Reviewerly/exHarmony huggingface dataset:
Reducing non-realistic deformations in registration using precise and reliable landmark correspondences

Non-rigid image registration is prone to non-realistic deformations. In this paper, we proposed a novel landmark-correspondence detection algorithm, with which, the non-realistic deformations in image registration can be reduced. Our method consists of the following steps. First, the landmarks in the reference image are extracted by a corner detector. Then the landmarks are transferred to the template image by the proposed Multiscale Local Rigid Matching (MsLRM) algorithm. A two-stage method is designed for outlier removal before the landmark correspondences are incorporated into a FFD-based registration through a penalty term considering that the interpolating splines in FFD are highly sensitive to outliers. The proposed method was validated on both simulated images and real-world clinical lung dynamic contrast-enhanced magnetic resonance images. The results showed that the proposed MsLRM achieved sub-pixel accuracy, and was robust to local contrast changes. On clinical datasets, the MsLRM-based landmark-constrained registration improved the registration accuracy by at least 25%, compared with the state-of-the-art registration methods. It achieved an average expert landmark distance of 0.23 mm, close to the inter-observer variability of 0.17 mm. We conclude that our novel landmark-constrained registration improves registration performance on dynamic medical images and outperforms the state-of-the-art registration methods.
Analysis of Peripheral Vision and Vibrotactile Feedback During Proximal Search Tasks with Dynamic Virtual Entities in Augmented Reality

A primary goal of augmented reality (AR) is to seamlessly embed virtual content into a real environment. There are many factors that can affect the perceived physicality and co-presence of virtual entities, including the hardware capabilities, the fidelity of the virtual behaviors, and sensory feedback associated with the interactions. In this paper, we present a study investigating participants’ perceptions and behaviors during a time-limited search task in close proximity with virtual entities in AR. In particular, we analyze the effects of (i) visual conflicts in the periphery of an optical see-through head-mounted display, a Microsoft HoloLens, (ii) overall lighting in the physical environment, and (iii) multimodal feedback based on vibrotactile transducers mounted on a physical platform. Our results show significant benefits of vibrotactile feedback and reduced peripheral lighting for spatial and social presence, and engagement. We discuss implications of these effects for AR applications.
Image-Based Visual Servoing of Quadrotor using Homography Reconstruction

In this paper, a novel image-based visual servoing (IBVS) method is proposed for the case that the servoing target randomly and slowly tilts. With the basic IBVS function and virtual camera plane method, a four-dimensional image kinematics is derived. Then a transformation based homography reconstruction method is presented to solve the problem of tilting target plane by defining a new virtual camera plane. Subsequently the IBVS control laws are designed according to the quadrotor dynamics and transformed image kinematics. Finally, simulations show the validity and priority of the new method compared with the classic dynamic IBVS method.
Cyclic distances of idempotent convolutional codes

We show that, for convolutional codes endowed with a cyclic structure, it is possible to define and compute two sequences of positive integers, called cyclic column and row distances, which present a more regular behavior than the classical column and row distance sequences. We then design an algorithm for the computation of the free distance based on the calculation of the cyclic column distance sequence.
Datalog Materialisation in Distributed RDF Stores with Dynamic Data Exchange

Several centralised RDF systems support datalog reasoning by precomputing and storing all logically implied triples using the well-known seminaïve algorithm. Large RDF datasets often exceed the capacity of centralised RDF systems, and a common solution is to distribute the datasets in a cluster of shared-nothing servers. While numerous distributed query answering techniques are known, distributed seminaïve evaluation of arbitrary datalog rules is less understood. In fact, most distributed RDF stores either support no reasoning or can handle only limited datalog fragments. In this paper, we extend the dynamic data exchange approach for distributed query answering by Potter et al. [13] to a reasoning algorithm that can handle arbitrary rules while preserving important properties such as nonrepetition of inferences. We also show empirically that our algorithm scales well to very large RDF datasets.
On the Effect of the Activation Function on the Distribution of Hidden Nodes in a Deep Network

We analyze the joint probability distribution on the lengths of the vectors of hidden variables in different layers of a fully connected deep network, when the weights and biases are chosen randomly according to gaussian distributions. We show that if the activation function φ satisfies a minimal set of assumptions, satisfied by all activation functions that we know that are used in practice, then, as the width of the network gets large, the "length process" converges in probability to a length map that is determined as a simple function of the variances of the random weights and biases and the activation function φ. We also show that this convergence may fail for φ that violate our assumptions. We show how to use this analysis to choose the variance of weight initialization, depending on the activation function, so that hidden variables maintain a consistent scale throughout the network.
AIM: Designing a language for AI models

Describing an unambiguous model for an Artificial Intelligence (AI) problem has been a significant topic in AI for almost 70 years, with the main goal of formalizing a natural language description to allow the computer to solve it. Nowadays, an AI problem is usually modelled as a transitional system, by following four steps: identify a representation for a problem state, describe the initial and final states in that representation, describe valid transitions together with a search strategy that looks for a path between an initial and a final state using the available transitions. This paper proposes a new language for describing AI models with the goal to generate executable code. The proposed language is capable of representing all common types of problems, and models implemented can be adapted easily to any search strategy with specific requirements (such as score functions). The language is fully described in this paper together with several non-trivial examples, while the code generation feature is work-in-progress.
Aspect Analysis towards ArchiMate Diagrams

ArchiMate is the language to model Enterprise Architecture using diagrams. Although ArchiMate is the powerful language to describe the whole content of Enterprise Architecture, it is not easy to learn and use correctly ArchiMate because it has approximately 60 graphical icons. The application knowledge how to use ArchiMate is highly expected. In this paper, we propose the aspect analysis approach to design ArchiMate diagrams. A simple example to develop ArchiMate diagram using the approach is also explained. Moreover, a workshop design to educate ArchiMate using the approach for software engineers is discussed.
Adaptive Frame Rate Optimization Based on Particle Swarm and Neural Network for Industrial Video Stream

The emergence of a large number of video data puts forward higher requirements on traditional video transmission technology. The new streaming media technology based on HTTP dynamic adaptive streaming DASH transmission protocol has become an important research direction of video services. How to overcome the unstable characteristics of wireless links in a limited bandwidth, achieve high-quality intelligent transmission of video, and obtain optimal user quality of experience (QoE), has become an urgent problem to be solved. This paper abandons the traditional streaming media adaptive transmission method, and combines neural network and particle swarm optimization algorithm to design a new intelligent transmission scheme. The particle swarm optimization algorithm obtains the optimal transmission parameters of QoE, and the model established by neural network predicts the optimal one. The system sets parameters to ensure video service quality under limited bandwidth and large network fluctuations in the wireless network.
Image denoising of real photographs with generative adversarial network for data augmentation

Although image denoising methods based on a convolutional neural network (CNN) have achieved the state-of-the-art in Gaussian noise reduction, their performances are still very limited on real photographs even beyond the reach of traditional methods, such as block-matching and 3-D filtering and weighted nuclear norm minimization. We use a denoising benchmark to train a generative adversarial network for noise modeling and produce more data indistinguishable from the original ones, which can be regarded as a data augmentation scheme. Then, we utilize this extended dataset, including real images and synthetic images to train a CNN-based denoiser named DRNet for real photograph denoising. In the design of DRNet, we introduce a noise estimation module to improve the robustness of the single learning framework for handling unknown noise levels and a pair of reversible downsampling and upsampling operators to enlarge the receptive field. Experiments on real-world noisy images are conducted to evaluate our algorithm, and the results show that DRNet is effective for real photographs in comparison with other methods, especially in balancing the noise removal and the structure preservation.
Non-Functional Norms Specification and Verification Approach for Normative Multi-agents Systems

Normative Multi-agent system (NMAS) forms a promising approach to software engineering for the development of autonomous systems, in norms-based environments. During the construction of these systems, different requirements can be considered: functional and non-functional requirement. Existing research in NMAS have mostly focused on the study of functional and behavioral requirements specification and verification, while Non-Functional Requirements (NFR) are crucial in the development of systems. They have dealt with the NFR implicitly through the capabilities of functional requirements. An important research issue refers to NFR in NMAS, i.e. how to specify and enforce NFR in NMAS that involves heterogeneous and autonomous agents. The NFR define constraints for the system and specify the qualities that a system can have, for example, security, performance, reliability, comfort, availability. Also, NFR might conflict among each other (for example, cost and quality, comfort and economy). A conflict between two NFR occurs when the fulfillment of one NFR violates another NFR. One of the main challenges currently faced in NMAS research is that of NFR specification and verification. In particular, how to specify and verify NFR in autonomous systems? In this paper, we propose a normative approach that allows the specification and verification of NFR for autonomous systems. To do this, we propose a new type of norms, called Non-Functional Norms (NFN). Indeed, our approach provides (i) a BNF normative language for specifying NFN, and (ii) a mechanism, based on Constraint Satisfaction Problem (CSP) technique, for the detection and resolution of normative conflicts. Our normative approach is also illustrated by a case study describing Smart City Management System (SCMS).
okralact - a multi-engine Open Source OCR training system

Optical character recognition (OCR) of historical documents has been significantly more difficult than OCR of modern texts largely due to idiosyncrasies and wide variability of font, layout, language, orthography of printed texts before ca. 1850. However, traditional OCR engines were optimized towards supporting the widest possible set of modern text ("OmniFont OCR") with little or no facilities for the user to adapt the engine. Since OCR technologies began embracing deep neural networks, various Free Software OCR engines are now available that can in principle be adapted to different types of documents by training specific models from ground truth (GT). What these engines offer in terms of implementation finesse, they lack in interoperability and standardization. To overcome this, we developed okralact, a set of specifications and a prototypical implementation of an engine-agnostic system for training Open Source OCR engines like Tesseract, OCRopus, kraken or Calamari. We discuss training of these engines, compare their features, describe the specifications and functionality of okralact and outline how a turn-key system for adapting Open Source OCR engines can contribute to better OCR for historical documents and to the general Open Source OCR ecosystem.
Minimum Euclidean distance evaluation using deep neural networks

In this paper, we propose a deep neural network (DNN) based minimum Euclidean distance evaluation method. It is known that a closed form solution of the problem to calculate the minimum Euclidean distance is not available and an exhaustive search is inevitable, which requires prohibitive complexity for practical implementation. In order to reduce the complexity, we consider a supervised machine learning approach, where the minimum Euclidean distance evaluation is modeled as a DNN regression problem. The DNN for evaluating the minimum Euclidean distance is trained with a sufficient number of training data. Numerical results are presented to show that our trained DNN provides enough accuracy to be adopted for a precoder selection problem in multiple-input multiple-output (MIMO) spatial multiplexing systems with maximum likelihood (ML) receivers.
Researching Mobile Learning

m-Learning (mobile learning) is an exciting research field but few studies address the potential effect of human-computer interactions on users' learning experience. A well-designed mobile learning study should consider the potential confounding variables of user context and usability. The purpose of this chapter is to inform readers of current research in m-learning and mobile HCI (human-computer interactions). The chapter will provide an example of a study that was designed to address the important factors of user context and usability.
Daily Global Solar Radiation Based on MODIS Products: The Case Study of ADRAR Region (Algeria)

The Measurements of the solar radiation is important in choosing sites to install projects with solar systems. Adrar region is classified from the high potential solar areas in the world. In this study, we using the atmospheric products of satellite MODIS (Moderate Resolution Imaging Spectroradiometer) and bird model to estimate global solar radiation. The results of estimation were validated with observed data of Adrar station (lat = 36.6, lon = 028). The correlation is very significant and the linear correlation coefficient between estimated and measured values of global solar radiation equal 0.78.
A Common Frame for Formal Imagination

In this paper, we review three influential theories of imagination in order to understand how the dynamics of imagination acts could be modeled using formal languages. While reviewing them, we notice that they are not detailed enough to account for all the mechanisms involved in creating and developing imaginary worlds. We claim those theories could be further refined into what we call the Common Frame for Imagination Acts, which defines a framework that can be used to study the dynamics of imagination using formal languages, and we support our claim by showing how the framework allows to model certain imagination acts step by step. Then, we introduce the Rhombus of Imagination, which is a visual tool that can be used in conjunction with our framework to study the procedural structure of different kinds of imagination acts and identify their dynamic properties.
A Moment Based Feature Extraction for Texture Image Retrieval

The most important tasks for texture-based image retrieval system are efficient method of feature extraction. Invariant image moments, viz., geometric moment (GM) and Zernike moment (ZM) are used for the feature extraction in content-based image retrieval (CBIR) system. GM is not orthogonal, and ZM is orthogonal, but it shows poor reconstruction of images. Hence, they are not fit enough for texture-feature-based image retrieval system. Hence, an efficient texture feature extraction algorithm having low computational time and high retrieval accuracy is required. Legendre moment (LM) is an orthogonal moment and represents any image with almost nil information redundancy. Different methods for the computation of Legendre moment are available. In this paper, a fast and accurate method of calculating LM called exact Legendre moment (ELM) proposed by Hosny is used for feature extraction. Comparison has been carried out among the method described in this paper, ZM and GM. Performance of the proposed method in the presence of Gaussian noise also has been tested and compared with other moment-based feature extraction systems.
Nonlinear Activation Function Generation Based on Silicon Microring Resonators for Integrated Photonic Neural Networks

To overcome the interconnect problem of CMOS Neural Network (NN) implementations (increased power consumption while inhibiting speed), small-scale linear optics-based solutions have been proposed to replace the electronic NN layer in multiple works - e.g. [1-3]. Nevertheless, an all-optical NN is difficult to achieve as it would imply substituting the existing electro-optic signal conversion and digital-driven activation function necessary between NN layers. In this work, we demonstrate how feedback controlled microring resonators (MRR) can be used as activation functions in NNs. The design we focus on is shown in Fig. 1-a. Pulses of light at different frequencies carry signals while weights are applied using PIN ring modulators with proper free spectral range. Pulses are used to ensure that the detuning due to heating of the device is mostly avoided. The light is then coupled in the main ring resonator responsible for the non-linear transfer function. The power dependent response is governed by an interplay between free carrier dispersion and free carrier absorption [4]. An electronic feedback loop will ensure carrier lifetime control, crucial for output stability and reproducibility. The output of this resonator is then filtered again to extract the necessary signal, before passing it to the next NN layer.
Lossless Compression of Grayscale and Colour Images Using Multidimensional CSE

Originally, compression by substring enumeration (CSE) is a lossless compression technique that is intended for strings of bits. As such, the original version is one-dimensional. An extension of CSE for strings drawn from a larger alphabet has later been introduced. Also, CSE has recently been extended to two-dimensional (2D) data. As such, 2D CSE can be used directly to compress images. Unfortunately, CSE generally does not perform on data drawn from large alphabets as well as on binary data. This means that, although we can expect 2D CSE to perform well on bilevel images, we must expect a loss of performance on grayscale and colour images, where the alphabet sizes may be 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">24</sup> , respectively, as in common image formats. As a workaround for this difficulty, we propose to handle grayscale and colour images by remaining in the realm of binary data but by extending CSE to higher dimensions. Grayscale images may have the levels of gray of their pixels decomposed into bit planes and, then, get compressed using a 3D CSE. Colour images may have their three colour channels treated as yet another dimension and, then, get compressed using a 4D CSE. Actual empirical measurements are deferred to another paper as we do not have a working implementation of multidimensional CSE yet.
Consensus on IMM-UKFs for Tracking in Mixed LOS/NLOS Conditions

The problem of distributed target tracking in mixed LOS/NLOS conditions is addressed using average consensus in this paper. Unlike the existing methods that send local measurements or estimates to a fusion center and perform centralized measurement/ track fusion, a totally distributed estimation fusion framework with only one-hop neighbors' information exchange is developed. First, based on local time of arrival (TOA) and received signal strength (RSS) measurements, each robot runs an interactive multiple model of unscented Kalman filter (IMM-UKF) to estimate the target state in parallel. Then to distribute the centralized track fusion algorithm, average consensus is used to estimate global information contribution through information interaction between neighbors. Simulation results show that for the proposed consensus tracking, each robot performs like a fusion center and has nearly identical accuracy to the optimal centralized fusion strategy, while the communication burden has been extensively reduced.
Formal Modeling for Magnetic Resonance Images Tamper Mitigation

The picture archiving and communication system is a medical imaging technology used primarily in healthcare organizations to store and digitally transmit electronic images and clinically-relevant reports. As demonstrated, these systems can be exploited by malicious users: in fact, considering that medical images are not digitally encrypted, any medical image modifications would be difficult to detect for a radiologist. To mitigate this aspect, in this paper a formal modelisation for picture archiving and communication system systems is proposed. The main aim is to avoid illegal writing and reading from components that should not do it, by representing the system components in terms of automa.
Learning attentive dynamic maps (ADMs) for Understanding Human Actions

This paper presents a novel end-to-end trainable deep architecture to learn an attentive dynamic map (ADM) for understanding human motion from skeleton data. An ADM intends not only to capture the dynamic information over the period of human motion, referred to as an action, as the conventional dynamic image/map does, but also to embed in it the spatio-temporal attention for the classification of the action. Specifically, skeleton sequences are encoded into sequences of Skeleton Joint Maps (STMs), each STM encodes both joint location (i.e. spatial) and relative temporal order (i.e. temporal) of the skeleton in the sequence. The STM sequences are fed into a customized 3DConvLSTM to explore the local and global spatio-temporal information from which a dynamic map is learned. This dynamic map is subsequently used to learn the spatio-temporal attention at each time-stamp. ADMs are then generated from the learned attention weights and all hidden states of the 3DConvLSTM and used for action classification. The proposed method achieved competitive performance compared with the state-of-the-art results on the Large Scale Combined dataset, MSRC-12 dataset and NTU RGB+D dataset.