Reviewerly/exHarmony huggingface dataset:
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

Medical image analysis is currently experiencing a paradigm shift due to deep learning. This technology has recently attracted so much interest of the Medical Imaging Community that it led to a specialized conference in “Medical Imaging with Deep Learning” in the year 2018. This paper surveys the recent developments in this direction and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying pattern recognition tasks and further sub-categorize it following a taxonomy based on human anatomy. This paper does not assume prior knowledge of deep learning and makes a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community. This paper provides a unique computer vision/machine learning perspective taken on the advances of deep learning in medical imaging. This enables us to single out “lack of appropriately annotated large-scale data sets” as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of computer vision, pattern recognition, and machine learning, where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging Community to fully harness deep learning in the future.
Oil and Gas 4.0 era: A systematic review and outlook

Recently, with the development of “Industry 4.0”, “Oil and Gas 4.0” has also been put on the agenda in the past two years. Some companies and experts believe that “Oil and Gas 4.0” can completely change the status quo of the oil and gas industry, which can bring huge benefits because it accelerates the digitization and intelligentization of the oil and gas industry. However, the “Oil and Gas 4.0” is still in its infancy. Therefore, this paper systematically introduces the concept and core technologies of “Oil and Gas 4.0”, such as big data and the industrial Internet of Things (IIoT). Moreover, this paper analyzes typical application scenarios of the oil and gas industry chain (upstream, midstream and downstream) through examples, such as intelligent oilfield, intelligent pipeline, and intelligent refinery. It is concluded that the essence of “Oil and Gas 4.0” is a data-driven intelligence system based on the highly digitization. To the best of our knowledge, this is the first academic peer-reviewed paper on the “Oil and Gas 4.0” era, aiming to let more oil and gas industry personnel understand its benefits and application scenarios, so as to better apply it to practical engineering in the future. In the discussion section, this paper also analyzes the opportunities and difficulties that may be brought about by the “Oil and Gas 4.0” era. Finally, relevant policy recommendations are proposed.
A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on the training data that presents an effective approach to incorporating the evaluation measure (i.e., G-mean) into the objective function. We first optimize the misclassification costs, and then apply them to DBN. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state of the art on both benchmark data sets and real-world data set for fault diagnosis in tool condition monitoring.
Deep Learning for Launching and Mitigating Wireless Jamming Attacks

An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts the next successful transmissions and effectively jams them. This jamming approach is shown to reduce the transmitter's performance much more severely compared with random or sensing-based jamming. The deep learning classification scores are used by the jammer for power control subject to an average power constraint. Next, a generative adversarial network is developed for the jammer to reduce the time to collect the training dataset by augmenting it with synthetic samples. As a defense scheme, the transmitter deliberately takes a small number of wrong actions in spectrum access (in form of a causative attack against the jammer) and therefore prevents the jammer from building a reliable classifier. The transmitter systematically selects when to take wrong actions and adapts the level of defense to mislead the jammer into making prediction errors and consequently increase its throughput.
Scalable Probes of Measurement-Induced Criticality

We uncover a local order parameter for measurement-induced phase transitions: the average entropy of a single reference qubit initially entangled with the system. Using this order parameter, we identify scalable probes of measurement-induced criticality (MIC) that are immediately applicable to advanced quantum computing platforms. We test our proposal on a 1+1 dimensional stabilizer circuit model that can be classically simulated in polynomial time. We introduce the concept of a "decoding light cone" to establish the local and efficiently measurable nature of this probe. We also estimate bulk and surface critical exponents for the transition. Developing scalable probes of MIC in more general models may be a useful application of noisy-intermediate scale quantum (NISQ) devices, as well as point to more efficient realizations of fault-tolerant quantum computation.
Gaze360: Physically Unconstrained Gaze Estimation in the Wild

Understanding where people are looking is an informative social cue. In this work, we present Gaze360, a large-scale remote gaze-tracking dataset and method for robust 3D gaze estimation in unconstrained images. Our dataset consists of 238 subjects in indoor and outdoor environments with labelled 3D gaze across a wide range of head poses and distances. It is the largest publicly available dataset of its kind by both subject and variety, made possible by a simple and efficient collection method. Our proposed 3D gaze model extends existing models to include temporal information and to directly output an estimate of gaze uncertainty. We demonstrate the benefits of our model via an ablation study, and show its generalization performance via a cross-dataset evaluation against other recent gaze benchmark datasets. We furthermore propose a simple self-supervised approach to improve cross-dataset domain adaptation. Finally, we demonstrate an application of our model for estimating customer attention in a supermarket setting. Our dataset and models will be made available at http://gaze360.csail.mit.edu.
Partition level multiview subspace clustering

Multiview clustering has gained increasing attention recently due to its ability to deal with multiple sources (views) data and explore complementary information between different views. Among various methods, multiview subspace clustering methods provide encouraging performance. They mainly integrate the multiview information in the space where the data points lie. Hence, their performance may be deteriorated because of noises existing in each individual view or inconsistent between heterogeneous features. For multiview clustering, the basic premise is that there exists a shared partition among all views. Therefore, the natural space for multiview clustering should be all partitions. Orthogonal to existing methods, we propose to fuse multiview information in partition level following two intuitive assumptions: (i) each partition is a perturbation of the consensus clustering; (ii) the partition that is close to the consensus clustering should be assigned a large weight. Finally, we propose a unified multiview subspace clustering model which incorporates the graph learning from each view, the generation of basic partitions, and the fusion of consensus partition. These three components are seamlessly integrated and can be iteratively boosted by each other towards an overall optimal solution. Experiments on four benchmark datasets demonstrate the efficacy of our approach against the state-of-the-art techniques.
Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning

The outputs of photovoltaic (PV) power are random and uncertain due to the variations of meteorological elements, which may disturb the safety and stability of power system operation. Hence, precise day-ahead PV power forecasting is crucial in renewable energy utilization, as it is beneficial to power generation schedule and short-term dispatch of the PV integrated power grid. In this study, a novel day-ahead PV power forecasting approach based on deep learning is proposed and validated. Firstly, two novel deep convolutional neural networks (CNNs), i.e. residual network (ResNet) and dense convolutional network (DenseNet), are introduced as the core models of forecasting. Secondly, a new data preprocessing is proposed to construct input feature maps for the two novel CNNs, which involves historical PV power series, meteorological elements and numerical weather prediction. Thirdly, a meta learning strategy based on multi-loss-function network is proposed to train the two deep networks, which can ensure a high robustness of the extracted convolutional features. Owing to the learning strategy and unique architectures of the two novel CNNs, they are designed into relatively deep architectures with superb nonlinear representation abilities, which consist of more than ten layers. Both point and probabilistic forecasting results are provided in the case study, demonstrating the accuracy and reliability of the proposed forecasting approach.
Blockchain and Machine Learning for Communications and Networking Systems

Recently, with the rapid development of information and communication technologies, the infrastructures, resources, end devices, and applications in communications and networking systems are becoming much more complex and heterogeneous. In addition, the large volume of data and massive end devices may bring serious security, privacy, services provisioning, and network management challenges. In order to achieve decentralized, secure, intelligent, and efficient network operation and management, the joint consideration of blockchain and machine learning (ML) may bring significant benefits and have attracted great interests from both academia and industry. On one hand, blockchain can significantly facilitate training data and ML model sharing, decentralized intelligence, security, privacy, and trusted decision-making of ML. On the other hand, ML will have significant impacts on the development of blockchain in communications and networking systems, including energy and resource efficiency, scalability, security, privacy, and intelligent smart contracts. However, some essential open issues and challenges that remain to be addressed before the widespread deployment of the integration of blockchain and ML, including resource management, data processing, scalable operation, and security issues. In this paper, we present a survey on the existing works for blockchain and ML technologies. We identify several important aspects of integrating blockchain and ML, including overview, benefits, and applications. Then we discuss some open issues, challenges, and broader perspectives that need to be addressed to jointly consider blockchain and ML for communications and networking systems.
Trustworthy Blockchain Oracles: Review, Comparison, and Open Research Challenges

The essence of blockchain smart contracts lies in the execution of business logic code in a decentralized architecture in which the execution outcomes are trusted and agreed upon by all the executing nodes. Despite the decentralized and trustless architectures of the blockchain systems, smart contracts on their own cannot access data from the external world. Instead, smart contracts interact with off-chain external data sources, called oracles, whose primary job is to collect and provide data feeds and input to smart contracts. However, there is always risk of oracles providing corrupt, malicious, or inaccurate data. In this paper, we analyze and present the notion of trust in the oracles used in blockchain ecosystems. We analyze and compare trust-enabling features of the leading blockchain oracle approaches, techniques, and platforms. Moreover, we discuss open research challenges that should be addressed to ensure secure and trustworthy blockchain oracles.
Adversarial Latent Autoencoders

Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.
One app to trace them all? Examining app specifications for mass acceptance of contact-tracing apps

The current COVID-19 crisis has seen governments worldwide mobilising to develop and implement contact-tracing apps as an integral part of their lockdown exit strategies. The challenge facing policy makers is that tracing can only be effective if the majority of the population uses the one app developed; its specifications must therefore be carefully considered. We theorise on tracing apps and mass acceptance and conduct a full-factorial experiment to investigate how app installation intention is influenced by different app specifications based on three benefit appeals, two privacy designs, and two convenience designs. By applying quantile regression, we not only estimate the general effect of these app specifications but also uncover how their influence differs among citizens with different propensities for acceptance (i.e. critics, undecided, advocates) – a crucial insight for succeeding with mass acceptance. This study contributes to research in three ways: we theorise how mass acceptance differs from established app acceptance, we provide a fine-grained approach to investigating the app specifications salient for mass acceptance, and we reveal contextualised insights specific to tracing apps with multi-layered benefit structures. Our findings can guide policy makers by providing specification recommendations for facilitating mass acceptance of tracing apps during pandemics or other societal crises.
An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study

BackgroundThere is high demand to develop computer-assisted diagnostic tools to evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of clinical deployment of such tools. We report here on a blinded clinical validation study and deployment of an artificial intelligence (AI)-based algorithm in a pathology laboratory for routine clinical use to aid prostate diagnosis.MethodsAn AI-based algorithm was developed using haematoxylin and eosin (H&E)-stained slides of prostate CNBs digitised with a Philips scanner, which were divided into training (1 357 480 image patches from 549 H&E-stained slides) and internal test (2501 H&E-stained slides) datasets. The algorithm provided slide-level scores for probability of cancer, Gleason score 7–10 (vs Gleason score 6 or atypical small acinar proliferation [ASAP]), Gleason pattern 5, and perineural invasion and calculation of cancer percentage present in CNB material. The algorithm was subsequently validated on an external dataset of 100 consecutive cases (1627 H&E-stained slides) digitised on an Aperio AT2 scanner. In addition, the AI tool was implemented in a pathology laboratory within routine clinical workflow as a second read system to review all prostate CNBs. Algorithm performance was assessed with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity, as well as Pearson's correlation coefficient (Pearson's r) for cancer percentage.FindingsThe algorithm achieved an AUC of 0·997 (95% CI 0·995 to 0·998) for cancer detection in the internal test set and 0·991 (0·979 to 1·00) in the external validation set. The AUC for distinguishing between a low-grade (Gleason score 6 or ASAP) and high-grade (Gleason score 7–10) cancer diagnosis was 0·941 (0·905 to 0·977) and the AUC for detecting Gleason pattern 5 was 0·971 (0·943 to 0·998) in the external validation set. Cancer percentage calculated by pathologists and the algorithm showed good agreement (r=0·882, 95% CI 0·834 to 0·915; p<0·0001) with a mean bias of −4·14% (−6·36 to −1·91). The algorithm achieved an AUC of 0·957 (0·930 to 0·985) for perineural invasion. In routine practice, the algorithm was used to assess 11 429 H&E-stained slides pertaining to 941 cases leading to 90 Gleason score 7–10 alerts and 560 cancer alerts. 51 (9%) cancer alerts led to additional cuts or stains being ordered, two (4%) of which led to a third opinion request. We report on the first case of missed cancer that was detected by the algorithm.InterpretationThis study reports the successful development, external clinical validation, and deployment in clinical practice of an AI-based algorithm to accurately detect, grade, and evaluate clinically relevant findings in digitised slides of prostate CNBs.FundingIbex Medical Analytics.
GDumb: A Simple Approach that Questions Our Progress in Continual Learning

We discuss a general formulation for the Continual Learning (CL) problem for classification—a learning task where a stream provides samples to a learner and the goal of the learner, depending on the samples it receives, is to continually upgrade its knowledge about the old classes and learn new ones. Our formulation takes inspiration from the open-set recognition problem where test scenarios do not necessarily belong to the training distribution. We also discuss various quirks and assumptions encoded in recently proposed approaches for CL. We argue that some oversimplify the problem to an extent that leaves it with very little practical importance, and makes it extremely easy to perform well on. To validate this, we propose GDumb that (1) greedily stores samples in memory as they come and; (2) at test time, trains a model from scratch using samples only in the memory. We show that even though GDumb is not specifically designed for CL problems, it obtains state-of-the-art accuracies (often with large margins) in almost all the experiments when compared to a multitude of recently proposed algorithms. Surprisingly, it outperforms approaches in CL formulations for which they were specifically designed. This, we believe, raises concerns regarding our progress in CL for classification. Overall, we hope our formulation, characterizations and discussions will help in designing realistically useful CL algorithms, and GDumb will serve as a strong contender for the same.
Know Your Surroundings: Exploiting Scene Information for Object Tracking

Current state-of-the-art trackers rely only on a target appearance model in order to localize the object in each frame. Such approaches are however prone to fail in case of e.g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking. Having the knowledge about the presence and locations of other objects in the surrounding scene can be highly beneficial in such cases. This scene information can be propagated through the sequence and used to, for instance, explicitly avoid distractor objects and eliminate target candidate regions. In this work, we propose a novel tracking architecture which can utilize scene information for tracking. Our tracker represents such information as dense localized state vectors, which can encode, for example, if a local region is target, background, or distractor. These state vectors are propagated through the sequence and combined with the appearance model output to localize the target. Our network is learned to effectively utilize the scene information by directly maximizing tracking performance on video segments. The proposed approach sets a new state-of-the-art on 3 tracking benchmarks, achieving an AO score of $$63.6\%$$ on the recent GOT-10k dataset.
Blockchain Adoption for Combating Deceptive Counterfeits

Counterfeiting is a severe problem in many sectors. There are two types of counterfeits: non‐deceptive and deceptive. While both types are important business challenge, deceptive counterfeit has an additional negative impact—customers have a post‐purchase regret if they expect to purchase a real product but ended up with a fake. The focus of this study is on the setting that relates to deceptive counterfeits. Our paper is one of the first that examines the effectiveness of blockchain as a solution to a supply chain challenge. Specifically, the unique feature of blockchain that we model, which none of the traditional strategies studied in the literature is capable of, is that blockchain adoption changes the analysis from a deceptive counterfeit setting to a non‐deceptive counterfeit setting. We also consider government being a decision maker and customers' privacy concern from blockchain adoption, two features that are not examined in the existing literature. We consider a market with a manufacturer and a deceptive counterfeiter. The manufacturer can signal product authenticity either with blockchain technology or through pricing. The government can provide subsidy to encourage blockchain adoption. Blockchain should be used when the counterfeit quality is intermediate or when customers have intermediate distrust about products in the market. If government provides subsidy, blockchain can be more effective than differential pricing strategy in eliminating post‐purchase regret. Our results advocate for government providing subsidy because it benefits both customers and the society and could be a better approach than government enforcement efforts.