonstrate that the chain-PPFL scheme can achieve practical privacy preservation (equivalent to differential privacy with ∈ approaching zero) for FL with some cost of communication and without impairing the accuracy and convergence speed of the training model.
Image denoising using complex-valued deep CNN

While complex-valued transforms have been widely used in image processing and have their deep connections to biological vision systems, complex-valued convolutional neural networks (CNNs) have not seen their applications in image recovery. This paper aims at investigating the potentials of complex-valued CNNs for image denoising. A CNN is developed for image denoising with its key mathematical operations defined in the complex number field to exploit the merits of complex-valued operations, including the compactness of convolution given by the tensor product of 1D complex-valued filters, the nonlinear activation on phase, and the noise robustness of residual blocks. The experimental results show that, the proposed complex-valued denoising CNN performs competitively against existing state-of-the-art real-valued denoising CNNs, with better robustness to possible inconsistencies of noise models between training samples and test images. The results also suggest that complex-valued CNNs provide another promising deep-learning-based approach to image denoising and other image recovery tasks.
FPGAN: Face de-identification method with generative adversarial networks for social robots

In this paper, we propose a new face de-identification method based on generative adversarial network (GAN) to protect visual facial privacy, which is an end-to-end method (herein, FPGAN). First, we propose FPGAN and mathematically prove its convergence. Then, a generator with an improved U-Net is used to enhance the quality of the generated image, and two discriminators with a seven-layer network architecture are designed to strengthen the feature extraction ability of FPGAN. Subsequently, we propose the pixel loss, content loss, adversarial loss functions and optimization strategy to guarantee the performance of FPGAN. In our experiments, we applied FPGAN to face de-identification in social robots and analyzed the related conditions that could affect the model. Moreover, we proposed a new face de-identification evaluation protocol to check the performance of the model. This protocol can be used for the evaluation of face de-identification and privacy protection. Finally, we tested our model and four other methods on the CelebA, MORPH, RaFD, and FBDe datasets. The results of the experiments show that FPGAN outperforms the baseline methods.
Difficulties of Online Physical Education Classes in Middle and High School and an Efficient Operation Plan to Address Them

This study examined the difficulties of running online physical education classes in the context of coronavirus disease 2019 (COVID-19) and used the findings to develop an efficient operation plan to address these difficulties. Six middle and high school physical education teachers participated; three were experts in online physical education and active in the Korea Council School Physical Education Promotion, and three were recommended teachers making efforts to improve the online classes offered by the Korea Ministry of Education. A qualitative case study method employing phenomenological procedures to collect and analyze the data was used. The difficulties of operating middle and high school online physical education classes for the first time included (1) the monotony of the classes within their limited environmental conditions and limited educational content that did not adequately convey the value of physical education, (2) trial-and-error methods applied nationwide, resulting from a lack of expertise in operating online physical education classes, and (3) very limited evaluation guidelines proposed by the Korea Ministry of Education, which made systematic evaluation with online methods impossible. To address the identified problems and facilitate the efficient operation of online physical education classes, changes in strategic learning methods are needed to understand online physical education characteristics and thereby better communicate the value of physical education. It is also necessary to cultivate teaching expertise through sharing online physical education classes, where collaboration among physical education teachers is central. In addition, evaluation processes should be less formal to encourage active student participation.
Real-Time Facemask Recognition with Alarm System using Deep Learning

In the background of the COVID-19 pandemic, institutions such as the academy suffer a great deal from practically closed globally if the current situation is not going to change. COVID-19 also known as Serious Acute Respiratory Syndrome Corona virus-2 is an infectious disease that is released from an infected sick person who speaks, sneezes, or coughs by respiratory droplets. This spreads quickly through close contact with anyone infected, or by touching objects or surfaces affected with a virus. There's still currently no vaccine available to protect against COVID-19 and preventing exposure to the virus seems to be the only method to safeguard ourselves. Wearing a facemask that covers the nose and mouth in a public setting and often washing hands or the use of at least 70% alcohol-based sanitizers is one way to avoid being exposed to the virus. Amid the advancement of technology, Deep Learning has proven its effectiveness in recognition and classification through image processing. The research study uses deep learning techniques in distinguishing facial recognition and recognize if the person is wearing a facemask or not. The dataset collected contains 25,000 images using 224x224 pixel resolution and achieved an accuracy rate of 96% as to the performance of the trained model. The system develops a Raspberry Pi-based real-time facemask recognition that alarms and captures the facial image if the person detected is not wearing a facemask. This study is beneficial in combating the spread of the virus and avoiding contact with the virus.
Feature Space Augmentation for Long-Tailed Data

Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably homogeneous performances across classes. Introducing class-balanced loss and advanced methods on data re-sampling and augmentation are among the best practices to alleviate the data imbalance problem. However, the other part of the problem about the under-represented classes will have to rely on additional knowledge to recover the missing information.In this work, we present a novel approach to address the long-tailed problem by augmenting the under-represented classes in the feature space with the features learned from the classes with ample samples. In particular, we decompose the features of each class into a class-generic component and a class-specific component using class activation maps. Novel samples of under-represented classes are then generated on the fly during training stages by fusing the class-specific features from the under-represented classes with the class-generic features from confusing classes. Our results on different datasets such as iNaturalist, ImageNet-LT, Places-LT and a long-tailed version of CIFAR have shown the state of the art performances.
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks — such as sentiment polarity classification — and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field necessary to attain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">true</i> sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions.
SuperPlotsOfData—a web app for the transparent display and quantitative comparison of continuous data from different conditions

SuperPlotsOfData is a web tool for the transparent display and quantitative comparison of continuous data from different conditions. It offers easy and open access to state-of-the-art data visualization. In addition, it incorporates recent innovations in data visualization and analysis, including raincloud plots and estimation statistics.
An attention‐based category‐aware GRU model for the next POI recommendation

With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based category-aware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.
The future of service: The power of emotion in human-robot interaction

Astoundingly, recent technological advancements have enabled robots to display emotions. Yet, while emotional expression is valued in the field of service, understanding emotions in human-robot interaction remains underexplored. Since emotions are contagious/transmittable, this study utilised Instagram data to uncover how emotional robots influence potential consumers’ affective feelings. By employing machine learning algorithms and sentiment analysis, the findings suggest that the expressions of surprise and happiness are key to creating positive impacts on potential consumers. The cross-disciplinary nature of this study lays the groundwork for next-level social, design, and creative experiences in artificial intelligence research regarding consumer service and experience contexts.
Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition

Action recognition via 3D skeleton data is an emerging important topic. Most existing methods rely on hand-crafted descriptors to recognize actions, or perform supervised action representation learning with massive labels. In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that exploits different augmentations of unlabeled skeleton sequences to learn action representations in an unsupervised manner. Specifically, we first propose to contrast similarity between augmented instances of the input skeleton sequence, which are transformed with multiple novel augmentation strategies, to learn inherent action patterns (“pattern-invariance”) in different skeleton transformations. Second, to encourage learning the pattern-invariance with more consistent action representations, we propose a momentum LSTM, which is implemented as the momentum-based moving average of LSTM based query encoder, to encode long-term action dynamics of the key sequence. Third, we introduce a queue to store the encoded keys, which allows flexibly reusing proceeding keys to build a consistent dictionary to facilitate contrastive learning. Last, we propose a novel representation named Contrastive Action Encoding (CAE) to represent human’s action effectively. Empirical evaluations show that our approach significantly outperforms hand-crafted methods by 10–50% Top-1 accuracy, and it can even achieve superior performance to many supervised learning methods (Our codes are available athttps://github.com/Mikexu007/AS-CAL).
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation

Public resources and services (e.g., datasets, training platforms, pre-trained models) have been widely adopted to ease the development of Deep Learning-based applications. However, if the third-party providers are untrusted, they can inject poisoned samples into the datasets or embed backdoors in those models. Such an integrity breach can cause severe consequences, especially in safety- and security-critical applications. Various backdoor attack techniques have been proposed for higher effectiveness and stealthiness. Unfortunately, existing defense solutions are not practical to thwart those attacks in a comprehensive way.
Shopping in virtual reality: A literature review and future agenda

Virtual reality (VR) refers to technologies for substituting the perceived reality. With the recent proliferation of consumer-grade head-mounted VR displays, several industries have started to wake up to the possible potential of virtual reality. One typical area in the early stages of the adoption of these technologies is marketing, and especially its sub-areas of retail and shopping. However, there has been a dearth in our understanding of how VR technology has been investigated in retail-related literature. This study systematically reviewed a body of 72 research papers that investigated the application of VR in shopping. The corpus was reviewed for covered research methods, theories, investigated output and input devices, tracking technologies, products and simulated environments, antecedents and consequences. Based on experiment-based studies in the corpus, we assessed the effects of VR and related stimuli on consumer psychology and behavior in the context of shopping. Based on the review, we propose 16 future research avenues pertaining to concepts, themes, methodologies and technologies.