Reviewerly/exHarmony huggingface dataset:
Predictive Analysis Of Breast Cancer Using Machine Learning Techniques

This paper is a product of the research Project “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques” performed in Manav Rachna International Institute of Research and Studies, Faridabad in the year 2018.&#x0D; Introduction: The present article is part of the effort to predict breast cancer which is a serious concern for women’s health.&#x0D; Problem: Breast cancer is the most common type of cancer and has always been a threat to women’s lives. Early diagnosis requires an effective method to predict cancer to allow physicians to distinguish benign and malicious cancer. Researchers and scientists have been trying hard to find innovative methods to predict cancer.&#x0D; Objective: The objective of this paper will be predictive analysis of breast cancer using various machine learning techniques like Naïve Bayes method, Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine method. &#x0D; Methodology: Predictive data mining has become an instrument for scientists and researchers in the medical field. Predicting breast cancer at an early stage helps in better cure and treatment. KDD (Knowledge Discovery in Databases) is one of the most popular data mining methods used by medical researchers to identify the patterns and the relationship between variables and also helps in predicting the outcome of the disease based upon historical data of datasets.&#x0D; Results: To select the best model for cancer prediction, accuracy of all models will be estimated and the best model will be selected.&#x0D; Conclusion: This work seeks to predict the best technique with highest accuracy for breast cancer.&#x0D; Originality: This research has been performed using R and the dataset taken from UCI machine learning repository.&#x0D; Limitations: The lack of exact information provided by data.
Practical Traffic Analysis Attacks on Secure Messaging Applications

Instant Messaging (IM) applications like Telegram, Signal, and WhatsApp have become extremely popular in recent years. Unfortunately, such IM services have been targets of continuous governmental surveillance and censorship, as these services are home to public and private communication channels on socially and politically sensitive topics. To protect their clients, popular IM services deploy state-of-the-art encryption mechanisms. In this paper, we show that despite the use of advanced encryption, popular IM applications leak sensitive information about their clients to adversaries who merely monitor their encrypted IM traffic, with no need for leveraging any software vulnerabilities of IM applications. Specifically, we devise traffic analysis attacks that enable an adversary to identify administrators as well as members of target IM channels (e.g., forums) with high accuracies. We believe that our study demonstrates a significant, real-world threat to the users of such services given the increasing attempts by oppressive governments at cracking down controversial IM channels. We demonstrate the practicality of our traffic analysis attacks through extensive experiments on real-world IM communications. We show that standard countermeasure techniques such as adding cover traffic can degrade the effectiveness of the attacks we introduce in this paper. We hope that our study will encourage IM providers to integrate effective traffic obfuscation countermeasures into their software. In the meantime, we have designed and deployed an open-source, publicly available countermeasure system, called IMProxy, that can be used by IM clients with no need for any support from IM providers. We have demonstrated the effectiveness of IMProxy through experiments.
A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension

The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehension. In this paper, we study the cloze-style machine reading comprehension in the clinical medical field and propose a Gated Dilated Convolution with Attention (GDCA) model, which consists of a gated dilated convolution module and an attention mechanism. Our model has high parallelism and is capable of capturing long-distance dependencies. On the CliCR data set, our model surpasses the present best model on several metrics and obtains state-of-the-art result, and the training speed is 8 times faster than that of the best model.
Automatically Designing Convolutional Neural Network Architecture with Artificial Flora Algorithm

Convolutional neural networkBezdan, Timea has demonstratedTuba, Eva high performanceStrumberger, Ivana in many real-world problems in recent years. However, theBacanin, Nebojsa results and accuracyTuba, Milan of a CNN that are applied for a specific problem are highly controlled by the architecture and its hyperparameters. The process of finding the right set of hyperparameters for the network’s architecture is a very time-consuming process and requires expertise. To address this problem, we present a powerful method that does automatic hyperparameter search for designing CNN architecture. The hyperparameter optimization is performed by the artificial flora optimization algorithm. The proposed framework has the ability to explore different architectures and optimize the values of hyperparameters for a given task. In this research, the proposed framework is validated on MNIST image classification task and it can be concluded from the experimental results that the suggested search method accomplishes promising achievement in this domain.
Enterprise IT service downtime cost and risk transfer in a supply chain

Abstract In this paper we present an economic model for analyzing enterprise IT service downtime cost, first on a standalone basis and then in a supply chain setting. With a baseline probability model of Poisson arrival frequency with random downtime duration, we analyze optimal production of a firm’s investments in reducing frequency and duration of downtime, and corresponding premiums for insuring against downtime cost. We also present a model for the spillover effect of downtime for interconnected firms in a supply chain, and discuss how third-party insurance coverage can help enterprises to internalize the externalities of spillover effects on the supply chain.
Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows

We introduce a new regularization method for Artificial Neural Networks (ANNs) based on the Kernel Flow (KF) algorithm. The algorithm was introduced in Owhadi and Yoo (2019) as a method for kernel selection in regression/kriging based on the minimization of the loss of accuracy incurred by halving the number of interpolation points in random batches of the dataset. Writing fθ(x)=(fθn(n)∘fθn−1(n−1)∘⋯∘fθ1(1))(x) for the functional representation of compositional structure of the ANN (where θi are the weights and biases of the layer i), the inner layers outputs h(i)(x)=(fθi(i)∘fθi−1(i−1)∘…∘fθ1(1))(x) define a hierarchy of feature maps and a hierarchy of kernels k(i)(x,x′)=exp(−γi‖h(i)(x)−h(i)(x′)‖22). When combined with a batch of the dataset, these kernels produce KF losses e2(i) (defined as the L2 regression error incurred by using a random half of the batch to predict the other half) depending on the parameters of the inner layers θ1,…,θi (and γi). The proposed method simply consists of aggregating (as a weighted sum) a subset of these KF losses with a classical output loss (e.g., cross-entropy). We test the proposed method on Convolutional Neural Networks (CNNs) and Wide Residual Networks (WRNs) without alteration of their structure nor their output classifier and report reduced test errors, decreased generalization gaps, and increased robustness to distribution shift without a significant increase in computational complexity relative to standard CNN and WRN training (with Drop Out and Batch Normalization). We suspect that these results might be explained by the fact that while conventional training only employs a linear functional (a generalized moment) of the empirical distribution defined by the dataset and can be prone to trapping in the Neural Tangent Kernel regime (under over-parameterizations), the proposed loss function (defined as a nonlinear functional of the empirical distribution) effectively trains the underlying kernel defined by the CNN beyond regressing the data with that kernel.
Sensitivity of Adversarial Perturbation in Fast Gradient Sign Method

Fast Gradient Sign Method is a well-known method for adversarial sample attack. New adversarial samples could be generated by adding a small perturbation to input images, however the small perturbation usually need users to select by themselves. This paper focuses on FGSM attack in face recognition scenario and empirically evaluate multiple factors for adversarial perturbation in terms of recognition performance. The results demonstrate adversarial perturbation is sensitive to many factors, such as size of perturbation, number of iterations, and granularity of the perturbation.
Towards Contradiction Detection in German: a Translation-Driven Approach

With the recent advancements in Machine Learning based Natural Language Processing (NLP), language dependency has always been a limiting factor for a majority of NLP applications. Typically, models are trained for the English language due to the availability of very large labeled and unlabeled datasets, which also allow to fine tune models for that language. Contradiction Detection is one such problem that has found many practical applications in NLP and up to this point has only been studied in the context of English language. The scope of this paper is to examine a set of baseline methods for the Contradiction Detection task on German text. For this purpose, the well-known Stanford Natural Language Inference (SNLI) data set (110,000 sentence pairs) is machine-translated from English to German. We train and evaluate four classifiers on both the original and the translated data, using state-of-the-art textual data representations. Our main contribution is the first large-scale assessment for this problem in German, and a validation of machine translation as a data generation method. We also present a novel approach to learn sentence embeddings by exploiting the hidden states of an encoder-decoder Sequence-To-Sequence RNN trained for autoencoding or translation.
Exponential Automatic Amortized Resource Analysis

Abstract Automatic amortized resource analysis (AARA) is a type-based technique for inferring concrete (non-asymptotic) bounds on a program’s resource usage. Existing work on AARA has focused on bounds that are polynomial in the sizes of the inputs. This paper presents and extension of AARA to exponential bounds that preserves the benefits of the technique, such as compositionality and efficient type inference based on linear constraint solving. A key idea is the use of the Stirling numbers of the second kind as the basis of potential functions, which play the same role as the binomial coefficients in polynomial AARA. To formalize the similarities with the existing analyses, the paper presents a general methodology for AARA that is instantiated to the polynomial version, the exponential version, and a combined system with potential functions that are formed by products of Stirling numbers and binomial coefficients. The soundness of exponential AARA is proved with respect to an operational cost semantics and the analysis of representative example programs demonstrates the effectiveness of the new analysis.
Applying Blockchain Technology for User Incentivization in mmWave-Based Mesh Networks

Wireless traffic produced by modern mobile devices displays high temporal and spatial dynamics as users spontaneously engage in collective applications where a significant portion of generated data remains localized. As a result, conventional service provisioning approaches may no longer be sufficient in beyond fifth generation (B5G) systems. The challenge of increased dynamics on the access networks can be mitigated with moving cells. However, the deployment time of these temporary serving entities may lag behind the service demand lifetime. Another viable solution to offload excessive cellular traffic is to rely upon locally available radio resources offered by user devices via direct mmWave-based mesh interworking. An important challenge in such systems is related to the incentivization of users to partake in collaborative resource sharing. To leverage multi-hop mesh capabilities, we propose the use of emerging blockchain technology that offers cryptographically-strong accounting while maintaining the anonymity of the participants. With system-level evaluations, we demonstrate that the utilization of mobile blockchain methods allows for a non-incremental improvement in the offloading gains. This demonstrates the potential of the outlined proposal for becoming a successful mechanism in the emerging B5G systems.
Neural-network approach for identifying nonclassicality from click-counting data

Machine-learning and neural-network approaches have gained huge attention in the context of quantum science and technology in recent years. One of the most essential tasks for the future development of quantum technologies is the verification of nonclassical resources. Here, we present an artificial neural network approach for the identification of nonclassical states of light based on recorded measurement statistics. In particular, we implement and train a network which is capable of recognizing nonclassical states based on the click statistics recorded with multiplexed detectors. We use simulated data for training and testing the network, and we show that it is capable of identifying some nonclassical states even if they were not used in the training phase. Especially, in the case of small sample sizes, our approach can be more sensitive in identifying nonclassicality than established criteria which suggests possible applications in presorting of experimental data and online applications.
Learning quantum models from quantum or classical data

In this paper, we address the problem of how to represent a classical data distribution in a quantum system. The proposed method is to learn the quantum Hamiltonian, that is such that its ground state approximates the given classical distribution. We review previous work on the quantum Boltzmann machine (QBM) (Kieferova M and Nathan W 2017 Phys. Rev. A 96 062327, Amin M H et al 2018 Phys. Rev. X 8 021050) and how it can be used to infer quantum Hamiltonians from quantum statistics. We then show how the proposed quantum learning formalism can also be applied to a purely classical data analysis. Representing the data as a rank one density matrix introduces quantum statistics for classical data in addition to the classical statistics. We show that quantum learning yields results that can be significantly more accurate than the classical maximum likelihood approach, both for unsupervised learning and for classification. The data density matrix and the QBM solution show entanglement, quantified by the quantum mutual information I. The classical mutual information in the data I c ≤ I/2 = C, with C maximal classical correlations obtained by choosing a suitable orthogonal measurement basis. We suggest that the remaining mutual information Q = I/2 is obtained by non orthogonal measurements that may violate the Bell inequality. The excess mutual information I − I c may potentially be used to improve the performance of quantum implementations of machine learning or other statistical methods.
Joint extraction of entities and relations based on character graph convolutional network and Multi-Head Self-Attention Mechanism

The traditional method of extracting entities and relations not only disregards the dependency between the two subtasks of entities and relations but also facilitates the cumulative propagation of errors. To solve these problems, a method – called MSBD – of joint extraction of entities and relations based on the Character Graph Convolutional Network (CGCN) and Multi-Head Self-Attention Mechanism (MS) is proposed. First, a new tagging scheme is used to tag the positions of entities and relations in the text. Second, to depict the hierarchical structure information between the entities and the relations in the text and the internal structure information of the entity, the CGCN is designed to obtain the character vector of the text. MS is introduced into the coding framework of Bidirectional Long Short-Term Memory (BiLSTM) to capture the relative position information between two entities in the text and represent the subspace information. The Dense Connected Convolutional Network (Dense Net) is embedded in the decoding framework to enhance the reuse and transmission of key information and achieve joint extraction of entities and relations in the text. The experimental results show that the P, R and F values are significantly improved for extracting entities and relations.
In-situ imaging of inter- and intra-laminar damage in open-hole tension tests of carbon fibre-reinforced composites

This paper investigates damage progression during Open-Hole Tension (OHT) test of carbon fibre-reinforced composites with different Quasi-Isotropic (QI) layups. Digital Image Correlation (DIC) and in-situ edge microscopy are utilized simultaneously to detect damage on the specimen’s surface and in the inner plies, respectively. It is found that damage does not always initiate in the 90° plies, but also in the surface 45° plies, depending on the orientation of the adjacent ply. DIC resulting strain field on the surface does not necessarily have the same butterfly-shape concentration around the hole, in all layups. Some laminates show large free edge delaminations at 45°/90° interfaces. This study adds in-situ evidences to the existing data on damage during OHT test of QI laminates, providing quantitative and comparative information on different damage modes, and reveals new features of the damage phenomena.
The effect of a SECoS in crude palm oil forecasting to improve business intelligence

Crude palm oil is a crop that has a harvest period of ± 2 weeks and is in dire need of dissemination of information using e-commerce in order to be able to predict the price of the yield of companies or individual gardens within the next 2 weeks in order to improve studies on business intelligence. The disadvantage of not implementing e-commerce is certainly detrimental to the garden owner because they have to go through an agent so prices are set based on the agent. So with the application of e-commerce, buyers of crude palm oil can predict prices in conducting business processes to the future. So the need to forecasting the price of crude palm oil heads in order to improve the application of business intelligence using the evolution-based artificial neural network (ANN) method which in this paper is tested with SECoS get a MAPE value of 0.035% and by applying business intelligence can protect transaction costs by 33.3%.
On the Impact of the Rules on Autonomous Drive Learning

Autonomous vehicles raise many ethical and moral issues that are not easy to deal with and that, if not addressed correctly, might be an obstacle to the advent of such a technological revolution. These issues are critical because autonomous vehicles will interact with human road users in new ways and current traffic rules might not be suitable for the resulting environment. We consider the problem of learning optimal behavior for autonomous vehicles using Reinforcement Learning in a simple road graph environment. In particular, we investigate the impact of traffic rules on the learned behaviors and consider a scenario where drivers are punished when they are not compliant with the rules, i.e., a scenario in which violation of traffic rules cannot be fully prevented. We performed an extensive experimental campaign, in a simulated environment, in which drivers were trained with and without rules, and assessed the learned behaviors in terms of efficiency and safety. The results show that drivers trained with rules enforcement are willing to reduce their efficiency in exchange for being compliant to the rules, thus leading to higher overall safety.
Gradient Centralization: A New Optimization Technique for Deep Neural Networks

Optimization techniques are of great importance to effectively and efficiently train a deep neural network (DNN). It has been shown that using the first and second order statistics (e.g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance. Different from these existing methods that mostly operate on activations or weights, we present a new optimization technique, namely gradient centralization (GC), which operates directly on gradients by centralizing the gradient vectors to have zero mean. GC can be viewed as a projected gradient descent method with a constrained loss function. We show that GC can regularize both the weight space and output feature space so that it can boost the generalization performance of DNNs. Moreover, GC improves the Lipschitzness of the loss function and its gradient so that the training process becomes more efficient and stable. GC is very simple to implement and can be easily embedded into existing gradient based DNN optimizers with only one line of code. It can also be directly used to fine-tune the pre-trained DNNs. Our experiments on various applications, including general image classification, fine-grained image classification, detection and segmentation, demonstrate that GC can consistently improve the performance of DNN learning. The code of GC can be found at https://github.com/Yonghongwei/Gradient-Centralization.
Convergence Analysis of Adaptive Exponential Functional Link Network

The adaptive exponential functional link network (AEFLN) is a recently introduced novel linear-in-the-parameters nonlinear filter and is used in numerous nonlinear applications, including system identification, active noise control, and echo cancellation. The improved modeling accuracy offered by AEFLN for different nonlinear applications can be attributed to the exponentially varying sinusoidal basis functions used for nonlinear expansion. Even though AEFLN has been widely used for the identification of nonlinear systems, no theoretical analysis of AEFLN is available in the literature. Hence, in this article, a theoretical performance analysis of AEFLN trained using an adaptive exponential least mean square (AELMS) algorithm under the Gaussian input assumption is discussed. Expressions describing the mean as well as mean square behavior of the weight vector and adaptive exponential parameter are derived. Computer simulations are carried out, and the derived theoretical expressions show a close correspondence with simulation results.
Open Diagrams via Coend Calculus

Morphisms in a monoidal category are usually interpreted as processes, and graphically depicted as square boxes. In practice, we are faced with the problem of interpreting what non-square boxes ought to represent in terms of the monoidal category and, more importantly, how should they be composed. Examples of this situation include lenses or learners. We propose a description of these non-square boxes, which we call open diagrams, using the monoidal bicategory of profunctors. A graphical coend calculus can then be used to reason about open diagrams and their compositions.