Reviewerly/exHarmony huggingface dataset:
Defending against adversarial attacks using spherical sampling-based variational auto-encoder

Although deep neural networks achieve outstanding performance in many tasks, adding very imperceptible perturbations to clean images can easily fool the deep neural network. In this paper, we propose a new defence model: Adversarial Memory Variational AutoEncoder(AdMVAE), that can be used to transform adversarial images into clean images. At inference time, it finds an output that is similar to a given image in a high probability region of the manifold space. And the memory module uses normal features to reconstruct the image in the process of reconstruction. It can effectively prevent the reconstruction of malicious perturbations and avoid defense failure. Our approach is a pre-processing module that does not change the results of the classifier. Therefore, it can be combined with other defence models to jointly improve the performance robustness of the classifier. The experimental results on three benchmark datasets including Fashion-MNIST, CIFAR10 and Imagenet show that the proposed method outperforms the state-of-the-art defense methods.
Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading

Abstract Cancer grade is an indicator of the aggressiveness of cancer. It is used for prognosis and treatment decisions. Conventionally cancer grading is performed manually by experienced pathologists via microscopic examination of pathology slides. Among the three factors involved in breast cancer grading (mitosis count, nuclear atypia, and tubule formation), mitotic cell counting is the most challenging task for pathologists. It is possible to automate this task by applying computational algorithms on pathology slides images. Lack of sufficiently large datasets and class imbalance between mitotic and non‐mitotic cells in slide images are the two major challenges in developing effective deep learning‐based methods for mitosis detection. In this paper, we propose a new approach and a method based on that to address these challenges. The high training data requirement of the advanced deep neural network is met by combining two datasets from different sources after a color‐normalization process. Class imbalance is addressed by the augmentation of the mitotic samples in a context‐preserving manner. Finally, a customized convolutional neural network classifier is used to classify the candidate cells into the target classes. We have used the publicly available datasets MITOS‐ATYPIA and MITOS for the experiments. Our method outperforms most of the recent methods that are based on independent datasets and at the same time offers adaptability to the combination of datasets from different sources.
Distortion Convolution Module for Semantic Segmentation of Panoramic Images Based on the Image-Forming Principle

Semantic segmentation of panoramic images plays a key role in many applications, such as security monitoring and autonomous driving. With the rapid development of deep learning, some deep networks are developed to segment panoramic images semantically. However, these networks don’t have the special modules to correct the image distortion of panoramic images according to the distortion principle, which makes feature extraction unreasonable during the convolution because of the image distortion. This article proposes a novel semantic segmentation network for panoramic images of outdoor scenes based on the distortion convolution. The network contains an encoder and a decoder. The encoder consists of a distortion convolutional module (DCM), a residual network, and an atrous spatial pyramid pooling (ASPP). The DCM is developed to correct the image distortion according to the image-forming principle. In the decoder, a deep feature aggregation network (DFAN) is designed to fully fuse low-level features with high-level features. The proposed network introduces the DCM and DFAN into the semantic segmentation of panoramic images, which improves the segmentation accuracy. The experiments demonstrate that the proposed network has good performance for different outdoor scenes and plays an important role in measurement applications.
A Research on Digital Violence in Social Media

The internet, which started to enter our lives with the last quarter of the 20th century, is being used more and more widely every day due to the facilitating effect of technological innovations on human life. Especially in the last 20 years, people have moved their social lives to the internet due to the fast and practical access to information, the diversity of opportunities it offers, the freedom to meet people from different parts of the world, and similar conveniences. In this new process, which is called the information society, there are many areas from social life to economy, from politics to health. However, this structure, which facilitates human life, has also brought with it negativities that can cause serious problems in interpersonal relations. All these negativities, which have a legal dimension, are described as the concept of “digital violence.”
Relational String Abstract Domains

In modern programming languages, more and more functionalities, such as reflection and data interchange, rely on string values. String analysis statically computes the set of string values that are possibly assigned to a variable, and it involves a certain degree of approximation. During the last decade, several abstract domains approximating string values have been introduced and applied to statically analyze programs. However, most of them are not precise enough to track relational information between string variables whose value is statically unknown (e.g., user input), causing the loss of relevant knowledge about their possible values. This paper introduces a generic approach to formalize relational string abstract domains based on ordering relationships. We instantiate it to several domains built upon different well-known string orders (e.g., substring). We implemented the domain based on the substring ordering into a prototype static analyzer for Go, and we experimentally evaluated its precision and performance on some real-world case studies.
Adversarial UV-Transformation Texture Estimation for 3D Face Aging

Face aging aims to estimate aged facial textures given a certain face image. A number of 2D face-aging methods have been developed, but there have been few studies on 3D face aging, which would be valuable in several real-world applications. The lack of 3D face-aging data has had a significant impact on the development of 3D face aging, but we hypothesized that the large amounts of 2D face-aging data on the internet could be leveraged for 3D aged facial textures. In this paper, we propose a novel 3D aging framework, which we call UV-transformation texture estimation based on generative adversarial networks (UVTE-GAN), to achieve 3D face aging. Specifically, the proposed framework has three parts: 1) a 3D vertex and texture estimator, which accurately estimates the face’s spatial vertices and textures; 2) a texture-aging GAN, which is responsible for aging the estimated texture map via adversarial learning; and 3) a 2D & 3D rendering rebuilder, which recovers 2D & 3D faces using the estimated facial vertex map and aged facial texture map. In addition, we also design a plugin layer that allows us to train the whole model in an end-to-end manner. Experimental results demonstrate the effectiveness of the proposed method in synthesizing visually pleasing 3D aged face pictures, and state-of-the-art performance is achieved on several public datasets.
DoubleQExt: Hardware and Memory Efficient CNN Through Two Levels of Quantization

To fulfil the tight area and memory constraints in IoT applications, the design of efficient Convolutional Neural Network (CNN) hardware becomes crucial. Quantization of CNN is one of the promising approach that allows the compression of large CNN into a much smaller one, which is very suitable for IoT applications. Among various proposed quantization schemes, Power-of-two (PoT) quantization enables efficient hardware implementation and small memory consumption for CNN accelerators, but requires retraining of CNN to retain its accuracy. This paper proposes a two-level post-training static quantization technique (DoubleQ) that combines the 8-bit and PoT weight quantization. The CNN weight is first quantized to 8-bit (level one), then further quantized to PoT (level two). This allows multiplication to be carried out using shifters, by expressing the weights in their PoT exponent form. DoubleQ also reduces the memory storage requirement for CNN, as only the exponent of the weights is needed for storage. However, DoubleQ trades the accuracy of the network for reduced memory storage. To recover the accuracy, a selection process (DoubleQExt) was proposed to strategically select some of the less informative layers in the network to be quantized with PoT at the second level. On ResNet-20, the proposed DoubleQ can reduce the memory consumption by 37.50% with 7.28% accuracy degradation compared to 8-bit quantization. By applying DoubleQExt, the accuracy is only degraded by 1.19% compared to 8-bit version while achieving a memory reduction of 23.05%. This result is also 1% more accurate than the state-of-the-art work (SegLog). The proposed DoubleQExt also allows flexible configuration to trade off the memory consumption with better accuracy, which is not found in the other state-of-the-art works. With the proposed two-level weight quantization, one can achieve a more efficient hardware architecture for CNN with minimal impact to the accuracy, which is crucial for IoT applications.
Classification in Dynamic Data Streams With a Scarcity of Labels

Ensemble techniques are a powerful method for recognising and reacting to changes in non-stationary data. However, most researches into dynamic classification with ensembles assume that the true class label of each incoming point is available or easily obtained. This is unrealistic in most practical applications, especially in high-velocity streams where manually labeling each point is prohibitively expensive. To address this challenge, this paper proposes an algorithm, named Clustering and One-Class Classification Ensemble Learning (COCEL), which incorporates a stream clustering algorithm and an ensemble of one-class classifiers with active learning, for classification in dynamic data streams. The method exploits the intuitive relationship between clusters and one-class classifiers to cope with a small training set (or no training set) and improve with experience, self-modifying its internal state to cope with changes in the data stream. The proposed method is evaluated on synthetic data streams exhibiting concept evolution and concept drift and a collection of high-velocity real data streams where manually labeling each incoming point is infeasible or expensive and labor intensive. Finally, a comparative evaluation with peer stream classification ensembles shows that COCEL can achieve superior or comparative accuracy while typically requiring less than 0.01% of the stream labels.
SENGR: Sentiment-Enhanced Neural Graph Recommender

In recent years, reviews and user-item interaction have been recognized as valuable information to improve representation learning abilities in recommendations. However, on the one hand, the existing review-based recommendations normally ignore the importance of sentiment words regarding the corresponding aspect words, which reflect user preference for the item aspect. On the other hand, when modeling interaction, both user-user and user-item interactions should be considered. To solve these issues, in this paper, we propose a novel sentiment-enhanced neural graph recommender by incorporating the information derived from both textual reviews and bipartite graph. Specifically, we first design a hierarchically structured attention mechanism with a sentiment auxiliary task to help the recommendation task learn user preference for different aspects of items from reviews, where the co-attention mechanism is used to select important item/user reviews for the current user/item. Second, we construct a user-item interaction graph to capture preference-based user-item interaction with social-based user-user interaction, where the graph convolutional network is used to simulate the diffusion of information. Finally, we adopt a Factorization Machine model to accomplish the recommendation task. The experimental results demonstrate that our model significantly outperforms the related approaches w.r.t. rating prediction accuracy on Yelp and Amazon datasets.
ASTRAEA: Grammar-based Fairness Testing

Software often produces biased outputs.In particular, machine learning (ML) based software is known to produce erroneous predictions when processing discriminatory inputs.Such unfair program behavior can be caused by societal bias.In the last few years, Amazon, Microsoft and Google have provided software services that produce unfair outputs, mostly due to societal bias (e.g.gender or race).In such events, developers are saddled with the task of conducting fairness testing.Fairness testing is challenging; developers are tasked with generating discriminatory inputs that reveal and explain biases.We propose a grammar-based fairness testing approach (called ASTRAEA) which leverages context-free grammars to generate discriminatory inputs that reveal fairness violations in software systems.Using probabilistic grammars, ASTRAEA also provides fault diagnosis by isolating the cause of observed software bias.ASTRAEA's diagnoses facilitate the improvement of ML fairness.ASTRAEA was evaluated on 18 software systems that provide three major natural language processing (NLP) services.In our evaluation, ASTRAEA generated fairness violations at a rate of about 18%.ASTRAEA generated over 573K discriminatory test cases and found over 102K fairness violations.Furthermore, ASTRAEA improves software fairness by about 76% via model-retraining, on average.
Key Frame Extraction Method of Music and Dance Video Based on Multicore Learning Feature Fusion

The purpose of video key frame extraction is to use as few video frames as possible to represent as much video content as possible, reduce redundant video frames, and reduce the amount of computation, so as to facilitate quick browsing, content summarization, indexing, and retrieval of videos. In this paper, a method of dance motion recognition and video key frame extraction based on multifeature fusion is designed to learn the complicated and changeable dancer motion recognition. Firstly, multiple features are fused, and then the similarity is measured. Then, the video sequences are clustered by the clustering algorithm according to the scene. Finally, the key frames are extracted according to the minimum amount of motion. Through the quantitative analysis and research of the simulation results of different models, it can be seen that the model proposed in this paper can show high performance and stability. The breakthrough of video clip retrieval technology is bound to effectively promote the inheritance and development of dance, which is of great theoretical significance and practical value.
Towards Explaining Metaheuristic Solution Quality by Data Mining Surrogate Fitness Models for Importance of Variables

Metaheuristics are randomised search algorithms that are effective at finding “good enough” solutions to optimisation problems. However, they present no justification for generated solutions and these solutions are non-trivial to analyse in most cases. We propose that identifying the combinations of variables that strongly influence solution quality, and the nature of this relationship, represents a step towards explaining the choices made by a metaheuristic. Using three benchmark problems, we present an approach to mining this information by using a “surrogate fitness function” within a metaheuristic. For each problem, rankings of the importance of each variable with respect to fitness are determined through sampling of the surrogate model. We show that two of the three surrogate models tested were able to generate variable rankings that agree with our understanding of variable importance rankings within the three common binary benchmark problems trialled.
A Blockchain Based Transparent Framework for Plastic Waste Management

A massive amount of plastic waste is generated every day. However, a very small portion of it gets recycled, primarily due to improper waste management and lack of access to the plastic recycling infrastructure. Hence, we position that plastic waste, if handled carefully and in a timely manner, the percentage of recycled plastic waste can be significantly increased. Such recycling can transform used plastic objects into plastic bricks, plastic plant pots, plastic fibres, or social plastics. A few organizations do these recycling jobs; however, they need a vast amount of plastic materials to be made available in a timely manner for effective recycling – noteworthy a need for an effective and transparent supply-chain system. Hence, we propose building a Blockchain-based decentralized application through which these organizations can collect plastics from anyone and track them in a timely manner. Further, transparency of the process and associated rewards system (non-monetary) for Blockchain transactions can greatly incentivize the users to recycle the plastics. Thus, a Blockchain-based distributed management system with a right incentive structure can help bridge the gap between users and organizations, and help towards reducing the footprint of plastic waste.
Afterwords: Considering the Postgraduate, Postdigital and Postcritical

I explore four topics relevant to research and practice in online postgraduate education. The first concerns the historic neglect of taught postgraduate education by higher education researchers and the challenges this creates for discussions of practice. For example, there is little consensus about how postgraduate and undergraduate courses and students differ, and whether research on undergraduate education generalises to the postgraduate level. I then raise some questions about three lines of analysis that are salient in other chapters of the book, and in wider academic writing on online and higher education. First, I ask whether the tumbril labelled ‘learnification’ is as pleasing a mode of transport as those now pushing it appear to think. Renewed attention to teaching, and celebration of its importance and difficulty, is timely, but is best accompanied by serious, continuing attention to learning. The two need not be in competition. Second, I argue for framing teaching, design and other educational work, as situated, proposing that this has implications for what educational workers, at all levels, can do in those segments of time when consequential change is possible. Third, I suggest that we need to untangle sociomaterial analysis from the temptation to debunk—especially where the debunking is aimed at outcomes of sincere educational work by our peers. The chapter plays a little with the traps and affordances of language, and weaves in some skeins of autoethnography, suggesting there are many ways we can learn from each other’s writing, if we so choose.
RHMC: Modeling consistent information from deep multiple views via Regularized and Hybrid Multiview Coding

Modeling multiple views by unsupervised representation learning is a challenging problem, since multi-view data features are different from each other, and it is unclear how to perform representation learning to fully explore discriminative information from multiple views. Recent works focus on learning consistent information by contrasting high-dimensional features, but amounts of useful view-consistent information of low-dimensional representation is ignored, and such information can benefit the learned representation on downstream tasks. In this paper, we propose a novel method called Regularized and Hybrid Multiview Coding (RHMC) for comprehensively modeling consistent information between multiple views. We induce the discriminative improvement of features by constraining the learned multi-view representations in the latent space with diversified self-supervised learning tasks. Specifically, RHMC proposes a hybrid MI estimation, which jointly considers the mutual information between the aggregated high-dimensional feature and low-dimensional representation of views. RHMC enables the learned representation to model the consistent information that is hidden in low-dimensional representations under the direction of the aggregated high-dimensional feature. In hidden space, the problem of multi-view domain shift degenerates the performance of mutual information estimation. To tackle the issue, RHMC imposes globally aligned structures of the learned representations in the latent space, that is aligning the probability distribution of different views by utilizing the Wasserstein distance-based view alignment regularization. Empirically, our experimental validation supports the ability of RHMC to outperform the state-of-the-art self-supervised methods by a significant margin, which proves that RHMC can indeed model consistent and discriminative information from multi-view data.
Kernel-Induced Possibilistic Fuzzy Associate Background Subtraction for Video Scene

The background subtraction (BGS) technique is popularly used for many surveillance systems, segmenting the foreground by subtracting the modeled background from the image sequences. The effectiveness of any BGS technique depends on the robustness of the constructed background model. It is to be noted that many BGS schemes are affected by the inclusion of either noisy pixels in background construction or parameters of generative models. In this regard, we propound an idea of a kernel-induced possibilistic fuzzy associated BGS scheme for local change detection from a fixed camera captured sequence. The proposed scheme follows two stages: background training and foreground segmentation. In the background construction stage, each pixel is modeled using a possibilistic fuzzy cost function in kernel-induced space. The use of the induced kernel function will project the low-dimensional data into a higher dimensional space and the use of the possibilistic function will construct a robust background model based on the density of the data in the temporal domain avoiding the noisy and outlier points. The performance of the proposed scheme is tested on three benchmark databases. The effectiveness of the proposed scheme is evaluated on different performance evaluation measures: precision, recall, F-measure, and average similarity. We corroborate our findings by comparing them against 19 state-of-the-art existing BGS techniques.