Reviewerly/exHarmony huggingface dataset:
A new technique with improved control quality of nonlinear systems using an optimized fuzzy logic controller

Fuzzy logic controllers are increasingly applied to control complex systems because they have several advantages. The objective of this work is to propose a new technique to optimize a Takagi-Sugeno fuzzy logic controller with quality using the Non-dominated Sorting Genetic Algorithm-II by optimizing three objectives functions which are a cost function, the number of fuzzy inference rules, and the maximum instantaneous quadratic error. In this technique, the Multi-Criteria Decision-Making approach is used to choose one of the best controllers from the Pareto set of the last generation of the genetic algorithm. The proposed technique ensure: (i) that the output of the controlled system correctly follows the desired reference. (ii) the acceleration of the control process and (iii) avoid the existence of large overshoots; which are usually observed when applying commands to complex processes with variable behavior. At the end of the control process, a robustness test is performed to verify the efficiency of the proposed technique. It is shown here that the optimization of the third objective function, allows the improvement of the control quality. This new technique can be used to improve expert and intelligent systems based on fuzzy rules to control high complex systems with variable behavior which they have disturbing overshoots during their control. This technique; allows to accelerate the calculation of the control law of expert systems based on fuzzy rules; while ensuring that the quality of the control and the output signal are good.
An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions

Deep learning-based computer-aided diagnosis (CAD) is an important method in aiding diagnosis for radiologists. We investigated the accuracy of a deep learning-based CAD in classifying breast lesions with different histological types.A total of 448 breast lesions were detected on ultrasound (US) and classified by an experienced radiologist, a resident and deep learning-based CAD respectively. The pathological results of the lesions were chosen as the golden standard. The diagnostic performances of the three raters in different pathological types were analyzed.For the overall diagnostic performance, deep learning-based CAD presented a significantly higher specificity (76.96%) compared with the two radiologists. The area under ROC of CAD was almost equal with the experienced radiologist (0.81 vs. 0.81), while significantly higher than the resident (0.81 vs. 0.70, P<0.0001). In the benign lesions, deep learning-based CAD had a higher accuracy than both the two radiologists, which correctly classified as benign lesions in 119/135 of fibroadenomas (88.1%), 25/35 of adenosis (71.4%), 14/27 of intraductal papillary tumors (51.9%), 5/10 of inflammation (50%), and 4/8 of sclerosing adenosis (50%). But only the differences between CAD and the two radiologists in fibroadenomas had statistical significance (P=0.0011 and P=0.0313), and the differences between CAD and the resident in adenosis had statistical significance (P=0.012). In the malignant lesions, 151/168 of invasive ductal carcinomas (89.9%), 21/29 of ductal carcinoma in situ (DCIS) (72.4%) and 6/7 of invasive lobular carcinomas (85.7%) were diagnosed as malignancies by deep learning-based CAD, with no significant differences between CAD and the two radiologists.In the diagnosis of these common types of breast lesions, deep learning-based CAD had a satisfying performance. Deep learning-based CAD had a better performance in the breast benign lesions, especially in fibroadenomas and adenosis. Therefore, deep learning-based CAD is a promising supplemental tool to US to increase the specificity and avoid unnecessary benign biopsies.
Combining Q-Learning and Search with Amortized Value Estimates

We introduce with Amortized Value Estimates (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used in combination with real experience to update the prior. This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search. SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari. SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets. By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.
Bayesian inference for Common cause failure rate based on causal inference with missing data

This paper proposes a methodology to handle the causality to make inference on common cause failure (CCF) in a missing data situation. The data are collected in the form of contingency tables but the only available tokens of information are the numbers of CCFs of different orders and the numbers of failures due to a given cause, i.e. the margins of the contingency table. The frequencies in each cell are unknown; we are in a situation of missing data. Assuming a Poisson model for the count, we suggest a Bayesian approach and we use the inverse Bayes formula (IBF) combined with a Metropolis-Hastings algorithm to make inference on the parameters. The performance of the resulting algorithm is evaluated through simulations. A comparison is made by analogy with results obtained from the recently proposed α-composition method.
Polarization design for ground-to-satellite quantum entanglement distribution

High-fidelity transmission of polarization encoded qubits plays a key role in long distance quantum communication. By establishing the channel between ground and satellite, the communication distance can even exceed thousands of kilometers. Aimed to achieve the efficient uplink quantum communication, here we describe a high-fidelity polarization design of a transmitting antenna with an average polarization extinction ratio of 887:1 by a local test. We also implement a feasible polarization-compensation scheme for satellite motions with a fidelity exceeding 0.995 ± 0.001. Based on these works, we demonstrate the ground-to-satellite entanglment distribution with a violation of Bell inequality by 2.312±0.096, which is well above the classic limit 2.
Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations

The goal of many computer vision systems is to transform image pixels into 3D representations. Recent popular models use neural networks to regress directly from pixels to 3D object parameters. Such an approach works well when supervision is available, but in problems like human pose and shape estimation, it is difficult to obtain natural images with 3D ground truth. To go one step further, we propose a new architecture that facilitates unsupervised, or lightly supervised, learning. The idea is to break the problem into a series of transformations between increasingly abstract representations. Each step involves a cycle designed to be learnable without annotated training data, and the chain of cycles delivers the final solution. Specifically, we use 2D body part segments as an intermediate representation that contains enough information to be lifted to 3D, and at the same time is simple enough to be learned in an unsupervised way. We demonstrate the method by learning 3D human pose and shape from un-paired and un-annotated images. We also explore varying amounts of paired data and show that cycling greatly alleviates the need for paired data. While we present results for modeling humans, our formulation is general and can be applied to other vision problems.
Blockchain: A Misunderstood Digital Revolution. Things You Need to Know about Blockchain

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Dynamics analysis of chaotic maps: From perspective on parameter estimation by meta-heuristic algorithm*

Chaotic encryption is one of hot topics in cryptography, which has received increasing attention. Among many encryption methods, chaotic map is employed as an important source of pseudo-random numbers (PRNS). Although the randomness and the butterfly effect of chaotic map make the generated sequence look very confused, its essence is still the deterministic behavior generated by a set of deterministic parameters. Therefore, the unceasing improved parameter estimation technology becomes one of potential threats for chaotic encryption, enhancing the attacking effect of the deciphering methods. In this paper, for better analyzing the cryptography, we focus on investigating the condition of chaotic maps to resist parameter estimation. An improved particle swarm optimization (IPSO) algorithm is introduced as the estimation method. Furthermore, a new piecewise principle is proposed for increasing estimation precision. Detailed experimental results demonstrate the effectiveness of the new estimation principle, and some new requirements are summarized for a secure chaotic encryption system.
Towards an Evaluation Framework for Threat Intelligence Sharing Platforms

Threat intelligence sharing is an important countermeasure against the increasing number of security threats to which companies and governments are exposed.Its objective is the cross-organizational exchange of information about actual and potential threats.In recent years, a heterogeneous market of threat intelligence sharing platforms (TISPs) has emerged.These platforms are inter-organizational systems that support collaborative collection, aggregation, analysis and dissemination of threat-related information.Organizations that consider using TISPs are often faced with the challenge of selecting suitable platforms.To facilitate the evaluation of TISPs, we present a framework for analyzing and comparing relevant TISPs.Our framework provides a set of 25 functional and non-functional criteria that support potential users in selecting suitable platforms.We demonstrate the applicability of our evaluation framework by assessing three platforms: MISP, OTX and ThreatQ.We describe common features and differences between the three platforms.
OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender

Recently, wearable computing resources, such as smart-phones, are developing fast due to the advancements of technology and their great supports to human life. People are using smartphone for communication, work, entertainment, business, traveling, and browsing information. However, the health-care application is very limited due to many challenges. We would like to break the limitation and boost up the research to support human health. One of the important steps for a health-care system is to understand age and gender of the user through gait, who is wearing the sensor. Gait is chosen because it is the most dominant daily activity, which is considered to contain not only identity but also physical, medical conditions. To this end, we organize a challenging competition on gender and age prediction using wearable sensors. The evaluation is mainly based on the published OU-ISIR inertial dataset which is currently the world largest inertial gait dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> .
In Situ Measurements for Plastic Zone Ahead of Crack Tip and Continuous Strain Variation under Cyclic Loading Using Digital Image Correlation Method

Fatigue crack is one of the most common damage forms for aeronautical aluminum alloy. With crack propagation, the strain fields of the whole object surface and plastic zone (PZ) ahead of the crack tip are changing continuously. For most metallic materials, the behavior of PZ around the crack tip and continuous strain variation play a vital role in crack propagation. In this work, the “continuous” strain information at and in front of the crack tip on the specimen surface was obtained quantitatively and the PZ size ahead of crack tip was in situ measured quantitatively with crack propagation by using the digital image correlation (DIC) method, which overcomes the difficulty for the in situ measurement of mechanical variables. Moreover, the method of specimen preparation was simplified by using a white matt paint with strong adhesion, but also resulted in a higher resolution being shown, even for such a large area. Furthermore, the experimental results of the PZ size from the proposed method had good agreement with the theoretical values, which overcomes the limitation that the conventional approaches only consider the quasi-static crack. Finally, the continuous strain variation behavior was analyzed from the experimental results in detail with the consideration of crack propagation.
Adaptive scaling factors based on the impact of selected DCT coefficients for image watermarking

Image watermarking technique aims at high imperceptibility of the embedded watermark with minimal distortion in the watermarked images. In frequency-domain, block-based Discrete Cosine Transform (DCT) is a popular method which can be improved by different scaling factors. This paper presents an adaptive scaling factor for selected DCT coefficients in image watermarking. The image blocks that have lowest pixel variances are chosen as the embedding locations. The optimal scaling factors for selected DCT coefficients on the middle frequencies are obtained by finding the best quality of images. The embedding process is performed using the obtained scaling factors. The proposed technique was investigated to verify the robustness and imperceptibility against various image-processing attacks. It is proved that our technique achieves higher robustness against noise addition, filter and compression than the existing schemes in most cases. Our technique produces greater imperceptibility than the existing schemes.
Generalization bounds for deep convolutional neural networks

We prove bounds on the generalization error of convolutional networks.The bounds are in terms of the training loss, the number ofparameters, the Lipschitz constant of the loss and the distance fromthe weights to the initial weights. They are independent of thenumber of pixels in the input, and the height and width of hiddenfeature maps.We present experiments using CIFAR-10 with varyinghyperparameters of a deep convolutional network, comparing our boundswith practical generalization gaps.
A New Convolutional Neural Network-Based Steganalysis Method for Content-Adaptive Image Steganography in the Spatial Domain

Convolutional neural network-based methods are attracting increasing attention in steganalysis.However, steganalysis for content-adaptive image steganography in the spatial domain is still a difficult problem.In this paper, a new convolutional neural network-based steganalysis approach was proposed with two contributions.1) By adding more convolutional layers in the lower part of the model, we proposed a new arrangement of convolutional layers and pooling layers, which can process the local information better than the existing CNN models in steganalysis.2) By adding the global average pooling layer before the softmax layer instead of using global average pooling before the fully connected layer, the global average pooling was placed in a better position for steganalysis.Two state-of-the-art steganographic algorithms in the spatial domain, namely, WOW and S-UNIWARD, were used to evaluate the effectiveness of our model.The experimental results on BOSSbase showed that the proposed CNN could obtain better steganalysis performance than YeNet across all tested algorithms when the payloads were 0.2, 0.3, and 0.4 bpp.
Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

Abstract Background A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.
Learning-based image coding: early solutions reviewing and subjective quality evaluation

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality level, to enable efficient storage and transmission. Deep learning (DL) image coding solutions, typically using an auto-encoder architecture, promise significant improvements in compression efficiency. These methods adopt a novel coding approach where the encoder-decoder architecture is mostly based on neural networks, notably with analysis and synthesis transforms learned from a large amount of training data and an appropriate loss function. There are limited amount of works targeting the subjective evaluation of DL learning-based image coding solutions compression performance. Since learning-based image codecs use complex and highly non-linear generative models, very different artifacts are present in the decoded images, when compared to conventional artifacts such as blockiness, blurring and ringing distortions typical of traditional DCT block-based and wavelet image coding. In this context, the main objective of this paper is to review, characterize and evaluate some of the most relevant learning-based image coding solutions in the literature. Regarding the subjective quality evaluation, the assessment tests were conducted during the 84th JPEG meeting in Brussels, Belgium, by a mix of experts and naive observers. These subjective tests evaluated the performance of five state-of-the-art learning-based image coding solutions against four conventional, standard image coding (HEVC, WebP, JPEG 2000 and JPEG), applied to eight natural images, at four different coding bitrates. The experimental results obtained show that the subjective quality obtained with the selected learning-based image coding solution are competitive with conventional codecs. Moreover, a thorough inspection on the visual results has revealed some of the typical artifacts encountered in the learning -based image coding.
Computer-Assisted Language Comparison: State of the Art

Historical language comparison opens windows onto a human past, long before the availability of written records. Since traditional language comparison within the framework of the comparative method is largely based on manual data comparison, requiring the meticulous sifting through dictionaries, word lists, and grammars, the framework is difficult to apply, especially in times where more and more data have become available in digital form. Unfortunately, it is not possible to simply automate the process of historical language comparison, not only because computational solutions lag behind human judgments in historical linguistics, but also because they lack the flexibility that would allow them to integrate various types of information from various kinds of sources. A more promising approach is to integrate computational and classical approaches within a <em>computer-assisted framework</em>, "neither completely computer-driven nor ignorant of the assistance computers afford" [1, p. 4]. In this paper, we will illustrate what we consider the current state of the art of computer-assisted language comparison by presenting a workflow that starts with raw data and leads up to a stage where sound correspondence patterns across multiple languages have been identified and can be readily presented, inspected, and discussed. We illustrate this workflow with the help of a newly prepared dataset on Hmong-Mien languages. Our illustration is accompanied by Python code and instructions on how to use additional web-based tools we developed so that users can apply our workflow for their own purposes.
A blind proposed 3D mesh watermarking technique for copyright protection

Watermarking provides an efficient mechanism for copyright protection of digital media by embedding information in the object to identify the owner. This paper proposed a watermarking technique that can successfully embed a grey-scale image in a 3D mesh object. In our proposed technique, each three vertices of the 3D object can hide one pixel from the secret image. The experimental results showed that the proposed technique has better visual quality on both the watermarked object and the extracted secret image.
Bitcoin—A hype or digital gold? Global evidence

Abstract This study explores whether Bitcoin constitutes as a hedging instrument whilst seeking portfolio diversification opportunities among sustainable, conventional and Islamic asset classes since Bitcoin emerges as a distinct alternative investment and asset class across the world. We apply multivariate generalised autoregressive conditional heteroscedastic‐dynamic conditional correlation and continuous wavelet transforms based on the recent data set ranging from August 18, 2011, to September 10, 2018. First, our findings show that Bitcoin returns are mean‐reverting which implies that its value tends to come down to mean value in the long run and not completely crushed to zero irrespective of price changes suggesting Bitcoin as a sustainable asset class. Second, the time‐invariant model shows that Bitcoin offers portfolio diversification opportunities with almost all equity indices, in particular, Dow Jones Islamic followed by FTSE 4 Good index. Finally, the time‐variant analysis reconfirms that Bitcoin offers portfolio diversification benefits both in the short and long run. These findings carry meaningful policy considerations for fund managers and cross‐country investors.