Reviewerly/exHarmony huggingface dataset:
Computer‐Aided Diagnosis of Solid Breast Lesions With Ultrasound: Factors Associated With False‐negative and False‐positive Results

To investigate factors that may lead to false-positive or false-negative results in a computer-aided diagnostic system (S-Detect; Samsung Medison Co, Ltd, Seoul, Korea) for ultrasound (US) examinations of solid breast lesions.This prospective study was approved by the Institutional Review Board of Sun Yat-sen Memorial Hospital. All patients signed and provided written informed consent before biopsy or surgery. From September 2017 to May 2018, 269 consecutive women with 338 solid breast lesions were included. All lesions were examined with US and S-Detect before biopsy or surgical excision. The final US assessments made by radiologists and S-Detect were matched to the pathologic results. Patient and lesion factors in the "true" and "false" S-Detect groups were compared, and multivariate logistic regression analyses were used to identify the factors associated with false S-Detect results.The mean age of the patients ± SD was 42.6 ± 12.9 years (range, 18-77 years). Of the 338 lesions, 209 (61.8%) were benign, and 129 (38.2%) were malignant. Larger lesions, the presence of lesion calcifications detected by B-mode US, and grades of 2 and 3 according to Adler et al (Ultrasound Med Biol 1990; 16:553-559) were significantly associated with false-positive S-Detect results (odds ratio [OR], 1.071; P = .006; OR, 5.851; P = .001; OR, 1.726; P = .009, respectively). Smaller lesions and the absence of calcifications detected by B-mode US in malignant solid breast lesions were significantly associated with false-negative S-Detect results (OR, 1.141; P = .015; OR, 7.434; P = .016).Larger benign lesions, the presence of lesion calcifications, and high degrees of vascularity are likely to show false-positive S-Detect results. Smaller malignant lesions and the absence of calcifications are likely to show false-negative S-Detect results.
User choice of interactive data visualization format: The effects of cognitive style and spatial ability

Interactive data visualization has become an important tool for business executives in monitoring key performance indicators, analyzing business process data, and managing individual and organizational performances. One of the key features in interactive data visualization is that users have direct control over how information is presented during the decision-making process. Few studies have examined how users choose visualization formats in such an environment. In this study, we investigate how cognitive style, spatial ability, and task difficulty affect the preference and choice of visualization formats and how the chosen visualization format in turn affects decision accuracy and confidence. Our results show that the choice of visualization format is affected by a user's cognitive style and task difficulty. While decision performances in accuracy and confidence are affected by task difficulty, spatial ability, and visualization format, they are largely not affected by the interactions between visualization format and cognitive style, spatial ability, and task difficulty.
A Novel Approach to Reliable Sensor Selection and Target Tracking in Sensor Networks

This paper addresses the problem of sensor selection in a sensor network for tracking a moving target. By considering network uncertainties and unpredictable movements of the target, reliable sensor selection approaches such as sigma points probability and target trajectory are proposed. An updated unscented Kalman filter is proposed to achieve effective tracking of the target through the sensor selection. A multialgorithm genetically adaptive multiobjective is utilized to have a selection strategy without knowing the number of sensors to be selected. Extensive experiments are conducted to evaluate the effectiveness of the proposed approach both in simulation and practical experimentation. The proposed algorithm is also tested in the industrial setting where providing safety is of great importance for a human worker who walks in a potentially dangerous workplace. The results confirm the effectiveness and utility of the proposed scheme.
Energy-efficient fault-tolerant replica management policy with deadline and budget constraints in edge-cloud environment

With the development of large-scale distributed systems such as grids and clouds, data replication management has become a hot research topic. Although replica management can improve the cluster system performance, it also brings a series of management and overhead issues. Therefore, the energy-efficient fault-tolerant replica management policy with the deadline and budget constraints in the edge-cloud environment is proposed. The experiments show that the proposed dynamic replica placement algorithm can effectively reduce the mean job time, reduce the use of network bandwidth and improve the utilization of storage space. Considering the issue of energy efficiency, the energy-aware cluster scaling strategy is proposed to reduce system energy consumption and achieve energy efficiency by sleeping and waking up the data nodes according to the load state of the system. Besides, in order to avoid access failures and data loss caused by node failures, the node failure recovery method based on availability metrics is used to deal with node failures. The experiments show that the performance of the proposed algorithm is better than the other algorithms in terms of energy efficiency and fault tolerance.
An experimental method to merge far-field images from multiple longwave infrared sensors for short-term solar forecasting

In a system used for short-term forecasting of solar irradiance, multiple longwave infrared sensors are used to acquire an image of a large, continuous section of the sky dome. The field of view of each sensor is directed at a particular, fixed portion of the sky dome, with some overlap between adjacent edges of each field of view to ensure that a continuous image can be acquired. Because of unavoidable imperfections in the optical components and in the alignment of the sensors, and because of the complex optics, it is difficult to pre-determine adjustable parameters in the geometric transformations required to merge the multiple images into a single one. Instead, it is possible to do this experimentally, by rotating the imaging sensor array via a two-axis rotating platform, using the sun itself as a convenient far-field reference object, and using the images collected to generate relations that map each sensor pixel into an altitude-azimuth direction, α and ϕ respectively. The experimental proof of concept of this method is described here.
Securing Resources in Decentralized Cloud Storage

Decentralized Cloud Storage services represent a promising opportunity for a different cloud market, meeting the supply and demand for IT resources of an extensive community of users.The dynamic and independent nature of the resulting infrastructure introduces security concerns that can represent a slowing factor towards the realization of such an opportunity, otherwise clearly appealing and promising for the expected economic benefits.In this paper, we present an approach enabling resource owners to effectively protect and securely delete their resources while relying on decentralized cloud services for their storage.Our solution combines All-Or-Nothing-Transform for strong resource protection, and carefully designed strategies for slicing resources and for their decentralized allocation in the storage network.We address both availability and security guarantees, jointly considering them in our model and enabling resource owners to control their setting.
A Novel Data Mining on Breast Cancer Survivability Using MLP Ensemble Learners

Abstract Breast cancer survivability has always been an important and challenging issue for researchers. Different methods have been utilized mostly based on machine learning techniques for prediction of survivability among cancer patients. The most comprehensive available database of cancer incidence is SEER in the United States, which has been frequently used for different research purposes. In this paper, a new data mining has been performed on the SEER database in order to investigate the ability of machine learning techniques for survivability prediction of breast cancer patients. To this end, the data related to breast cancer incidence have been preprocessed to remove unusable records from the dataset. In sequel, two machine learning techniques were developed based on the Multi-Layer Perceptron (MLP) learner machine including MLP stacked generalization and mixture of MLP-experts to make predictions over the database. The machines have been evaluated using K-fold cross-validation technique. The evaluation of the predictors revealed an accuracy of 84.32% and 83.86% by the mixture of MLP-experts and MLP stacked generalization methods, respectively. This indicates that the predictors can be significantly used for survivability prediction suggesting time- and cost-effective treatment for breast cancer patients.
Incremental Wishart Broad Learning System for Fast PolSAR Image Classification

In recent years, deep learning neural networks have seen wide adoption in synthetic aperture radar (SAR) image applications. Comparatively, convenient and fast neural network models have attracted less attention. In this letter, a novel incremental Wishart broad learning system (IWBLS) is specifically designed to achieve polarimetric SAR (PolSAR) image classification for the first time. IWBLS can effectively transfer essential Wishart distribution and other types of polarimetric decomposition and spatial features to establish mapped feature and enhancement nodes in one layer without deep learning structures, which means that massive layer-by-layer training consumption can be decreased significantly. Incremental learning concept is incorporated to deal with new PolSAR images or additional features, thereby avoiding retraining entire neural networks, whose properties are very appropriate for long-term monitoring or stepwise feature integration. The experiments substantiate advantages of PolSAR image classification based on our proposed IWBLS algorithm.
End-To-End Learned Random Walker for Seeded Image Segmentation

We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the un- derlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusiv- ities for a linear diffusion process. After calculating the exact gradient for optimizing these diffusivities, we pro- pose simplifications that sparsely sample the gradient while still maintaining competitive results. The proposed method achieves the currently best results on the seeded CREMI neuron segmentation challenge.
ReForm

Although the Deep Neural Network (DNN) technique has been widely applied in various applications, the DNN-based applications are still too computationally intensive for the resource-constrained mobile devices. Many works have been proposed to optimize the DNN computation performance, but most of them are limited in an algorithmic perspective, ignoring certain computing issues in practical deployment. To achieve the comprehensive DNN performance enhancement in practice, the expected DNN optimization works should closely cooperate with specific hardware and system constraints (i.e. computation capacity, energy cost, memory occupancy, and inference latency). Therefore, in this work, we propose ReForm -- a resource-aware DNN optimization framework. Through thorough mobile DNN computing analysis and innovative model reconfiguration schemes (i.e. ADMM based static model fine-tuning, dynamically selective computing), ReForm can efficiently and effectively reconfigure a pre-trained DNN model for practical mobile deployment with regards to various static and dynamic computation resource constraints. Experiments show that ReForm has ~3.5× faster optimization speed than state-of-the-art resource-aware optimization method. Also, ReForm can effective reconfigure a DNN model to different mobile devices with distinct resource constraints. Moreover, ReForm achieves satisfying computation cost reduction with ignorable accuracy drop in both static and dynamic computing scenarios (at most 18% workload, 16.23% latency, 48.63% memory, and 21.5% energy enhancement).
Predicting cryptocurrency defaults

We examine all available 146 Proof-of-Work-based cryptocurrencies that started trading prior to the end of 2014 and track their performance until December 2018. We find that about 60% of those cryptocurrencies were eventually in default. The substantial sums of money involved mean those bankruptcies will have an enormous societal impact. Employing cryptocurrency-specific data, we estimate a model based on linear discriminant analysis to predict such defaults. Our model is capable of explaining 87% of cryptocurrency bankruptcies after only one month of trading and could serve as a screening tool for investors keen to boost overall portfolio performance and avoid investing in unreliable cryptocurrencies.
BiScaled-DNN

Fixed-point implementations (FxP) are prominently used to realize Deep Neural Networks (DNNs) efficiently on energy-constrained platforms. The choice of bit-width is often constrained by the ability of FxP to represent the entire range of numbers in the datastructure with sufficient resolution. At low bit-widths (< 8 bits), state-of-the-art DNNs invariably suffer a loss in classification accuracy due to quantization/saturation errors.
Actuator and system component fault tolerant control using interval type-2 Takagi-Sugeno fuzzy controller for hybrid nonlinear process

The paper present efficient Fault-tolerant Control approach using Interval Type-2 Takagi-Sugeno Fuzzy Controller (IT2TSFLC) with conventional Proportional Integral (PI) controller for MIMO hybrid nonlinear level process. The implementation of control
Spatial Sampling Network for Fast Scene Understanding

We propose a network architecture to perform efficient scene understanding. This work presents three main novelties: the first is a module named Improved Guided Upsampling Module that can replace in toto the decoder part in common semantic segmentation networks. Our second contribution is the introduction of a new module based on spatial sampling to perform Instance Segmentation. It provides a very fast instance segmentation needing only a simple post-processing step at inference time. Finally, we propose a novel efficient network design that includes the new modules and test it against different datasets for outdoor scene understanding. To our knowledge, our network is one of the most efficient architectures for scene understanding published to date, furthermore being 8.6% more accurate than the fastest competitor on semantic segmentation and almost five times faster than the most efficient network for instance segmentation.
ACNN-FM: A novel recommender with attention-based convolutional neural network and factorization machines

With the rapid development of the Internet, the data generated from application platforms such as online shopping, e-education, and digital entertainment has exhibited dramatical growth, which has caused serious information overload to Internet users. The traditional recommendation approaches are crucial for Internet users to extract valuable information from various information. However, there exist some problems such as sparse data, cold start, and over-reliance on manual extracted feature and so on. To address the above problems, this paper proposes a novel recommender with Attention-based Convolutional Neural Network and Factorization Machines (ACNN-FM), which achieves the recommendation with comments. Firstly, from the perspective of local to overall, this paper proposes a word-level attention mechanism and a phrase-level attention mechanism to increase the ability to remember the importance and the order of historical vocabulary (phrase) in the process of text processing of convolutional neural networks. Secondly, it constructs a model to automatically extract hidden features of users and items from comments in the form of natural language. Finally, we utilize factorization machines to analyze the association between the hidden features of users and items, and implement the recommendation based on the association. Extensive experiments are conducted for demonstrating that ACNN-FM method outperforms state-of-the-art NARR method, and ACNN-FM has the highest data utilization among NARR, DeepCoNN, BCF and NMF methods, thus the recommendation performance is significantly improved in large-scale data environment.
Single image super-resolution via multi-scale residual channel attention network

Recently, various convolutional neural networks (CNNs) based single image super-resolution (SR) methods have been vigorously explored, and a lot of impressive results have emerged. However, more or less unfortunately, most of the methods mainly focused on increasing the depth of network to improve reconstruction performance. As a matter of fact, deeper depth of network usually means an increase in parameters and computations, or worse still, the increase in parameters or computations often results in the difficulty to train the network. This paper develops a new SR approach called multi-scale residual channel attention network (MSRCAN), which is comparative shallow two-stage neural network structure, and can extract more details to effectively ameliorate the quality of SR. Specifically, a multi-scale residual channel attention block (MSRCAB) is designed to plenarily exploit the image features with convolutional kernels of different sizes. At the same time, a channel attention mechanism is introduced to recalibrate the channel significance of feature mappings adaptively. Furthermore, multiple short skip connections and a long skip connection are presented in each MSRCAB to complement information loss. Moreover, the two-stage design contributes to fully uncover low-level and high-level information. Evaluation on the benchmark data set indicates that the proposed method can rival the state-of-the-art convolutional methods.
A continuation approach for training Artificial Neural Networks with meta-heuristics

Artificial Neural Networks research field is among the areas of major activity in Artificial Intelligence. Training a neural network is an NP-hard optimization problem that presents several theoretical and computational limitations. In optimization, continuation refers to an homotopy transformation of the fitness function that is used to obtain simpler versions of such fitness function and improve convergence. In this paper we propose an approach for Artificial Neural Network training based on optimization by continuation and meta-heuristic algorithms. The goal is to reduce overall execution time of training without causing negative effects in accuracy. We use continuation together with Particle Swarm Optimization, Firefly Algorithm and Cuckoo Search for training neural networks on public benchmark datasets. The continuation variations of the studied meta-heuristic algorithms reduce execution time required to complete training in about 5–30% without statistically significant loss of accuracy when compared with standard variations of the meta-heuristics.
Developing the sensitivity of LIME for better machine learning explanation

Machine learning systems can provide outstanding results, but their black-box nature means that it's hard to understand how the conclusion has been reached. Understanding how the results are determined is especially important in military and security contexts due to the importance of the decisions that may be made as a result. In this work, the reliability of LIME (Local Interpretable Model Agnostic Explanations), a method of interpretability, was analyzed and developed. A simple Convolutional Neural Network (CNN) model was trained using two classes of images of "gun-wielder" and "non-wielder". The sensitivity of LIME improved when multiple output weights for individual images were averaged and visualized. The resultant averaged images were compared to the individual images to analyze the variability and reliability of the two LIME methods. Without techniques such as those explored in this paper, LIME appears to be unstable because of the simple binary coloring and the ease with which colored regions flip when comparing different analyses. A closer inspection reveals that the significantly weighted regions are consistent, and the lower weighted regions flip states due to inherent randomness of the method. This suggests that improving the weighting methods for explanation techniques, which can then be used in the visualization of the results, is important to improve perceived stability and therefore better enable human interpretation and trust.
Specifying and Evaluating Quality Metrics for Vision-based Perception Systems

Robust perception algorithms are a vital ingredient for autonomous systems such as self-driving vehicles. Checking the correctness of perception algorithms such as those based on deep convolutional neural networks (CNN) is a formidable challenge problem. In this paper, we suggest the use of Timed Quality Temporal Logic (TQTL) as a formal language to express desirable spatio-temporal properties of a perception algorithm processing a video. While perception algorithms are traditionally tested by comparing their performance to ground truth labels, we show how TQTL can be a useful tool to determine quality of perception, and offers an alternative metric that can give useful information, even in the absence of ground truth labels. We demonstrate TQTL monitoring on two popular CNNs: YOLO and SqueezeDet, and give a comparative study of the results obtained for each architecture.
Latent topic ensemble learning for hospital readmission cost optimization

Unplanned hospital readmission is a costly problem in the United States, and in 2013 the U.S. federal government began to reduce payments to hospitals with preventable patient readmissions. Predictive modeling using machine learning and data analytics can be a useful decision support tool to help identify patients most likely to be readmitted. However, current systems have several shortcomings, such as difficulties in utilizing unstructured data and combining data from multiple hospitals. In this paper, we propose Latent Topic Ensemble Learning, which uses an ensemble of topic specific models to leverage data from multiple hospitals, as key data analytic algorithm for predicting hospital readmission. Models are built and evaluated incorporating federal financial penalties and tested using dataset containing data collected from 16 regional hospitals. It is found that LTEL significantly outperforms the best performing baseline method for readmission cost optimization.
Blind gain invariant image watermarking using random projection approach

Digital Watermarking helps us to protect the intellectual property of distributed media by hiding a piece of information within it. In this paper, a robust image watermarking method based on the projection of samples of the host signal into a random space is proposed. First, the vector constructed by the host signal samples is projected on two random vectors. Then, to embed a watermark digit, the ratio of the two projections is quantized. In order to preserve the imperceptibility of the watermark, analytical calculations are made to minimize the resultant distortion. To extract the watermark, a maximum likelihood detector is designed and accordingly the error probability of the proposed method is derived. The performance of the proposed method is investigated in terms of watermark to noise ratio. It is shown that there exists a trade-off between distortion and capacity without any concerns about the error probability. Experimental results verify the theoretical achievements of this study and show the superior performance of the proposed method in comparison to the state-of-the-art methods. Simulation results also demonstrate the robustness of the proposed method against common attacks such as filtering and geometrical attacks.