Reviewerly/exHarmony huggingface dataset:
New Random Noise Denoising Method for Biomedical Image Processing Applications

Since the inception of digital image processing, noise removal in images has always been a challenge to researchers and experts of the field. Most significant of these noises are the randomly varying impulse noises developed while image is acquired. Hence, the need for methodical denoising method has led to extensive research and development of various innovative methods to remove the random valued impulse noise. For this, a method which detects and filters random valued impulse noise in medical images is employed. The method proposed in this paper uses a decision tree based impulse detector and an edge preserving filter to rebuild noise free images. This method is more efficient than the existing techniques due to its lower complexity. Different gray scale Magnetic Resonance Imaging (MRI) brain images are tested by using this algorithm and have given better Peak Signal to Noise Ratio (PSNR) than the other techniques.
Adaptive Multiple-pixel Wide Seam Carving

Content-aware image retargeting methods address the resizing of an image to be displayed on devices having different aspect ratios and resolutions. Seam carving method is an effective image retargeting method which suffers from high computational complexity. It requires one to find one-pixel wide minimum energy path in either vertical or horizontal direction, called seam, to reduce the image size by one pixel. In this paper, we propose an acceleration of the seam carving method by expanding the width of the seam making it multiple-pixel wide seam carving. The two types of energies: one corresponding to the pixels to be removed and another corresponding to the pixels across the multiple-pixel wide seam, increase as the width of the seam increases. In order to prevent the increase in these energies, we make the width of the seam adaptive as a function of the number of iterations. We find the width of a seam for each iteration as a prior for the seam carving process using a set of maximum energy seams in an orthogonal direction to the seam carving process. Qualitative and quantitative results prove that the proposed method performs faster and better than the other state-of-the-art image retargeting operators.
Industrial Socio-Cyberphysical System’s Consumables Tokenization for Smart Contracts in Blockchain

As a result of the development of the Industry 4.0 concept, the physical characteristics of production and the production process were transferred to the digital form. This process is also known as production digitalization. Digitalization allows creating so-called digital twins of production components, among which are smart objects, including machines (production robots), people, software services, processed materials and manufactured products. Digital twins allow to display not only the current state of the component, but also predict its actions according the current situation by creating behavior patterns. The interaction of digital twins is carried out through the cloud platform of the Internet of Things using an ontological representation to ensure interoperability. Interaction via cloud IoT platform causes problems associated with providing trust in the distribution of consumables between components of the industrial IoT system. One of the recent solutions to such problems is using of blockchain technology. In this paper, an example of the integration of industrial IoT and blockchain technology is presented. Each component of the system is represented by a corresponding digital twin, capable of interacting on-the-fly with ontologies in the IoT and blockchain network. To solve the problem of control and exchange of consumable resources in IIoT, this paper proposes classification and principles of resource tokenization depending on their class. The tokenization has been checked by creating smart contracts for consumables allocation on Hyperledger Fabric platform.
Learning to Search Efficient DenseNet with Layer-wise Pruning

Deep neural networks have achieved out-standing performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to the dense connections of the internal structure, which leads to high computational costs in inference with such dense networks. To address this issue, we design a reinforcement learning framework to search for efficient DenseNet architectures with layer-wise pruning (LWP) for different tasks, while retaining the original advantages of DenseNet, such as feature reuse, short paths, etc. In this framework, an agent evaluates the importance of each connection between any two block layers, and prunes the redundant connections. In addition, a novel reward-shaping trick is introduced to make DenseNet reach a better trade-off between accuracy and float point operations (FLOPs). Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives.
Dynamics of the a-map over residually finite Dedekind domains and applications

Let D be a residually finite Dedekind domain, a∈D be a nonzero element and n be a nonzero ideal of D. In this paper we describe the dynamics of the map x↦ax over the quotient ring D/n. We further present some applications of our main result.
Distributed track‐to‐track fusion for non‐linear systems with Gaussian mixture noise

The authors propose a distributed state estimation algorithm based on optimal track-to-track fusion for local posteriors in terms of Gaussian mixtures. The track-to-track fusion system is implemented with both parallel and sequential structure based on generalised covariance intersection rule. They obtain the optimal fusion coefficients in a computationally efficient manner via Monte Carlo importance sampling method. The Dirac mixture approximation method is proposed for the computation of arbitrary power of a Gaussian mixture density. The resulting Gaussian mixture fusion rule is analytical and applicable to the multi-sensor case. Numerical examples are presented to demonstrate the performance advantages of the proposed method in comparison with existing track-to-track fusion algorithms.
Consensus-based distributed detection and tracking in clutter with random existence of target

We consider the distributed detection and tracking in the wireless sensor networks (WSNs) where the target may appear in or disappear from the surveillance region at random instances. We derive two consensus-based distributed detection and tracking algorithms. The first algorithm is for the distributed detection and tracking without clutter. With the cubature rule, we derive a centralized implementation with a fictitious fusion center (FC) and then a consensus-based distributed implementation without FC. The second algorithm is for the distributed detection and tracking in the clutter environment. Because of the clutter, it gives rise to more challenging scenarios. Based on the probabilistic data association filter, we develop a distributed algorithm for all nodes to cooperatively deal with the data association uncertainty. Numerical results illustrate that the distributed estimate at each node can be close to the global estimate with the FC which has the information of all nodes in the network.
Estimation of the disorder degree of the classical static noise using three entangled qubits as quantum probes

The paper investigates the estimation of the disorder degree of the classical static noise using three entangled qubits as quantum probes together with the tools of local quantum estimation theory. Three probing schemes namely common environment (CE), independent environments (IEs) and mixed environments (MEs) are investigated and the optimal initial state preparation of the probes taken as a partially depolarized GHZ state. The results show that: (i) the IEs probing scheme allows one to achieve better estimation precision compared to both MEs and CE schemes respectively; (ii) the higher is the initial amount of entanglement of the probes, the larger is the estimation precision, independently of the scheme considered; (iii) both small and large values of the disorder parameter are uniformly estimable at the optimal interaction time; (iv) entangled qubits probes quickly encode information about the disorder parameter than single-qubit probe; (v) there is an improvement in the estimation of the disorder parameter when entangled probes interacting either in IEs or MEs are used instead of a single probe, demonstrating that a single probe is not sufficient to optimally estimate the disorder parameter of the static noise. On the other hand, we have also investigated the relationship between the residual amount of entanglement present in the probes at the optimal interaction time and the estimation precision of the disorder parameter. We show that the higher the residual amount of entanglement at the optimal interaction time, the smaller the estimation precision.
Self-Synchronized Encryption for Physical Layer in 10Gbps Optical Links

In this work a new self-synchronized encryption method for 10 Gigabit optical links is proposed and developed. Necessary modifications to introduce this kind of encryption in physical layers based on 64b/66b encoding, such as 10 GBase-R, have been considered. The proposed scheme encrypts directly the 64b/66b blocks by using a symmetric stream cipher based on an FPE (Format Preserving Encryption) block cipher operating in PSCFB (Pipelined Statistical Cipher Feedback) mode. One of the main novelties in this paper is the security analysis done for this mode. For the first time, an expression for the IND-CPA (Indistinguishability under Chosen-Plaintext Attack) advantage of any adversary over this scheme has been derived. Moreover, it has been concluded that this mode can be considered secure in the same way of traditional modes are. In addition, the overall system has been simulated and implemented in an FPGA (Field Programmable Gate Array). An encrypted optical link has been tested with Ethernet data frames, concluding that it is possible to cipher traffic at this level, getting maximum throughput and hiding traffic pattern from passive eavesdroppers.
A novel method for structure selection of the Recurrent Random Neural Network using multiobjective optimisation

The Random Neural Network (RNN) has extensively investigated over the past few decades; this research has resulted in a considerable number of theoretical and application papers. Although, great effort has been done to develop a systematic procedure to train the recurrent fashion of the RNN, the choice of the number of neurons remains an open question. To overcome this problem, at least partially, this paper uses multiobjective optimisation (MOP) to select the number of neurons. The MOP framework used the mean square error (MSE) and the number of neurons (N) as the objectives to be minimised. The stochastic nondominated algorithm (SNA) to exclude dominated solutions of the Pareto-set has been also introduced. Instead of using only the best solution, candidates to the Pareto-set are excluded by statistical comparison among mean values of the two objectives in all training runs. The SNA allows a statistically correct exclusion of dominated solutions; the best solution can be picked up using classical decision-making procedures. Numerical and real examples illustrate the potentiality of the proposed method in two areas: classification problems and system identification.
How to utilize syllable distribution patterns as the input of LSTM for Korean morphological analysis

This paper proposes the use of syllable distribution patterns as deep learning inputs for morphological analysis. The proposed syllable distribution pattern comprises two parts: a distributed syllable embedding vector and a morpheme syllable-level distribution pattern. As a learning method, we utilize bidirectional long short-term memory with a conditional random field layer (Bi-LSTM-CRF) for Korean part-of-speech tagging tasks. After syllable-level outputs are generated by Bi-LSTM-CRF, a morpheme restoration process is performed utilizing pre-analyzed dictionaries that were automatically created from a training corpus. Experimental results reveal outstanding performance for the proposed method with an F1-score of 98.65%.
A resource-efficient parallel architecture for infrared image stripe noise removal based on the most stable window

Stripe noise is a common phenomenon arises in the multi-detectors infrared imaging equipment, which not only generally destroys the quality of the image, but also seriously affects the subsequent processing of the image. We exploit the stripe noise features of infrared image, and find that the adjacent noise difference can be well described by the difference of the mean values of adjacent rows in infrared images with the mean compensation and the two-point correction principle. Based on this, a novel stripe noise removal algorithm based on stable window is designed, it is not only effective to remove stripe noise in infrared image without causing undesired blurring effects and artifacts, but also efficient in terms of storage and computing resource utilization for real-time processing. In addition, in order to meet the demands of image real-time processing on satellites, we also design a low-latency, low-energy and highly-paralleled architecture for the de-noising algorithm, and implement it on a field programmable gate array (FPGA) processor. Experimental results show that the proposed algorithm outperforms the other current methods, and it can not only has a better performance in visual effect and objective quality than other state-of-the-art de-noising methods, but also has obvious advantages in the consumption of storage resources due to the design of resource-efficient parallel architecture. For instance, in the consumption of Block RAM storage, compared with the TPC method, this proposed algorithm reduces 80% memory, and gets a significant improvement of the processing speed.
Calibrated Bayes factors for model comparison

A Bayes factor between two models can be greatly affected by the prior distributions on the model parameters. When prior information is weak, very dispersed proper prior distributions are known to create a problem for the Bayes factor when competing models differ in dimension, and it is of even greater concern when one of the models is of infinite dimension. Therefore, we propose an innovative method which uses training samples to calibrate the prior distributions so that they achieve a reasonable level of ‘information’. Then the calibrated Bayes factor can be computed over the remaining data. This method makes no assumption on model forms (parametric or nonparametric) and can be used with both proper and improper priors. We illustrate, through simulation studies and a real data example, that the calibrated Bayes factor yields robust and reliable model preferences under various situations.
Multimodal supervised image translation

Multimodal image-to-image translation is a class of vision and graphics problems where the goal is to learn a one-to-many mapping between the source domain and target domain. Given an image in the source domain, the model aims to produce as many diverse results as possible. It is an important and challenging problem in the task of image translation. To this end, recent works utilise Gaussian vectors to produce diverse results but with a small difference. It is because of the special probabilistic nature of Gaussian distribution. In this work, the authors propose linearly distributed latent codes instead of conventional Gaussian vectors, which control the style of generated images. Taking advantage of linear distribution, their model can produce much more diverse results and outperform the state-of-the-art baselines in terms of diversity. Qualitative and quantitative comparisons against baselines demonstrate the effectiveness and superiority of their method.
Holistic VM Placement for Distributed Parallel Applications in Heterogeneous Clusters

In a heterogeneous cluster, virtual machine (VM) placement for a distributed parallel application is challenging due to numerous possible ways of placing the application and complexity of estimating the performance of the application. This study investigates a holistic VM placement technique for distributed parallel applications in a heterogeneous cluster, aiming to maximize the efficiency of the cluster and consequently reduce the costs for service providers and users. The proposed technique accommodates various factors that have an impact on performance in a combined manner. First, we analyze the effects of the heterogeneity of resources, different VM configurations, and interference between VMs on the performance of distributed parallel applications with a wide diversity of characteristics, including scientific and big data analytics applications. We then propose a placement technique that uses a machine learning algorithm to estimate the runtime of a distributed parallel application. To train a performance estimation model, a distributed parallel application is profiled against synthetic workloads that mostly utilize the dominant resource of the application, which strongly affects the application performance, reducing the profiling space dramatically. Through experimental and simulation studies, we show that the proposed placement technique can find good VM placement configurations for various workloads.
Quantifying risk factors in medical reports with a context-aware linear model

We seek to quantify the mortality risk associated with mentions of medical concepts in textual electronic health records (EHRs). Recognizing mentions of named entities of relevant types (eg, conditions, symptoms, laboratory tests or behaviors) in text is a well-researched task. However, determining the level of risk associated with them is partly dependent on the textual context in which they appear, which may describe severity, temporal aspects, quantity, etc.To take into account that a given word appearing in the context of different risk factors (medical concepts) can make different contributions toward risk level, we propose a multitask approach, called context-aware linear modeling, which can be applied using appropriately regularized linear regression. To improve the performance for risk factors unseen in training data (eg, rare diseases), we take into account their distributional similarity to other concepts.The evaluation is based on a corpus of 531 reports from EHRs with 99 376 risk factors rated manually by experts. While context-aware linear modeling significantly outperforms single-task models, taking into account concept similarity further improves performance, reaching the level of human annotators' agreements.Our results show that automatic quantification of risk factors in EHRs can achieve performance comparable to human assessment, and taking into account the multitask structure of the problem and the ability to handle rare concepts is crucial for its accuracy.
O2TR: Offline OTR messaging system under network disruption

Providing a secure and efficient communication system under network disruption without a trusted third party remains a challenging issue. To develop a secure and efficient system in such situations, we extend the conventional Off-The-Record (OTR) protocol into a new protocol named offline OTR (O2TR). O2TR provides end-to-end security between users without requiring the assumption that they are persistently connected to each other. To show the feasibility of the proposed protocol, we implemented a prototype to support O2TR based on the Gajim XMMP instant messaging platform. Our experiments showed that O2TR can be used reliably even when the corresponding network party is temporarily broken down. Moreover, O2TR provides an efficient way to resume private sessions which is about 34% faster than the original OTR. We also proved the secrecy of O2TR using an automated verification tool called AVISPA.
Manifold embedding for zero-shot recognition

Zero-Shot Recognition (ZSR) has gained its popularity recently owing to its promising characteristic that extends the classifiers to the unseen classes. It is typically addressed by resorting to a class semantic space to transfer the knowledge from the seen classes to unseen ones. Therefore, constructing the effective interactions between the visual space and the class semantic space is the key for ZSR. In this paper, under the assumption that the distribution of the semantic categories in the semantic space has an intrinsic manifold structure, we propose two manifold embedding-based ZSR approaches to capture the intrinsic structures of both the visual space and the class semantic space, i.e., ME-ZSR and MCCA-ZSR. Specifically, ME-ZSR builds embedding from visual space to semantic space, while MCCA-ZSR explores to embed both visual and semantic features into a common space. The linear, closed-form solutions make both methods efficient to optimize. Extensive experiments on three popular datasets AwA, CUB and NAB validate the effectiveness of both methods.
CDF Space Covariance Matrix of Gabor Wavelet With Convolutional Neural Network for Texture Recognition

A novel method for texture image recognition is proposed in this paper. The aim of the proposed method is to represent texture by combining the Gabor wavelet transform and deep learning which are efficient techniques for image analysis. We developed the cumulative distribution function (cdf) space covariance model of Gabor wavelet (CSCM-GW), which can jointly model multivariate data in cdf space, in the Gabor wavelet domain to represent texture. The images having different sizes will be transformed by CSCM-GW into same size covariance matrices. Because CSCM-GW is based on the covariance matrix which belongs to Riemannian space, it has the high computational cost in the recognition phase. Therefore, we proposed a novel method of texture recognition called CSCM-GWF-CNN which uses CNN to project the fused covariance of CSCM-GW into low-dimensional vector space for reducing the computational cost and improving the recognition performance. The experiments on Brodatz (111) and KTH-TIPS2-b texture databases show that the proposed method is efficient for texture representation and outperforms most of the state-of-the-art recognition methods.
Design of Wheelchairs Robot Based on ATmega128 to People with Physical Disability

Physical disability is individual to a person having disorder movement, due to its abnormality muscular, bone structure, sick or accident. Wheelchairs are a tool to help people with physical disability. From it, the researcher proposed the creation of a wheelchair robot that was able to be used to assist in daily activities. This robot uses the Atmega128 as the primary system and the motor driver. To movement wheelchairs robot used button command, it gives commands to the Atmega128, and then the motor will drive the wheelchair movement. In the first test is motor rotation setting without load, from the data, obtained the most significant wheel rotation difference from the command is 3rpm, this different rotation because wheel slip at wheelchair and the next test with load people weight 72kg, the difference wheel rotation is 88 rpm, it because the load people at wheelchairs robot. The last test is the command button on wheelchair movement, from the tests performed get a success rate of 87.5%, this is because there was an error when testing the command to move right. From this research expected the wheelchair robot can help people with physical disabilities carrying out at daily activities a wider area.