Reviewerly/exHarmony huggingface dataset:
Cascade learning from adversarial synthetic images for accurate pupil detection

Image-based pupil detection, which aims to find the pupil location in an image, has been an active research topic in computer vision community. Learning-based approaches can achieve preferable results given large amounts of training data with eye center annotations. However, there are limited publicly available datasets with accurate eye center annotations and it is unreliable and time-consuming for manually labeling large amounts of training data. In this paper, inspired by learning from synthetic data in Parallel Vision framework, we introduce a step of parallel imaging built upon Generative Adversarial Networks (GANs) to generate adversarial synthetic images. In particular, we refine the synthetic eye images by the improved SimGAN using adversarial training scheme. For the computational experiments, we further propose a coarse-to-fine pupil detection framework based on shape augmented cascade regression models learning from the adversarial synthetic images. Experiments on benchmark databases of BioID, GI4E, and LFW show that the proposed work performs significantly better over other state-of-the-art methods by leveraging the power of cascade regression and adversarial image synthesis.
Parameter optimization and real-time calibration of a measurement-device-independent quantum key distribution network based on a back propagation artificial neural network

The parameters choosing (such as probabilities of choosing X-basis or Z-basis, intensity of signal state and decoy state, etc.) and system calibrating will be more challenging when the number of users of a measurement-device-independent quantum key distribution(MDI-QKD) network becomes larger. At present, people usually use optimization algorithms to search the best parameters. This method can find the optimized parameters accurately but may cost lots of time and hardware resources. It's a big problem in large scale MDI-QKD network. Here, we present a new method, using Back Propagation Artificial Neural Network(BPNN) to predict, rather than searching the optimized parameters. Compared with optimization algorithms, our BPNN is faster and more lightweight, it can save system resources. Another big problem brought by large scale MDI-QKD network is system recalibration. BPNN can support this work in real time, and it only needs to use some discarded data generated from communication process, rather than require us to add additional devices or scan the system
Particle swarm optimization with damping factor and cooperative mechanism

A novel variant of particle swarm optimization with damping factor and cooperation mechanism (PSO-DFCM) to search the global optima in a large scale and high-dimensional searching space. In this optimal searching strategy, one balances the exploring and exploiting abilities of particles by introducing a new parameter, a damping factor α, which is used to adjust the position information inherited from the previous state. The cooperative mechanism between the global-best-oriented and the local-best-oriented swarms is employed to help find the global optima quickly. In order to reduce the negative effect of unfavorable particles on swarm evolution, a new concept of evolution history, the least optimal particle in individuals' histories — pleast, is defined to decide whether current information of particles is abandoned and reinitialized in our proposal. Also, fuzzy c-means clustering is applied to cluster the particles' positions for the neighborhood establishment of individuals. Our comparative study on benchmark test functions demonstrates that the proposed PSO outperforms the standard PSO and three state-of-art variants of PSO in terms of global optimum convergence and final optimal results.
Image-to-Markup Generation via Paired Adversarial Learning

Motivated by the fact that humans can grasp semantic-invariant features shared by the same category while attention-based models focus mainly on discriminative features of each object, we propose a scalable paired adversarial learning (PAL) method for image-to-markup generation. PAL can incorporate the prior knowledge of standard templates to guide the attention-based model for discovering semantic-invariant features when the model pays attention to regions of interest. Furthermore, we also extend the convolutional attention mechanism to speed up the image-to-markup parsing process while achieving competitive performance compared with recurrent attention models. We evaluate the proposed method in the scenario of handwritten-image-to-LaTeX generation, i.e., converting handwritten mathematical expressions to LaTeX. Experimental results show that our method can significantly improve the generalization performance over standard attention-based encoder-decoder models.
BDI personal medical assistant agents: The case of trauma tracking and alerting

Personal assistant agents can have an important role in healthcare as a smart technology to support physicians in their daily work, helping to tackle the increasing complexity of their task environment. In this paper we present and discuss a personal medical assistant agent technology for trauma documentation and management, based on the Belief-Desire-Intention (BDI) architecture. The purpose of the personal assistant agent is twofold: to assist the Trauma Team in doing precision tracking during a trauma resuscitation, so as to (automatically) produce an accurate documentation of the trauma, and to generate alerts at real-time, to be eventually displayed either on smart-glasses or room-display.
An analysis and classification of public information security data sources used in research and practice

In order to counteract today's sophisticated and increasing number of cyber threats the timely acquisition of information regarding vulnerabilities, attacks, threats, countermeasures and risks is crucial. Therefore, employees tasked with information security risk management processes rely on a variety of information security data sources, ranging from inter-organizational threat intelligence sharing platforms to public information security data sources, such as mailing lists or expert blogs. However, research and practice lack a comprehensive overview about these public information security data sources, their characteristics and dependencies. Moreover, comprehensive knowledge about these sources would be beneficial to systematically use and integrate them to information security processes. In this paper, a triangulation study is conducted to identify and analyze public information security data sources. Furthermore, a taxonomy is introduced to classify and compare these data sources based on the following six dimensions: (1) Type of information, (2) Integrability, (3) Timeliness, (4) Originality, (5) Type of Source,and (6) Trustworthiness. In total, 68 public information security data sources were identified and classified. The investigations showed that research and practice rely on a large variety of heterogeneous information security data sources, which makes it more difficult to integrate and use them for information security and risk management processes.
Cryptanalysis of the integrated chaotic systems based image encryption algorithm

Chaotic image encryption has been favored by more and more researchers, various techniques and chaotic systems have been introduced. Although chaotic systems have many advantages in image encryption, imperfect algorithm designs would compromise the security of chaotic image encryption algorithms. With this in mind, we carefully investigate the ICS-IE (Integrated Chaotic Systems based Image Encryption) algorithm reported in the most recent research paper. In this paper, we firstly point out some issues found in the ICS-IE algorithm, and then improve it. Without changing the security of the original algorithm, we cryptanalyze the improved ICS-IE algorithm and propose a chosen plaintext attack algorithm. With the moduli used in the ICS-IE algorithm and the proposed attack algorithm, some tests are carried out. Test results show that our analysis of modulus is correct, and the proposed algorithm can recover the plain images completely without knowing any secure key related information. Finally, some suggestions on improving the security of the ICS-IE algorithm are presented.
Back to the Roots: Multi-X Evolutionary Computation

Over the years, evolutionary computation has come to be recognized as one of the leading algorithmic paradigms in the arena of global black box optimization. The distinguishing facets of evolutionary methods, inspired by Darwin’s foundational principles of natural selection, stem mainly from their population-based search strategy—which gives rise to the phenomenon of implicit parallelism. Precisely, even as an evolutionary algorithm manipulates a population of a few candidate solutions (or: individuals), it is able to simultaneously sample, evaluate, and process a vast number of regions of the search space. This behavior is in effect analogous to our inherent cognitive ability of processing diverse information streams (such as sight and sound) with apparent simultaneity in different regions of our brain. For this reason, evolutionary algorithms have emerged as the method of choice for those search and optimization problems where a collection of multiple target solutions (that may be scattered throughout the search space) are to be found in a single run. With the above in mind, in this paper we return to the roots of evolutionary computation, with the aim of shedding light on a variety of problem settings that are uniquely suited for exploiting the implicit parallelism of evolutionary algorithms. Our discussions cover established concepts of multi-objective and multi-modal optimization, as well as new (schema) theories pertaining to emerging problem formulations that entail multiple searches to be carried out at once. We capture associated research activities under the umbrella term of multi-X evolutionary computation, where X, as of now, represents the following list: {“objective,” “modal,” “task,” “level,” “hard,” “disciplinary,” “form”}. With this, we hope that the present position paper will serve as a catalyst for effecting further research efforts into such areas of optimization problem-solving that are well-aligned with the fundamentals of evolutionary computation; in turn prompting the steady update of the list X with new applications in the future.
Volumetric calibration enhancements for single-camera light-field PIV

This work presents a volumetric calibration method for single-camera light-field particle image velocimetry (light-field PIV or LF-PIV). The proposed technique makes use of the unique point-like feature of particle light-field images to accurately determine affected pixels for a spatial voxel over a relative large measurement volume. A calibration model is derived based on Gaussian optics, which relates a spatial point light source with its confusion circle produced on microlens array (MLA), and optical distortions are accounted for by introducing five calibration parameters. By taking lens defects and misalignment between MLA and image sensor into account, the calibration method can calculate weighting coefficient for particle image reconstruction more accurately than the theoretical ray-tracing method, especially for regions further away from focal plane where light ray deflections are significant due to optical distortions. The volumetric calibration method was validated by simulation tests using synthetic light-field images, and has been successfully applied to a classic vortex-ring LF-PIV measurement, where the measurable range in depth direction was successfully extended and the quality of reconstructed volumetric velocity field was greatly improved.
Approximation of Fuzzy Sets by Interval Type-2 Trapezoidal Fuzzy Sets

In this paper, we propose a gradient-based method to approximate a fuzzy set through a trapezoidal fuzzy set (TFS). By adding some constraints in the formulated optimization problem, the major characteristics of the fuzzy set such as the core, the major part of the support, and the shape of the membership function could be preserved; also the form of the optimized result as a TFS is guaranteed. We regard the optimized TFS as the "skeleton" (blueprint) of the original fuzzy set. Based on this skeleton, we further extend the TFS to a higher type, that is, an interval type-2 TFS (IT2 TFS), so that more information about the original fuzzy set could be captured but the number of the parameters used to describe the original fuzzy set is still maintained low (nine parameters are required for an IT2 TFS). The principle of justifiable granularity is used to ensure that the formed type-2 information granule exhibits a sound interpretation. Both synthetic fuzzy sets and those constructed by the fuzzy C -means algorithm applied to the publicly available data have been used to demonstrate the usefulness of the proposed approximation methods.
Tracking Accuracy of a Stereo Camera-Based Augmented Reality Navigation System for Orthognathic Surgery

Tracking accuracy is critical to successful augmented reality (AR) in the diagnosis and surgical correction of maxillofacial deformities. The present study investigated the tracking accuracy of an AR navigation system combined with a stereo camera during repositioning of the maxilla after a Le Fort I osteotomy using a 3-dimensional skull model and compared the tracking accuracy with that of an existing infrared (IR)-based optical tracking system (OTS).Five maxillary surgery plans were designed using a 6 degrees-of-freedom articulator that allowed maxillary movement to be set up quantitatively (target distance, 5 mm). To evaluate the accuracy of the stereo camera AR navigation system, it was compared with a commercially available and commonly used IR-based OTS.The mean error was 0.0584 mm in the IR-based OTS and 0.0596 mm in the AR navigation system. The mean accuracy was 98.83% in the IR-based OTS and 98.81% in the AR navigation system.In this study, the stereo camera-based AR navigation system fabricated and analyzed by the authors was designed for accuracy. The experiments showed its reliability and accuracy. The hardware developed for this AR navigation system displayed accuracy similar to that of existing high-cost imported devices at a substantially lower cost. In addition to surgery, potential applications of the AR navigation system include patient communication and training for novice clinicians.
Usability in agile software development: A tertiary study

In the last years the interest in developing research on integration of usability and agile software development has been increasing. The number of systematic literature reviews, systematic mapping studies and non-systematic reviews, related to this thematic has also increased. Nevertheless, there is no analysis on the quality of these published secondary studies, nor is there a consolidated research that brings the answer of how to integrate these two areas. The goal of this paper is to categorize secondary studies related to the integration of usability and agile software development and present a critical analysis on the quality of the selected studies. To accomplish this goal a tertiary study was performed to categorize the related studies selected. Initially 3,065 papers were identified and further narrowed to 14 by applying exclusion criteria and analysis. We classified the selected studies as systematic literature reviews, systematic mapping studies and non-systematic literature reviews to report the data analysis. As a result of this study different forms to integrate usability and agile software development were detected as well as the various challenges that must be overcome for the integration success. Six main categories were identified to represent ways of integrating usability into agile development: processes, techniques, practices, recommendations, principles and different approaches. Regarding to the challenges for the integration seven main categories were also identified: issues related to tests, time, work balance, modularization, feedback, prioritization, and documentation. Although the interest in researching the integration of usability and agile software development has increased in the last years, mostly of the analyzed studies neglected the quality criteria and presented difficulties to use methods to synthetize the research results. Despite this, it has been realized that the integration of usability with agile software development is possible and is strongly aligned with user-centered design. The initial studies indicated a separation of activities and roles into specific tracks with parallel work to treat usability in agile software development, but the trend is no longer to manage and control these activities in separate ways, so new challenges are becoming to appear. Although we have identified several points of tension, the integration does not become unfeasible.
Research on image restoration algorithms based on BP neural network

With the development of information transmission technology and computer technology, information acquisition mode is mainly converted from character to image nowadays. However, in the process of acquiring and transmitting images, image damage and quality decrease due to various factors. Therefore, how to restore image has become a research hotspot in the field of image processing. This paper establishes an image restoration model based on BP neural network. The simulation results show that the proposed method has made a great improvement compared with the traditional image restoration method.
Experiences from a mobile-based behaviour change campaign on maternal and child nutrition in rural India

Social and behaviour change communication interventions are believed to gain from using mobile technology owing to the reach and rapid scaling promise of technology. In this paper we describe our experiences from the layering of an IVR based content platform for behaviour change in maternal and child nutrition practices in the state of Bihar in rural central India. We specifically show that being able to leverage technology effectively can be quite complex and needs a careful implementation design. We demonstrate that technology adoption takes time and has to be encouraged through offline mechanisms. We also demonstrate that diversification of content on the IVR platform can facilitate greater usage of the platform. We outline the technology assisted concurrent monitoring methods developed by us, and how the data analysis helps identify gaps to take timely corrective action. Finally, we discuss the pros and cons of different pathways to reach women users in rural areas. The insights derived from our work can serve as guidelines for designing technology-based behaviour change campaigns.
Ensemble multi‐objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps

Many methods aim to use data, especially data about gene expression based on high throughput genomic methods, to identify complicated regulatory relationships between genes. The authors employ a simple but powerful tool, called fuzzy cognitive maps (FCMs), to accurately reconstruct gene regulatory networks (GRNs). Many automated methods have been carried out for training FCMs from data. These methods focus on simulating the observed time sequence data, but neglect the optimisation of network structure. In fact, the FCM learning problem is multi-objective which contains network structure information, thus, the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm (MOEA), called EMOEAFCM -GRN, to reconstruct GRNs based on FCMs. In EMOEAFCM -GRN, the MOEA first learns a series of networks with different structures by analysing historical data simultaneously, which is helpful in finding the target network with distinct optimal local information. Then, the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network. The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEAFCM -GRN is efficient and able to reconstruct GRNs accurately.
Robust adaptive learning approach to self-organizing maps

The Self-Organizing Map (SOM) has attracted much research in diverse fields of study, where it has been successfully applied to a wide range of artificial intelligence applications. In this paper, a new intelligent adaptive learning SOM, in contrast with the conventional SOM algorithm, is presented. The proposed SOM overcomes the disadvantages of the conventional SOM by deriving a new variable learning rate that can adaptively achieve the optimal weights and obtain the winner neurons with a shorter learning process time. To quantify the performance, the proposed SOM was compared with various unsupervised algorithms to examine how well the network weight update equation adapts and reaches the optimum weights with fewer iterations. Extensive experiments were conducted using eight different databases from the UCI and KEEL repositories. The proposed SOM algorithm was further compared with the conventional SOM, GF-SOM, PLSOM, and PLSOM2 algorithms. The proposed SOM algorithm showed superiority in terms of the convergence rate, Quantization Error (QE), Topology Error (TE) preserving map, and accuracy during the classification process.
A Lagrangian Relaxation Approach for Binary Multiple Instance Classification

In the standard classification problems, the objective is to categorize points into different classes. Multiple instance learning (MIL), instead, is aimed at classifying bags of points, each point being an instance. The main peculiarity of a MIL problem is that, in the learning phase, only the label of each bag is known whereas the labels of the instances are unknown. We discuss an instance-level learning approach for a binary MIL classification problem characterized by two classes of instances, positive and negative, respectively. In such a problem, a negative bag is constituted only by negative instances, while a bag is positive if it contains at least one positive instance. We start from a mixed integer nonlinear optimization model drawn from the literature and the main result we obtain is to prove that a Lagrangian relaxation approach, equipped with a dual ascent scheme, allows us to obtain an optimal solution of the original problem. The relaxed problem is tackled by means of a block coordinate descent (BCD) algorithm. We provide, finally, the results of our implementation on some benchmark data sets.