Reviewerly/exHarmony huggingface dataset:
Taylor and Gradient Descent-Based Actor Critic Neural Network for the Classification of Privacy Preserved Medical Data

Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the privacy of the medical records and using the disease prediction mechanisms played a remarkable role in peoples' lives such that the earlier detection of the diseases is required for earlier diagnosis. Accordingly, this article proposes a method, named Taylor gradient descent (TGD)-based actor critic neural network (ACNN), which concentrates on performing the medical data classification. Initially, the privacy of the medical data is ensured by using the key matrix developed based on the privacy utility coefficient matrix using the chronological-Whale optimization algorithm. The privacy protected data are subjected to classification by using ACNN that performs the optimal classification using the proposed TGD algorithm. The proposed TGD algorithm is the integration of Taylor series in the gradient descent algorithm that updates the optimal weight of ACNN based on the weights in the previous iterations. The analysis using the Cleveland, Switzerland, and Hungarian dataset proves that the proposed classification strategy obtains an accuracy of 0.9252, a sensitivity of 0.8419, and a specificity of 0.8387, respectively.
Cross-Camera Person Re-Identification With Body-Guided Attention Network

Various challenges exist throughout person re-identification (ReID) process, including background clutters, illumination variation, pose variation, occlusion, etc. Addressing these problems, this paper explores the incorporation of human attention mechanism in person ReID and proposes an attention-aware model named Body-guided Attention Network (BANet). The proposed attention is based on the body masked images which are obtained by a reliable pixel-level segmentation strategy. To optimize the feature representation learning so as to pay more attention to the discriminative details of human body, BANet is built. It is composed of three attention branches. In order to guide attention learning layer by layer, these branches are applied to the convolution features of different levels. The proposed BANet aims to fully utilize fine-grained information of body region to guide the final process of feature extraction. Extensive experiments on benchmarks including CUHK03, Market1501 and DukeMTMC-reID show that BANet can achieve state-of-the-art performance, which validates the importance of attention mechanism in person ReID.
Formulas Free From Inconsistency: An Atom-Centric Characterization in Priest's Minimally Inconsistent LP

As one of fundamental properties to characterize inconsistency measures for knowledge bases, the property of free formula independence well captures the intuition that free formulas are independent of the amount of inconsistency in a knowledge base for cases where inconsistency is characterized in terms of minimal inconsistent subsets. But it has been argued that not all the free formulas are independent of inconsistency in some other contexts of inconsistency characterization. In this paper, we propose a characterization of formulas independent of inconsistency in the framework of Priest's minimally inconsistent LP. Based on an atom-based counterpart of the notion of free formula, we propose a notion of Bi-free formula to describe formulas that are free from inconsistency in both syntax and paraconsistent models in this logic. Then we propose the property of Bi-free formula independence, which is more suitable for characterizing the role of formulas free from inconsistency in measuring inconsistency from both syntactic and semantic perspectives.
A framework for rocket and satellite launch information management systems based on blockchain technology

The paper proposed the framework for rocket and satellite launch information management systems based on blockchain technology to meet the requirements for the storage and use of credible information. First, the launch stages of a rocket or satellite are analysed, and the roles of the participants in the launch process are identified. Then, the system framework is proposed. The framework includes an application layer, a contract and consensus layer, a data layer and a network layer. Each layer is designed in detail. Finally, the characteristics of the proposed framework are analysed to show its advantages.
Secure and Robust Image Watermarking Scheme Using Homomorphic Transform, SVD and Arnold Transform in RDWT Domain

The main objective for a watermarking technique is to attain imperceptibility, robustness and security against various malicious attacks applied by illicit users.To fulfil these basic requirements for a scheme is a big issue of concern.So, in this paper, a new image watermarking method is proposed which utilizes properties of homomorphic transform, Redundant Discrete Wavelet Transform (RDWT), Arnold Transform (AT) along with Singular Value Decomposition (SVD) to attain these required properties.RDWT is performed on host image to achieve LL subband.This LL subband image is further decomposed into illumination and reflectance components by homomorphic transform.In order to strengthen security of proposed scheme, AT is used to scramble watermark.This scrambled watermark is embedded with Singular Values (SVs) of reflectance component which are obtained by applying SVD to it.Since reflectance component contains important features of image, therefore, embedding of watermark in this part provides excellent imperceptibility.Proposed scheme is comprehensively examined against different attacks like scaling, shearing etc. for its robustness.Comparative study with other prevailing algorithms clearly reveals superiority of proposed scheme in terms of robustness and imperceptibility.
Design and Simulation of a Collision-free Path Planning Algorithm for Mobile Robots Based on Improved Ant Colony Optimization

This paper attempts to solve the 2D global path planning problem in a known environment.For this purpose, a smooth path planning method was designed for mobile robots based on dynamic feedback A* search algorithm and the improved ant colony optimization (ACO).Specifically, the ACO was improved from three aspects: optimizing the initial pheromone, improving evolutionary strategy and implementing dynamic closed-loop adjustment of parameters.The planned path was then smoothened by the cubic B-spline curve.The simulation results show our method converged to a shorter path in less time than the original ACO, and avoided the local optimum trap.
WolfGraph: The edge-centric graph processing on GPU

There is the significant interest nowadays in developing the frameworks for parallelizing the processing of large graphs such as social networks, web graphs, etc. The work has been proposed to parallelize the graph processing on clusters (distributed memory), multicore machines (shared memory) and GPU devices. Most existing research on GPU-based graph processing employs the vertex-centric processing model and the Compressed Sparse Row (CSR) form to store and process a graph. However, they suffer from irregular memory access and load imbalance in GPU, which hampers the full exploitation of GPU performance. In this paper, we present WolfGraph, a GPU-based graph processing framework that addresses the above problems. WolfGraph adopts the edge-centric processing, which iterates over the edges rather than vertices. The data structure and graph partition in WolfGraph are carefully crafted so as to minimize the graph pre-processing and allow the coalesced memory access. WolfGraph fully utilizes the GPU power by processing all edges in parallel. We also develop a new method, called Concatenated Edge List (CEL), to process a graph that is bigger than the global memory of GPU. WolfGraph allows the users to define their own graph-processing methods and plug them into the WolfGraph framework. Our experiments show that WolfGraph achieves 7-8x speedup over GraphChi and X-Stream when processing large graphs, and it also offers 65% performance improvement over the existing GPU-based, vertex-centric graph processing frameworks, such as Gunrock.
Multi-Agent Deep Reinforcement Learning with Emergent Communication

When compared with their single-agent counterpart, multi-agent systems have an additional set of challenges for reinforcement learning algorithms, including increased complexity, non-stationary environments, credit assignment, partial observability, and achieving coordination. Deep reinforcement learning has been shown to achieve successful policies through implicit coordination, but does not handle partial-observability. This paper describes a deep reinforcement learning algorithm, based on multi-agent actor-critic, that simultaneously learns action policies for each agent, and communication protocols that compensate for partial-observability and help enforce coordination. We also research the effects of noisy communication, where messages can be late, lost, noisy, or jumbled, and how that affects the learned policies. We show how agents are able to learn both high-level policies and complex communication protocols for several different partially-observable environments. We also show how our proposal outperforms other state-of-the-art algorithms that don't take advantage of communication, even with noisy communication channels.
Ornaments for Proof Reuse in Coq

Ornaments express relations between inductive types with the same inductive structure. We implement fully automatic proof reuse for a particular class of ornaments in a Coq plugin, and show how such a tool can give programmers the rewards of using indexed inductive types while automating away many of the costs. The plugin works directly on Coq code; it is the first ornamentation tool for a non-embedded dependently typed language. It is also the first tool to automatically identify ornaments: To lift a function or proof, the user must provide only the source type, the destination type, and the source function or proof. In taking advantage of the mathematical properties of ornaments, our approach produces faster functions and smaller terms than a more general approach to proof reuse in Coq.
Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy

Fever is a common symptom of many diseases. Fever temporal patterns can be different depending on the specific pathology. Differentiation of diseases based on multiple mathematical features and visual observations has been recently studied in the scientific literature. However, the classification of diseases using a single mathematical feature has not been tried yet. The aim of the present study is to assess the feasibility of classifying diseases based on fever patterns using a single mathematical feature, specifically an entropy measure, Sample Entropy. This was an observational study. Analysis was carried out using 103 patients, 24 hour continuous tympanic temperature data. Sample Entropy feature was extracted from temperature data of patients. Grouping of diseases (infectious, tuberculosis, non-tuberculosis, and dengue fever) was made based on physicians diagnosis and laboratory findings. The quantitative results confirm the feasibility of the approach proposed, with an overall classification accuracy close to 70%, and the capability of finding significant differences for all the classes studied. %An abstract is a brief of the paper; the abstract should not contain references, the text of the abstract section should be in 12 point normal Times New Roman.
A Prediction Method for Blood Glucose Based on Grey Wolf optimization Evolving Kernel Extreme Learning Machine

Patients with type 1 diabetes need to acquire their blood glucose prediction values which can make sure the actual value is well controlled within the normal range. Therefore, the accuracy of the blood glucose prediction method is very important. In this paper, the radial basis function kernel extreme learning machine (KELM) is used to predict blood glucose, and the parameters are adjusted by the grey wolf optimization (GWO) algorithm. Experiment results show that KELM based on GWO algorithm has great robustness and better generalization performance comparison with traditional extreme learning machine algorithm. The GWO-KELM model achieved high prediction accuracy.
Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods

Evidence accumulation models are a useful tool to allow researchers to investigate the latent cognitive variables that underlie response time and response accuracy.However, applying evidence accumulation models can be difficult because they lack easily computable forms.Numerical methods are required to determine the parameters of evidence accumulation that best correspond to the fitted data.When applied to complex cognitive models, such numerical methods can require substantial computational power which can lead to infeasibly long compute times.In this paper, we provide efficient, practical software and a step-by-step guide to fit evidence accumulation models with Bayesian methods.The software, written in C++, is provided in an R package: 'ggdmc'.The software incorporates three important ingredients of Bayesian computation, (1) the likelihood functions of two common response time models, (2) the Markov chain Monte Carlo (MCMC) algorithm (3) a population-based MCMC sampling method.The software has gone through stringent checks to be hosted on the Comprehensive R Archive Network (CRAN) and is free to download.We illustrate its basic use and an example of fitting complex hierarchical Wiener diffusion models to four shooting-decision data sets.
An Algorithmic Blockchain Readiness Index

This paper presents the design of a Blockchain Readiness Index (BRI) to be used as a tool for assisting nations to monitor the level of Blockchain maturity according to their suitability on hosting blockchain-based activities, and successfully adopting a blockchain regulatory framework. BRI is a composite index that combines a variety of indicators from a range of sources to a single score. The proposed methodology attempts to fill a knowledge gap by evaluating the relatively unexplored area of blockchain adoption per nation. The index presented in this paper aims to distinguish between the most promising and non-hostile countries, acting as the basis for professional work, decision making and operations of organizations, investors, academics, and other stakeholders within the blockchain space. As the index is updated regularly, all new developments on the fast-changing landscape of blockchain and cryptocurrencies are reflected. In addition, this research aims to go beyond the regulatory environment towards examining several other factors such as local engagement, expertise, investments, and the need for a decentralized provision of services. This paper presents an overview of the landscape of similar attempts on designing such indexes by reviewing and identifying potential gaps and opportunities for improving their methodological design that can lead to more accurate and relevant conclusions. In addition, with this paper we contribute a systematic methodology for building a BRI using techniques from the information retrieval domain to normalize the non-normalized values, and a cosine similarity measure to derive an index ranking consisting of various nations. More specifically the proposed BRI covers a wide range of blockchain readiness indicators which can be organized into the following &ldquo;pillars&rdquo;: (1) Government Regulation, (2) Research, (3) Technology, (4) Industry, and (5) User Engagement. An empirical evaluation reports preliminary but promising results of the algorithmic design methodology showing evidence that, the identified indicators are sufficient for developing our index when compared to judgements made by human experts.
Optimal GPU-CPU Offloading Strategies for Deep Neural Network Training

Training Deep Neural Networks is known to be an expensive operation, both in terms of computational cost and memory load. Indeed, during training, all intermediate layer outputs (called activations) computed during the forward phase must be stored until the corresponding gradient has been computed in the backward phase. These memory requirements sometimes prevent to consider larger batch sizes and deeper networks, so that they can limit both convergence speed and accuracy. Recent works have proposed to offload some of the computed forward activations from the device memory to the main memory and requires to determine which activations should be offloaded and when these transfers should take place. We prove that this problem is NP-complete in the strong sense, and propose two heuristics based on relaxations of the problem. We then conduct a thorough experimental evaluation of standard deep neural networks.
Image Threshold Segmentation Based on GLLE Histogram

To improve the threshold segmentation performance, we propose a novel 2-D histogram with the measure of gray level and local entropy. The proposed GLLE histogram introduces local entropy, which can effectively describe the spatial information of pixels. Furthermore, we put forward the criterion function for GLLE histogram and calculate the threshold by Renyi entropy, which makes our method more generalized. Experiment results show that our method is effective and robust compared with seven other methods in segmentation accuracy and has the ability to resist the illumination.
Learning Context Using Segment-Level LSTM for Neural Sequence Labeling

This article introduces an approach that learns segment-level context for sequence labeling in natural language processing (NLP). Previous approaches limit their basic unit to a word for feature extraction because sequence labeling is a tokenlevel task in which labels are annotated word-by-word. However, the text segment is an ultimate unit for labeling, and we are easily able to obtain segment information from annotated labels in a IOB/IOBES format. Most neural sequence labeling models expand their learning capacity by employing additional layers, such as a character-level layer, or jointly training NLP tasks with common knowledge. The architecture of our model is based on the charLSTM-BiLSTM-CRF model, and we extend the model with an additional segment-level layer called segLSTM. We therefore suggest a sequence labeling algorithm called charLSTM-BiLSTMCRF-segLSTMsLM which employs an additional segment-level long short-term memory (LSTM) that trains features by learning adjacent context in a segment. We demonstrate the performance of our model on four sequence labeling datasets, namely, Peen Tree Bank, CoNLL 2000, CoNLL 2003, and OntoNotes 5.0. Experimental results show that our model performs better than state-of-theart variants of BiLSTM-CRF. In particular, the proposed model enhances the performance of tasks for finding appropriate labels of multiple token segments.
Blockchain-Driven on-Demand Control Loops Over Iot Environments

The feedback control loop is the atomic unit in a control system. Typically, feedback control loops are rigid objects that involve a dynamical system, or plant, which has a set of its output states measured by dedicated sensors, which in turn feed a processing unit, known as controller, that calculates actions to be applied as inputs to the plant, via elements known as actuators, in order to drive the outputs to a desired value or trajectory. The appearance of the Internet of Things (IoT) paradigm, where a large number of sensors and processing units interact over a communication network, offers an underlying infrastructure to operate and configure control loops using a different logic: an on-demand strategy. This work introduces the concept of on-demand control loop, and proposes the use of blockchain technology as the enabling infrastructure for generating on-demand control loops over large-scale IoT environments. General design guidelines are given and a simple implementation example over the Ethereum blockchain is presented, which shows the feasibility of the proposed technique.
Author Correction: Unifying scrambling, thermalization and entanglement through measurement of fidelity out-of-time-order correlators in the Dicke model

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Malaria is a severe public health problem worldwide, with some developing countries being most affected. Reliable remote diagnosis of malaria infection will benefit from efficient compression of high-resolution microscopic images. This paper addresses a lossless compression of malaria-infected red blood cell images using deep learning. Specifically, we investigate a practical approach where images are first classified before being compressed using stacked autoencoders. We provide probabilistic analysis on the impact of misclassification rates on compression performance in terms of the information-theoretic measure of entropy. We then use malaria infection image datasets to evaluate the relations between misclassification rates and actually obtainable compressed bit rates using Golomb–Rice codes. Simulation results show that the joint pattern classification/compression method provides more efficient compression than several mainstream lossless compression techniques, such as JPEG2000, JPEG-LS, CALIC, and WebP, by exploiting common features extracted by deep learning on large datasets. This study provides new insight into the interplay between classification accuracy and compression bitrates. The proposed compression method can find useful telemedicine applications where efficient storage and rapid transfer of large image datasets is desirable.
Experimental conversion of position correlation into polarization entanglement

This manuscript presents a method to convert position correlation of photon-pairs into polarization entanglement. This is achieved by individually manipulating the polarization state of photons generated in different parts of a non-linear medium and putting them in coherent superposition. This concept is experimentally demonstrated using photon-pairs produced by spontaneous parametric down conversion (SPDC). The method was used to implement a compact source producing an observed photon-pair rate of 120,000/s/mW with an entanglement fidelity of 0.99. This method can be extended to any photon-pair generation process with initial position correlation.