Reviewerly/exHarmony huggingface dataset:
Deep convolutional neural networks for mammography: advances, challenges and applications

The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions.In this survey, we conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images. It summarizes 83 research studies for applying CNNs on various tasks in mammography. It focuses on finding the best practices used in these research studies to improve the diagnosis accuracy. This survey also provides a deep insight into the architecture of CNNs used for various tasks. Furthermore, it describes the most common publicly available MG repositories and highlights their main features and strengths.The mammography research community can utilize this survey as a basis for their current and future studies. The given comparison among common publicly available MG repositories guides the community to select the most appropriate database for their application(s). Moreover, this survey lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images. In addition, other listed techniques like transfer learning (TL), data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting and increase the generalization of the CNN models. Finally, this survey identifies the research challenges and directions that require further investigations by the community.
Decentralized On-Demand Energy Supply for Blockchain in Internet of Things: A Microgrids Approach

Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.
Meta-Sim: Learning to Generate Synthetic Datasets

Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task. We propose Meta-Sim, which learns a generative model of synthetic scenes, and obtain images as well as its corresponding ground-truth via a graphics engine. We parametrize our dataset generator with a neural network, which learns to modify attributes of scene graphs obtained from probabilistic scene grammars, so as to minimize the distribution gap between its rendered outputs and target data. If the real dataset comes with a small labeled validation set, we additionally aim to optimize a meta-objective, i.e. downstream task performance. Experiments show that the proposed method can greatly improve content generation quality over a human-engineered probabilistic scene grammar, both qualitatively and quantitatively as measured by performance on a downstream task.
TextDragon: An End-to-End Framework for Arbitrary Shaped Text Spotting

Most existing text spotting methods either focus on horizontal/oriented texts or perform arbitrary shaped text spotting with character-level annotations. In this paper, we propose a novel text spotting framework to detect and recognize text of arbitrary shapes in an end-to-end manner, using only word/line-level annotations for training. Motivated from the name of TextSnake, which is only a detection model, we call the proposed text spotting framework TextDragon. In TextDragon, a text detector is designed to describe the shape of text with a series of quadrangles, which can handle text of arbitrary shapes. To extract arbitrary text regions from feature maps, we propose a new differentiable operator named RoISlide, which is the key to connect arbitrary shaped text detection and recognition. Based on the extracted features through RoISlide, a CNN and CTC based text recognizer is introduced to make the framework free from labeling the location of characters. The proposed method achieves state-of-the-art performance on two curved text benchmarks CTW1500 and Total-Text, and competitive results on the ICDAR 2015 Dataset.
MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification

This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.
Blockchain in transport and logistics – paradigms and transitions

Blockchain – also known as distributed ledger – technology is set to revolutionise data and business process management and transactions. Blockchain adoption, pioneered initially as a financial tec...
Digital technology enablers and their implications for supply chain management

Several technologies drive digital trends and bring about change in SCM. They include AI and robotics, cloud computing, 3D printing, advanced analytics, blockchain, AR, RFID, IoT, and cloud technology. These technologies are changing the face of the industry, transforming many aspects of business models, supply chains, products, sales, and services. This paper conducts a literature review that aims to identify current research and directions in terms of how these technologies can enable or enhance and streamline digital supply chain (DSC) performance. This paper underscores the importance of these technologies for supply chains and logistics, examines their trends and challenges in supporting digital supply chain performance, and explores the implementation and managerial challenges of creating a new integrated paradigm of the digital supply chain. The research will also explore potential barriers to digitising procurement and supply chains and ways to overcome them. Finally, the paper reviews the role of 5 G as a significant technology for growing industry digitisation.
Technology assessment of blockchain-based technologies in the food supply chain

Beyond the financial domain, the application of blockchain is highly relevant in supply chain traceability, but the trade-offs between implementation challenges and achievable impact remain unclear. In this study, six cases of blockchain-based technologies in the food supply chain were analyzed applying a technology assessment framework that distinguishes between four different components of a technology: technique, knowledge, organization, and product. The study intends to provide new critical insights on how blockchain-based technologies can be implemented in the food supply chain and to further the discussion of social and environmental implications of blockchain-based technologies. The results highlight how blockchain is not a stand-alone-technology, but rather one element in a system of technologies. While blockchain-based technologies are expected to bring a variety of impacts, only some are directly attributable to the blockchain element: increased transparency, traceability, and trust. Other impacts such as improved data management are a side-effect of digitizing non-digital processes. Further research is needed to confirm whether blockchain-based technologies bring the expected sustainability improvements in food supply chains.
CNN-based encoder-decoder networks for salient object detection: A comprehensive review and recent advances

Convolutional neural network (CNN)-based encoder-decoder models have profoundly inspired recent works in the field of salient object detection (SOD). With the rapid development of encoder-decoder models with respect to most pixel-level dense prediction tasks, an empirical study still does not exist that evaluates performance by applying a large body of encoder-decoder models on SOD tasks. In this paper, instead of limiting our survey to SOD methods, a broader view is further presented from the perspective of fundamental architectures of key modules and structures in CNN-based encoder-decoder models for pixel-level dense prediction tasks. Moreover, we focus on performing SOD by leveraging deep encoder-decoder models, and present an extensive empirical study on baseline encoder-decoder models in terms of different encoder backbones, loss functions, training batch sizes, and attention structures. Moreover, state-of-the-art encoder-decoder models adopted from semantic segmentation and deep CNN-based SOD models are also investigated. New baseline models that can outperform state-of-the-art performance were discovered. In addition, these newly discovered baseline models were further evaluated on three video-based SOD benchmark datasets. Experimental results demonstrate the effectiveness of these baseline models on both image- and video-based SOD tasks. This empirical study is concluded by a comprehensive summary which provides suggestions on future perspectives.
Machine Learning Based Workload Prediction in Cloud Computing

As a widely used IT service, more and more companies shift their services to cloud datacenters. It is important for cloud service providers (CSPs) to provide cloud service resources with high elasticity and cost-effectiveness and then achieve good quality of service (QoS) for their clients. However, meeting QoS with cost-effective resource is a challenging problem for CSPs because the workloads of Virtual Machines (VMs) experience variation over time. It is highly necessary to provide an accurate VMs workload prediction method for resource provisioning to efficiently manage cloud resources. In this paper, we first compare the performance of representative state-of-the-art workload prediction methods. We suggest a method to conduct the prediction a certain time before the predicted time point in order to allow sufficient time for task scheduling based on predicted workload. To further improve the prediction accuracy, we introduce a clustering based workload prediction method, which first clusters all the tasks into several categories and then trains a prediction model for each category respectively. The trace-driven experiments based on Google cluster trace demonstrates that our clustering based workload prediction methods outperform other comparison methods and improve the prediction accuracy to around 90% both in CPU and memory.
Channel-Wise Autoregressive Entropy Models for Learned Image Compression

In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. Currently, the most effective learned image codecs take the form of an entropy-constrained autoencoder with an entropy model that uses both forward and backward adaptation. Forward adaptation makes use of side information and can be efficiently integrated into a deep neural network. In contrast, backward adaptation typically makes predictions based on the causal context of each symbol, which requires serial processing that prevents efficient GPU / TPU utilization. We introduce two enhancements, channel-conditioning and latent residual prediction, that lead to network architectures with better rate-distortion performance than existing context-adaptive models while minimizing serial processing. Empirically, we see an average rate savings of 6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to a context-adaptive baseline model. At low bit rates, where the improvements are most effective, our model saves up to 18% over the baseline and outperforms hand-engineered codecs like BPG by up to 25%.
Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

Artificial Intelligence (AI) has diffused into many areas of our private and professional life. In this research note, we describe exemplary risks of black-box AI, the consequent need for explainability, and previous research on Explainable AI (XAI) in information systems research. Moreover, we discuss the origin of the term XAI, generalized XAI objectives, and stakeholder groups, as well as quality criteria of personalized explanations. We conclude with an outlook to future research on XAI.
Scalable integrated single-photon source

Photonic qubits are key enablers for quantum-information processing deployable across a distributed quantum network. An on-demand and truly scalable source of indistinguishable single photons is the essential component enabling high-fidelity photonic quantum operations. A main challenge is to overcome noise and decoherence processes in order to reach the steep benchmarks on generation efficiency and photon indistinguishability required for scaling up the source. We report on the realization of a deterministic single-photon source featuring near-unity indistinguishability using a quantum dot in an 'on-chip' planar nanophotonic waveguide circuit. The device produces long strings of $>100$ single photons without any observable decrease in the mutual indistinguishability between photons. A total generation rate of $122$ million photons per second is achieved corresponding to an 'on-chip' source efficiency of $84 \%$. These specifications of the single-photon source are benchmarked for boson sampling and found to enable scaling into the regime of quantum advantage.
Hierarchical Alternate Interaction Network for RGB-D Salient Object Detection

Existing RGB-D Salient Object Detection (SOD) methods take advantage of depth cues to improve the detection accuracy, while pay insufficient attention to the quality of depth information. In practice, a depth map is often with uneven quality and sometimes suffers from distractors, due to various factors in the acquisition procedure. In this article, to mitigate distractors in depth maps and highlight salient objects in RGB images, we propose a Hierarchical Alternate Interactions Network (HAINet) for RGB-D SOD. Specifically, HAINet consists of three key stages: feature encoding, cross-modal alternate interaction, and saliency reasoning. The main innovation in HAINet is the Hierarchical Alternate Interaction Module (HAIM), which plays a key role in the second stage for cross-modal feature interaction. HAIM first uses RGB features to filter distractors in depth features, and then the purified depth features are exploited to enhance RGB features in turn. The alternate RGB-depth-RGB interaction proceeds in a hierarchical manner, which progressively integrates local and global contexts within a single feature scale. In addition, we adopt a hybrid loss function to facilitate the training of HAINet. Extensive experiments on seven datasets demonstrate that our HAINet not only achieves competitive performance as compared with 19 relevant state-of-the-art methods, but also reaches a real-time processing speed of 43 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://github.com/MathLee/HAINet.
Disentangling User Interest and Conformity for Recommendation with Causal Embedding

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users’ conformity towards popular items, which entangles users’ real interest. Existing methods tracks this problem as eliminating popularity bias, e.g., by re-weighting training samples or leveraging a small fraction of unbiased data. However, the variety of user conformity is ignored by these approaches, and different causes of an interaction are bundled together as unified representations, hence robustness and interpretability are not guaranteed when underlying causes are changing. In this paper, we present DICE, a general framework that learns representations where interest and conformity are structurally disentangled, and various backbone recommendation models could be smoothly integrated. We assign users and items with separate embeddings for interest and conformity, and make each embedding capture only one cause by training with cause-specific data which is obtained according to the colliding effect of causal inference. Our proposed methodology outperforms state-of-the-art baselines with remarkable improvements on two real-world datasets on top of various backbone models. We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
Deep learning in big data Analytics: A comparative study

Deep learning methods are extensively applied to various fields of science and engineering such as speech recognition, image classifications, and learning methods in language processing. Similarly, traditional data processing techniques have several limitations of processing large amount of data. In addition, Big Data analytics requires new and sophisticated algorithms based on machine and deep learning techniques to process data in real-time with high accuracy and efficiency. However, recently, research incorporated various deep learning techniques with hybrid learning and training mechanisms of processing data with high speed. Most of these techniques are specific to scenarios and based on vector space thus, shows poor performance in generic scenarios and learning features in big data. In addition, one of the reason of such failure is high involvement of humans to design sophisticated and optimized algorithms based on machine and deep learning techniques. In this article, we bring forward an approach of comparing various deep learning techniques for processing huge amount of data with different number of neurons and hidden layers. The comparative study shows that deep learning techniques can be built by introducing a number of methods in combination with supervised and unsupervised training techniques.
Facial Expression Recognition Using Hierarchical Features With Deep Comprehensive Multipatches Aggregation Convolutional Neural Networks

Facial expression recognition (FER) has long been a challenging task in computer vision. In this paper, we propose a novel method, named deep comprehensive multipatches aggregation convolutional neural networks (CNNs), to solve the FER problem. The proposed method is a deep-based framework, which mainly consists of two branches of the CNN. One branch extracts local features from image patches while the other extracts holistic features from the whole expressional image. In the model, local features depict expressional details and holistic features characterize the high-level semantic information of an expression. We aggregate both local and holistic features before making classification. These two types of hierarchical features represent expressions in different scales. Compared with most current methods with single type of feature, the model can represent expressions more comprehensively. Additionally, in the training stage, a novel pooling strategy named expressional transformation-invariant pooling is proposed for handling nuisance variations, such as rotations, noises, etc. Extensive experiments are conducted on the famous the Extended Cohn-Kanade (CK+) dataset and the Japanese Female Facial Expression (JAFFE) database expression datasets, where the recognition results obtained.
Adaptive sliding mode fault-tolerant control for type-2 fuzzy systems with distributed delays

In this paper, the problem of sliding-mode fault-tolerant control is addressed for a class of uncertain nonlinear systems with distributed delays and parameter perturbations. By using interval type-2 Takagi–Sugeno (T–S) fuzzy models, the nonlinear systems are formulated , of which uncertain parameters and distributed state delays are represented in a unified type-2 fuzzy framework. In order to tackle with the uncertain parameters in pre-designed membership functions, an adaptive mechanism is utilized to manage the time-varying weightings corresponding to the upper membership functions. A simple linear sliding surface subject to several solvable matrix inequalities is designed by using a reduced-order system. To guarantee the stability of the overall dynamic system, an adaptive sliding mode controller is designed, which can compensate for both uncertainties and distributed delays. Finally, a truck-trailer model system is used in simulations to verify the applicability and effectiveness of the control and estimation schemes.
The Promise of Empathy

This paper examines the promise of empathy, the name commonly given to the initial phase of the human-centered design process in which designers seek to understand their intended users in order to inform technology development. By analyzing popular empathy activities aimed at understanding people with disabilities, we examine the ways empathy works to both powerfully and problematically align designers with the values of people who may use their products. Drawing on disability studies and feminist theorizing, we describe how acts of empathy building may further distance people with disabilities from the processes designers intend to draw them into. We end by reimagining empathy as guided by the lived experiences of people with disabilities who are traditionally positioned as those to be empathized.
GenAttack

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting, where the attacker is restricted solely to query access. Existing black-box approaches to generating adversarial examples typically require a significant number of queries, either for training a substitute network or performing gradient estimation. We introduce GenAttack, a gradient-free optimization technique that uses genetic algorithms for synthesizing adversarial examples in the black-box setting. Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches. Against MNIST and CIFAR-10 models, GenAttack required roughly 2,126 and 2,568 times fewer queries respectively, than ZOO, the prior state-of-the-art black-box attack. In order to scale up the attack to large-scale high-dimensional ImageNet models, we perform a series of optimizations that further improve the query efficiency of our attack leading to 237 times fewer queries against the Inception-v3 model than ZOO. Furthermore, we show that GenAttack can successfully attack some state-of-the-art ImageNet defenses, including ensemble adversarial training and non-differentiable or randomized input transformations. Our results suggest that evolutionary algorithms open up a promising area of research into effective black-box attacks.