Reviewerly/exHarmony huggingface dataset:
Multiscale Context-Cascaded Ensemble Framework (MsC<sup>2</sup>EF): Application to Breast Histopathological Image

Microscopic analysis of breast cell and tissue is a critical step in the definitive diagnosis of breast cancer. However, it's time-consuming and fatigable for histopathologists to find the diagnostic characteristic of cell and tissue in breast histopathological image through multiple magnification scannings. Many computer-aided studies, including traditional machine learning and deep learning approaches, have been conducted to efficiently assist histopathologists in making diagnostic decision. However, precision and complexity of such approaches remain challenging. In this work, we propose and evaluate a new framework, called multiscale context-cascaded ensemble framework (MsC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EF), to classify breast histopathological images. The model based on MsC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EF exhibits a higher precision than traditional machine learning. Meantime, it is more efficient and hardware-independent compared with deep learning approaches. The MsC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EF consists of the input, cascade, and decision layers. The input layer comprises a feature extractor and a spatial pyramid of image to execute feature input from coarse to fine scales. Four ensemble channels are stacked in a parallel manner as the cascade layer to select and transfer contextual feature iteratively and adaptively. For the decision layer, kernel fusion-based method is integrated to perform classification of breast histopathological image by fusing four different feature spaces. Our proposed method has been evaluated on an open dataset. The experimental result shows that MsC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EF obtains a good classification performance (Accuracy at patch level: 0.948±0.016; accuracy at patient level: 0.981±0.016), indicating its potential application to the classification of breast histopathologist images.
Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network

Introduction The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this paper we introduce Tangle, a time span-guided neural attention model that can accurately and timely predict the upcoming need for a second-line diabetes therapy from administrative data in the Australian adult population. The method is suitable for designing automatic therapy review recommendations for patients and their providers without the need to collect clinical measures. Data We analyzed seven years of de-identified records (2008-2014) of the 10% publicly available linked sample of Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) electronic databases of Australia. Methods By design, Tangle inherits the representational power of pre-trained word embedding, such as GloVe, to encode sequences of claims with the related MBS codes. Moreover, the proposed attention mechanism natively exploits the information hidden in the time span between two successive claims (measured in number of days). We compared the proposed method against state-of-the-art sequence classification methods. Results Tangle outperforms state-of-the-art recurrent neural networks, including attention-based models. In particular, when the proposed time span-guided attention strategy is coupled with pre-trained embedding methods, the model performance reaches an Area Under the ROC Curve of 90%, an improvement of almost 10 percentage points over an attentionless recurrent architecture. Implementation Tangle is implemented in Python using Keras and it is hosted on GitHub at https://github.com/samuelefiorini/tangle.
OSINT as a part of cyber defense system

The paper presents the results of research on the development of fundamental and applied principles for analyzing information flows in global computer networks while conducting open source intelligence (OSINT). The relevance of this task, in particular, concerning the provision of cyber security, the parameters of the modern information space, the existing theoretical and technological solutions are substantiated. The description of methodological and instrumental means of analysis and modeling of information flows, distributed content monitoring of global networks, the creation of multilingual full-text databases, analysis of the dynamics of thematic information flows with the use of nonlinear analysis, automatic formation of models of subject areas in the field of cyber security are presented.
A Recursive Mean Filter for Image Denoising

In this article, we propose a Recursive Mean Filter (RMF) to remove the salt-and-pepper noise (SPN). We compare RMF with similar methods such as Decision Based Algorithm (DBA), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). In the experiments, we implement the test on 15 native images of the MATLAB library. To assess the denoising quality, we consider the following noise densities (noise levels): 10%, 30%, 50%, 70%, 90%; and use the well-known two quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that RMF removes the noise effectively and competes with other state-of-the-art denoising methods.
External quality assessment (EQA) program for the immunohistochemical detection of ER, PR and Ki-67 in breast cancer: results of an interlaboratory reproducibility ring study in China

An External Quality Assessment (EQA) program was developed to investigate the status of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 immunohistochemical (IHC) detection in breast cancer and to evaluate the reproducibility of staining and interpretation in 44 pathology laboratories in China.This program was implemented through three specific steps. In study I, three revising centres defined the reference value for 11 sections. In study II, 41 participating centres (PC) stained and interpreted 11 sections by their own daily practice IHC protocols. In study III, all cases received second interpretation opinions.The stained slides of 44 laboratories were up to the interpretation standard. The overall interpretation concordance rate of this study was over 90%. A perfect agreement was reached among the PCs for the cases with ER+ and PR+ > 50% and Ki-67 > 30%, whereas a moderate agreement was observed for intermediate categories. After second interpretations, the misclassification rates for ER were reduced by 12.20%, for PR were reduced by 17.07%, and for Ki-67 were reduced by 4.88%. Up to 31 PCs observed a benefit from the second opinion strategy.This project is the first EQA study performed on a national scale for assessment of ER, PR and Ki-67 status by IHC in China. In the whole IHC evaluation process, the intermediate categories were less reproducible than those with high expression rates. Second opinions can significantly improve the diagnostic agreement of pathologists' interpretations.
Phase Space Reconstruction-Based Conceptor Network for Time Series Prediction

The Conceptor network is a newly proposed reservoir computing (RC) model, which outperforms traditional classifiers, which can fail to model new classes of data for a supervised learning task. However, the reservoir structure design for the Conceptor is single, involving just a traditional random network, which has strong coupling between nodes and limits computing ability. This study focused on the reservoir topology design problem, and we propose a complex network Conceptor-based phase space reconstruction of time series. Several dynamical systems were chosen to build complex networks using a phase space reconstruction algorithm. The experiment results obtained using a mix of two irrational-period sines showed that the proposed phase space reconstruction reservoir topologies with the appropriate values of threshold provide Conceptors with extra reconstruction precision. Among them, the phase space reconstruction reservoir-based Lorenz system shows the best performance. Further experiments also identified the appropriate values of threshold of the phase space reconstruction method required to obtain optimal performance. The precision showed a non-linear decline with increase in memory load, and the proposed Lorenz phase space reconstruction reservoir maintained its advantages under different memory loads.
Adventures in Hologram Space: Exploring the Design Space of Eye-to-eye Volumetric Telepresence

Modern volumetric projection-based telepresence approaches are capable of providing realistic full-size virtual representations of remote people. Interacting with full-size people may not be desirable due to the spatial constraints of the physical environment, application context, or display technology. However, the miniaturization of remote people is known to create an eye gaze matching problem. Eye-contact is essential to communication as it allows for people to use natural nonverbal cues and improves the sense of “being there”. In this paper we discuss the design space for interacting with volumetric representations of people and present an approach for dynamically manipulating scale, orientation and the position of holograms which guarantees eye-contact. We created a working augmented reality-based prototype and validated it with 14 participants.
Visualization Algorithms for Multi-criteria Alternatives Filtering

The article considers a decision-making problem for multi-criteria problems characterized by a large number of criteria and alternatives.As one of stages in making decisions in such situations, it is proposed to use a filtering of alternatives based on their visual images.For these purposes, the paper describes main aspects of this approach, as well as a statement of the algorithms for constructing the corresponding visual images based on radar and radial diagrams.A description of data preparation for operation of visualization algorithms for multi-criteria alternatives with an aim of their filtering is given.The paper describes software "Al-ternativesVisualizer" that implements the proposed algorithms, as well as providing interaction with an expert for visual filtering of multi-criteria alternatives.Additionally, the capabilities of the developed software are described.They include filtering alternatives based on threshold values, as well as the possibility of conducting a series of experiments in order to obtain the union or the intersection of filtered sets of alternatives.An experimental study of the developed algorithms and approaches effectiveness based on a synthetic test and an experiment on visual filtering of real alternatives describing the accuracy of calculating the inviscid flow around a cone using several OpenFoam solvers (rhoCentralFoam, pisoCentral-Foam, sonicFoam, rhoPimpleFoam, QGDFoam) is presented.Each solver is characterized by 288 criteria, and with the help of the developed algorithms during visual filtering, the clear predominance of two solvers over the others is established.
Few shot learning for multi-class classification based on nested ensemble DSVM

Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning.
Personalized Substitution Ranking for Lexical Simplification

A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user’s vocabulary proficiency, and always aim for the simplest candidate. This approach may overlook less-simple candidates that the user can understand, and that are semantically closer to the original word. We propose a personalized approach for Substitution Ranking to identify the candidate that is the closest synonym and is non-complex for the user. In experiments on learners of English at different proficiency levels, we show that this approach enhances the semantic faithfulness of the output, at the cost of a relatively small increase in the number of complex words.
Granger causality-based information fusion applied to electrical measurements from power transformers

In the immediate future, with the increasing presence of electrical vehicles and the large increase in the use of renewable energies, it will be crucial that distribution power networks are managed, supervised and exploited in a similar way as the transmission power systems were in previous decades. To achieve this, the underlying infrastructure requires automated monitoring and digitization, including smart-meters, wide-band communication systems, electronic device based-local controllers, and the Internet of Things. All of these technologies demand a huge amount of data to be curated, processed, interpreted and fused with the aim of real-time predictive control and supervision of medium/low voltage transformer substations. Wiener–Granger causality, a statistical notion of causal inference based on Information Fusion could help in the prediction of electrical behaviour arising from common causal dependencies. Originally developed in econometrics, it has successfully been applied to several fields of research such as the neurosciences and is applicable to time series data whereby cause precedes effect. In this paper, we demonstrate the potential of this methodology in the context of power measures for providing theoretical models of low/medium power transformers. Up to our knowledge, the proposed method in this context is the first attempt to build a data-driven power system model based on G-causality. In particular, we analysed directed functional connectivity of electrical measures providing a statistical description of observed responses, and identified the causal structure within data in an exploratory analysis. Pair-wise conditional G-causality of power transformers, their independent evolution in time, and the joint evolution in time and frequency are discussed and analysed in the experimental section.
Joker: Elastic stream processing with organic adaptation

This paper addresses the problem of auto-parallelization of streaming applications. We propose an online parallelization optimization algorithm that adjusts the degree of pipeline and data parallelism in a joint manner. We define an operator development API and a flexible parallel execution model to form a basis for the optimization algorithm. The operator interface unifies the development of different types of operators and makes operator properties visible in order to enable safe optimizations. The parallel execution model splits a data flow graph into regions. A region contains the longest sequence of compatible operators that are amenable to data parallelism as a whole and can be further parallelized with pipeline parallelism. We also develop a stream processing run-time, named Joker, to scale the execution of streaming applications in a safe, transparent, dynamic, and automatic manner. This ability is called organic adaptation. Joker implements the runtime machinery to execute a data flow graph with any parallelization configuration and most importantly change this configuration at run-time with low cost in the presence of partitioned stateful operators, in a way that is transparent to the application developers. Joker continuously monitors the run-time performance, and runs the optimization algorithm to resolve bottlenecks and scale the application by adjusting the degree of pipeline and data parallelism. The experimental evaluation based on micro-benchmarks and real-world applications showcase that our solution accomplishes elasticity by finding an effective parallelization configuration.
Learning to Link

Clustering is an important part of many modern data analysis pipelines, including network analysis and data retrieval. There are many different clustering algorithms developed by various communities, and it is often not clear which algorithm will give the best performance on a specific clustering task. Similarly, we often have multiple ways to measure distances between data points, and the best clustering performance might require a non-trivial combination of those metrics. In this work, we study data-driven algorithm selection and metric learning for clustering problems, where the goal is to simultaneously learn the best algorithm and metric for a specific application. The family of clustering algorithms we consider is parameterized linkage based procedures that includes single and complete linkage. The family of distance functions we learn over are convex combinations of base distance functions. We design efficient learning algorithms which receive samples from an application-specific distribution over clustering instances and learn a near-optimal distance and clustering algorithm from these classes. We also carry out a comprehensive empirical evaluation of our techniques showing that they can lead to significantly improved clustering performance on real-world datasets.
Predictive Container Auto-Scaling for Cloud-Native Applications

In the past decade, cloud computing has become an essential technology in many areas such as Internet of Things, artificial intelligence, and social media. In the cloud-computing environment, the auto-scaling capability of services is important to optimize cloud operating costs and Quality of Service. Therefore, there is a need for auto-scaling technology that is able to dynamically adjust resource allocation to cloud services based on incoming workload. In this paper, we present a predictive auto-scaler for Kubernetes clusters to improve the efficiency of container auto-scaling. Being based on a predictive algorithm, our auto-scaling scheme simplifies the architecture of existing auto-scaling system for more efficient service offerings. In addition, we present experimental evaluation results of our proposed scheme.
Exhaustive dithering algorithm for 3D shape reconstruction by fringe projection profilometry

Three-dimensional (3D) shape reconstruction by projection of defocused binary patterns overcomes the nonlinearity introduced by the projector. Current patch-based procedures that generate dithered patterns are time consuming and are affected by the harmonics introduced through the tiling process. To overcome this problem, we propose a novel idea, to the best of our knowledge, to generate dithering patterns using the composition of two-dimensional patches as a stack of one-dimensional arrays obtained through an efficient deterministic approach. This procedure is a one-dimension optimization problem in the intensity domain, employing only a quarter of the fringe pitch. Furthermore, the unwanted distorting harmonics are eliminated using a Hilbert transform method. Both numerical simulations and experimental results verify the effectiveness of the proposal.
Ultrafast Video Attention Prediction with Coupled Knowledge Distillation

Large convolutional neural network models have recently demonstrated impressive performance on video attention prediction. Conventionally, these models are with intensive computation and large memory. To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net. The network is constructed based on depth-wise convolutions and takes low-resolution images as input. However, this straight-forward acceleration method will decrease performance dramatically. To this end, we propose a coupled knowledge distillation strategy to augment and train the network effectively. With this strategy, the model can further automatically discover and emphasize implicit useful cues contained in the data. Both spatial and temporal knowledge learned by the high-resolution complex teacher networks also can be distilled and transferred into the proposed low-resolution light-weight spatiotemporal network. Experimental results show that the performance of our model is comparable to 11 state-of-the-art models in video attention prediction, while it costs only 0.68 MB memory footprint, runs about 10,106 FPS on GPU and 404 FPS on CPU, which is 206 times faster than previous models.
Simple Permutation and Diffusion Operation Based Image Encryption Using Various One-Dimensional Chaotic Maps: A Comparative Analysis on Security

With the development in technology, the security of transmission and storage of digital information (basically, digital images) is a challenge to all cryptographic researchers. In recent years, for securing digital images multiple encryption techniques have been proposed. Among them, one-dimensional (1D) chaotic map based image encryption techniques render better security in storage and transmission of images. 1D chaotic maps are simple in structure and hence efficient to implement in both software and hardware. An image encryption based on simple permutation and diffusion operation using various 1D chaotic maps is proposed in this paper. The proposed technique first performs pixel permutation operation using various 1D chaotic maps and then performs pixel diffusion operation using pixel key generated by the Secure Hash Algorithm-256 and the plain image. Also in this paper, a comparative analysis of security is presented using various 1D chaotic maps in image encryptions. The comparative results show the best security of using most of the 1D chaotic maps in image encryptions.
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification

Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge transfer from a related language helps text classification. We present a cross-lingual document classification framework (caco) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. The embedder derives vector representations for input words from their written forms, and the classifier makes predictions based on the word vectors. We use a joint character representation for both the source language and the target language, which allows the embedder to generalize knowledge about source language words to target language words with similar forms. We propose a multi-task objective that can further improve the model if additional cross-lingual or monolingual resources are available. Experiments confirm that character-level knowledge transfer is more data-efficient than word-level transfer between related languages.