Reviewerly/exHarmony huggingface dataset:
Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks

Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign and malignant nodules have heterogeneous appearances. Computer-aided diagnosis (CAD) methods could potentially provide objective suggestions to assist radiologists. However, the performance of existing learning-based approaches is still limited, for direct application of general learning models often ignores critical domain knowledge related to the specific nodule diagnosis. In this study, we propose a novel deep-learning-based CAD system, guided by task-specific prior knowledge, for automated nodule detection and classification in ultrasound images. Our proposed CAD system consists of two stages. First, a multi-scale region-based detection network is designed to learn pyramidal features for detecting nodules at different feature scales. The region proposals are constrained by the prior knowledge about size and shape distributions of real nodules. Then, a multi-branch classification network is proposed to integrate multi-view diagnosis-oriented features, in which each network branch captures and enhances one specific group of characteristics that were generally used by radiologists. We evaluated and compared our method with the state-of-the-art CAD methods and experienced radiologists on two datasets, i.e. Dataset I and Dataset II. The detection and diagnostic accuracy on Dataset I were 97.5% and 97.1%, respectively. Besides, our CAD system also achieved better performance than experienced radiologists on Dataset II, with improvements of accuracy for 8%. The experimental results demonstrate that our proposed method is effective in the discrimination of thyroid nodules.
Human-centered design for global health equity

As digital technologies play a growing role in healthcare, human-centered design is gaining traction in global health. Amid concern that this trend offers little more than buzzwords, our paper clarifies how human-centered design matters for global health equity. First, we contextualize how the design discipline differs from conventional approaches to research and innovation in global health, by emphasizing craft skills and iterative methods that reframe the relationship between design and implementation. Second, while there is no definitive agreement about what the ‘human’ part means, it often implies stakeholder participation, augmenting human skills, and attention to human values. Finally, we consider the practical relevance of human-centered design by reflecting on our experiences accompanying health workers through over seventy digital health initiatives. In light of this material, we describe human-centered design as a flexible yet disciplined approach to innovation that prioritizes people's needs and concrete experiences in the design of complex systems.
Triple Archives Particle Swarm Optimization

There are two common challenges in particle swarm optimization (PSO) research, that is, selecting proper exemplars and designing an efficient learning model for a particle. In this article, we propose a triple archives PSO (TAPSO), in which particles in three archives are used to deal with the above two challenges. First, particles who have better fitness (i.e., elites) are recorded in one archive while other particles who offer faster progress, called profiteers in this article, are saved in another archive. Second, when breeding each dimension of a potential exemplar for a particle, we choose a pair of elite and profiteer from corresponding archives as two parents to generate the dimension value by ordinary genetic operators. Third, each particle carries out a specific learning model according to the fitness of its potential exemplars. Furthermore, there is no acceleration coefficient in TAPSO aiming to simplify the learning models. Finally, if an exemplar has excellent performance, it will be regarded as an outstanding exemplar and saved in the third archive, which can be reused by inferior particles aiming to enhance the exploitation and to save computing resources. The experimental results and comparisons between TAPSO and other eight PSOs on 30 benchmark functions and four real applications suggest that TAPSO attains very promising performance in different types of functions, contributing to both higher solution accuracy and faster convergence speed. Furthermore, the effectiveness and efficiency of these new proposed strategies are discussed based on extensive experiments.
Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazy image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light (BL) and the transmission map (TM). Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior (NUDCP) via the statistic of clear and high resolution (HD) underwater images, then a scene depth map based on the underwater light attenuation prior (ULAP) and an adjusted reversed saturation map (ARSM) are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R and G-B channels. Finally, to improve the color and contrast of the restored image with a dehazed and natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, lower computation time, overall superior performance, and better information retention.
Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems

In last 30 years, many metaheuristic algorithms have been developed to solve optimization problems. However, most existing metaheuristic algorithms have extra control parameters except the essential population size and stopping criterion. Considering different characteristics of different optimization problems, how to adjust these extra control parameters is a great challenge for these algorithms in solving different optimization problems. In order to address this challenge, a new metaheuristic algorithm called group teaching optimization algorithm (GTOA) is presented in this paper. The proposed GTOA is inspired by group teaching mechanism. To adapt group teaching to be suitable for using as an optimization technique, without loss of generality, four simple rules are first defined. Then a group teaching model is built under the guide of the four rules, which consists of teacher allocation phase, ability grouping phase, teacher phase and student phase. Note that GTOA needs only the essential population size and stopping criterion without extra control parameters, which has great potential to be used widely. GTOA is first examined over 28 well-known unconstrained benchmark problems and the optimization results are compared with nine state-of-the-art algorithms. Experimental results show the superior performance of the proposed GTOA for these problems in terms of solution quality, convergence speed and stability. Furthermore, GTOA is used to solve four constrained engineering design optimization problems in the real world. Simulation results demonstrate the proposed GTOA can find better solutions with faster speed compared with the reported optimizers.
Credit Card Fraud Detection using Machine Learning Algorithms

Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the paper is to design and develop a novel fraud detection method for Streaming Transaction Data, with an objective, to analyse the past transaction details of the customers and extract the behavioural patterns. Where cardholders are clustered into different groups based on their transaction amount. Then using sliding window strategy [1], to aggregate the transaction made by the cardholders from different groups so that the behavioural pattern of the groups can be extracted respectively. Later different classifiers [3],[5],[6],[8] are trained over the groups separately. And then the classifier with better rating score can be chosen to be one of the best methods to predict frauds. Thus, followed by a feedback mechanism to solve the problem of concept drift [1]. In this paper, we worked with European credit card fraud dataset.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content

Image visual try-on aims at transferring a target clothes image onto a reference person, and has become a hot topic in recent years. Prior arts usually focus on preserving the character of a clothes image (e.g. texture, logo, embroidery) when warping it to arbitrary human pose. However, it remains a big challenge to generate photo-realistic try-on images when large occlusions and human poses are presented in the reference person. To address this issue, we propose a novel visual try-on network, namely Adaptive Content Generating and Preserving Network (ACGPN). In particular, ACGPN first predicts semantic layout of the reference image that will be changed after try-on (e.g.long sleeve shirt→arm, arm→jacket), and then determines whether its image content needs to be generated or preserved according to the predicted semantic layout, leading to photo-realistic try-on and rich clothes details. ACGPN generally involves three major modules. First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on. Second, a clothes warping module warps clothes image according to the generated semantic layout, where a second-order difference constraint is introduced to stabilize the warping process during training.Third, an inpainting module for content fusion integrates all information (e.g. reference image, semantic layout, warped clothes) to adaptively produce each semantic part of human body. In comparison to the state-of-the-art methods, ACGPN can generate photo-realistic images with much better perceptual quality and richer fine-details.
Privacy-Preserving Federated Learning in Fog Computing

Federated learning can combine a large number of scattered user groups and train models collaboratively without uploading data sets, so as to avoid the server collecting user sensitive data. However, the model of federated learning will expose the training set information of users, and the uneven amount of data owned by users in multiple users' scenarios will lead to the inefficiency of training. In this article, we propose a privacy-preserving federated learning scheme in fog computing. Acting as a participant, each fog node is enabled to collect Internet-of-Things (IoT) device data and complete the learning task in our scheme. Such design effectively improves the low training efficiency and model accuracy caused by the uneven distribution of data and the large gap of computing power. We enable IoT device data to satisfy ε -differential privacy to resist data attacks and leverage the combination of blinding and Paillier homomorphic encryption against model attacks, which realize the security aggregation of model parameters. In addition, we formally verified our scheme can not only guarantee both data security and model security but completely resist collusion attacks launched by multiple malicious entities. Our experiments based on the Fashion-MNIST data set prove that our scheme is highly efficient in practice.
Weakly-supervised Semantic Guided Hashing for Social Image Retrieval

Hashing has been widely investigated for large-scale image retrieval due to its search effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided Hashing method coupled with binary matrix factorization to perform more effective nearest neighbor image search by simultaneously exploring the weakly-supervised rich community-contributed information and the underlying data structures. To uncover the underlying semantic information from the weakly-supervised user-provided tags, the binary matrix factorization model is leveraged for learning the binary features of images while the problem of imperfect tags is well addressed. The uncovered semantic information enables to well guide the discrete hash code learning. The underlying data structures are discovered by adaptively learning a discriminative data graph, which makes the learned hash codes preserve the meaningful neighbors. To the best of our knowledge, the proposed method is the first work that incorporates the hash code learning, the semantic information mining and the data structure discovering into one unified framework. Besides, the proposed method is extended to one deep approach for the optimal compatibility of discriminative feature learning and hash code learning. Experiments are conducted on two widely-used social image datasets and the proposed method achieves encouraging performance compared with the state-of-the-art hashing methods.
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration

Existing weakly-supervised semantic segmentation methods using image-level annotations typically rely on initial responses to locate object regions. However, such response maps generated by the classification network usually focus on discriminative object parts, due to the fact that the network does not need the entire object for optimizing the objective function. To enforce the network to pay attention to other parts of an object, we propose a simple yet effective approach that introduces a self-supervised task by exploiting the sub-category information. Specifically, we perform clustering on image features to generate pseudo sub-categories labels within each annotated parent class, and construct a sub-category objective to assign the network to a more challenging task. By iteratively clustering image features, the training process does not limit itself to the most discriminative object parts, hence improving the quality of the response maps. We conduct extensive analysis to validate the proposed method and show that our approach performs favorably against the state-of-the-art approaches.
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly

Building on Petroni et al. ( 2019), we propose two new probing tasks analyzing factual knowledge stored in Pretrained Language Models (PLMs).( 1) Negation.We find that PLMs do not distinguish between negated ("Birds cannot [MASK]") and non-negated ("Birds can [MASK]") cloze questions.(2) Mispriming.Inspired by priming methods in human psychology, we add "misprimes" to cloze questions ("Talk?Birds can [MASK]").We find that PLMs are easily distracted by misprimes.These results suggest that PLMs still have a long way to go to adequately learn human-like factual knowledge.cloze question true top 3 words generated with log probs Google RE O Marcel Oopa died in the city of [MASK].Paris Paris (-2.3),Lausanne (-3.3),Brussels (-3.3)N Marcel Oopa did not die in the city of [MASK].Paris (-2.4), Helsinki (-3.5),Warsaw (-3.5) M Yokohama?Marcel Oopa died in the city of [MASK].Yokohama (-1.0), Tokyo (-2.5), Paris (-3.0) O Anatoly Alexine was born in the city of [MASK].Moscow Moscow (-1.2),Kiev (-1.6), Odessa (-2.5)N Anatoly Alexine was not born in the city of [MASK].Moscow (-1.2),Kiev (-1.5),Novgorod (-2.5) M Kiev?Anatoly Alexine was born in the city of [MASK].Kiev (-0.0), Moscow (-6.1),Vilnius (-7.0)TERx O Platonism is named after [MASK] .Plato Plato (-1.5),Aristotle (-3.5),Locke (-5.8)N Platonism is not named after [MASK].Plato (-0.24),Aristotle (-2.5),Locke (-5.7) M Cicero?Platonism is named after [MASK].Cicero (-2.3),Plato ( -3.5), Aristotle (-5.1) O Lexus is owned by [MASK] .Toyota Toyota (-1.4), Renault (-2.0),Nissan (-2.4) N Lexus is not owned by [MASK].Ferrari (-1.0), Fiat (-1.4),BMW (-3.7) M Microsoft?Lexus is owned by [MASK] .Microsoft (-1.2),Google ( -2.1),Toyota (-2.6) Concept NetO Birds can [MASK].fly fly (-0.5), sing (-2.3), talk (-2.8)N Birds cannot [MASK].fly (-0.3), sing ( -3.6), speak (-4.1)M Talk?Birds can [MASK].talk (-0.2), fly ( -2.5), speak (-3.9) O A beagle is a type of [MASK].dog dog (-0.1), animal (-3.7), pigeon (-4.1)N A beagle is not a type of [MASK].dog (-0.2), horse ( -3.8), animal (-4.1)M Pigeon?A beagle is a type of [MASK].dog (-1.3), pigeon ( -1.4), bird (-2.2) SQuAD O Quran is a [MASK] text.religious religious (-1.0), sacred (-1.8),Muslim (-3.2) N Quran is not a [MASK] text.religious (-1.1), sacred ( -2.3), complete (-3.3)M Secular?Quran is a [MASK] text.religious (-1.5), banned ( -2.8), secular (-3.0) O Isaac's chains are made out of [MASK].silver silver (-1.9), gold (-2.1), iron (-2.2) N Isaac's chains are not made out of [MASK].iron (-1.2), metal ( -2.1), gold (-2.1)M Iron?Isaac's chains are made out of [MASK].iron (-0.4), steel ( -2.8), metal (-2.8)
Scaling Blockchains: A Comprehensive Survey

Blockchain (e.g., Bitcoin and Ethereum) has drawn much attention and has been widely-deployed in recent years. However, blockchain scalability is emerging as a challenging issue. This paper outlines the existing solutions to blockchain scalability, which can be classified into two categories: first layer and second layer solutions. First layer solutions propose modifications to the blockchain (i.e., changing the blockchain structure, such as block size) while second layer solutions propose mechanisms that are implemented outside of the blockchain. In particular, we focus on sharding as a promising first layer solution to the scalability issue; the basic idea behind sharding is to divide the blockchain network into multiple committees, each processing a separate set of transactions. More specifically, (a) we propose a taxonomy based on committee formation and intra-committee consensus; and (b) we compare the main existing sharding-based blockchain protocols. We also present a performance-based comparative analysis (i.e., throughput and latency), of the advantages, and disadvantages in existing scalability solutions.
Multiple cloud storage mechanism based on blockchain in smart homes

The emergence of the smart home has fundamentally changed the quality of human living owing to its usefulness and convenience. However, it still has some serious problems that mainly lie in its relational database security. The data storage of a smart home cannot meet the security requirements of its residents. To strengthen its security, blockchain technology is applied to the data storage and data connection, being embodied in the data storage model in smart homes based on blockchains under multiple cloud providers. However, the model still has weaknesses due to its limited blockchain transaction storage space and the current speed of addressing blockchain storage transactions. To solve these problems, this paper proposes an identity-based proxy aggregate signature (IBPAS) scheme to improve the efficiency of signature verification, as well as compress the storage space and reduce the communication bandwidth. According to our experiments, although the communication cost of our IBPAS scheme accounts for only 12% to 39% of that of an ordinary signature scheme, its storage performance in a blockchain is better than that of the blockchain itself by 20%.
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algorithm to calculate a specific variant of these characteristic functions where the probability weights of the characteristic function are defined as the transition probabilities of random walks. We argue that features extracted by this procedure are useful for node level machine learning tasks. We discuss the pooling of these node representations, resulting in compact descriptors of graphs that can serve as features for graph classification algorithms. We analytically prove that FEATHER describes isomorphic graphs with the same representation and exhibits robustness to data corruption. Using the node feature characteristic functions we define parametric models where evaluation points of the functions are learned parameters of supervised classifiers. Experiments on real world large datasets show that our proposed algorithm creates high quality representations, performs transfer learning efficiently, exhibits robustness to hyperparameter changes and scales linearly with the input size.
PhaseLink: A Deep Learning Approach to Seismic Phase Association

Abstract Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid‐free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences of P and S wave arrival times generated using a 1‐D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real‐time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state‐of‐the‐art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real‐time seismic monitoring, and streamline automated processing of large seismic data sets.