Reviewerly/exHarmony huggingface dataset:
AI Illustrator: Art Illustration Generation Based on Generative Adversarial Network

In recent years, people's pursuit of art has been on the rise. People want computers to be able to create artistic paintings based on descriptions. In this paper, we proposed a novel project, Painting Creator, which uses deep learning technology to enable the computer to generate artistic illustrations from a short piece of text. Our scheme includes two models, image generation model and style transfer model. In the real image generation model, inspired by the application of stack generative adversarial networks in text to image generation, we proposed an improved model, IStackGAN, to solve the problem of image generation. We added a classifier based on the original model and added image structure loss and feature extraction loss to improve the performance of the generator. The generator network can get additional hidden information from the classification information to produce better pictures. The loss of image structure can force the generator to restore the real image, and the loss of feature extraction can verify whether the generator network has extracted the features of the real image set. For the style transfer model, we improved the generator based on the original cycle generative adversarial networks and used the residual block to improve the stability and performance of the u-net generator. To improve the performance of the generator, we also added the cycle consistent loss with MS-SSIM. The experimental results show that our model is improved significantly based on the original paper, and the generated pictures are more vivid in detail, and pictures after the style transfer are more artistic to watch.
Interaction design for paediatric emergency VR training

Virtual reality (VR) in healthcare training has increased adoption and support, but efforts are still required to mitigate usability concerns. This study conducted a usability study of an in-use emergency medicine VR training application, available on commercially available VR hardware and with a standard interaction design. Nine users without prior VR experience but with relevant medical expertise completed two simulation scenarios for a total of 18 recorded sessions. They completed NASA Task Load Index and System Usability Scale questionnaires after each session, and their performance was recorded for the tracking of user errors. s Our results showed a medium (and potentially optimal) Workload and an above average System Usability Score. There was significant improvement in several factors between users' first and second sessions, notably increased Performance evaluation. User errors with the strongest correlation to usability were not directly tied to interaction design, however, but to a limited 'possibility space'. Suggestions for closing this 'gulf of execution' were presented, including 'voice control' and 'hand-tracking', which are only feasible for this commercial product now with the availability of the Oculus Quest headset. Moreover, wider implications for VR medical training were outlined, and potential next steps towards a standardized design identified.
Mining Multiple Fuzzy Frequent Patterns with Compressed List Structures

Fuzzy-set theory was invented to represent more meaningful representations of knowledge for human reasoning, which can also be applied and utilized for handling the quantitative database. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to fast discover the multiple fuzzy frequent patterns from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed to reduce the search space and speed up mining progress. Several experiments are carried out for the purpose of verifying the efficiency and effectiveness of the designed approach in terms of runtime and the number of examined nodes under different minimum support thresholds and the results indicated the designed EFM achieves the best performance compared to the existing models.
Cloud-based elastic architecture for distributed video encoding: Evaluating H.265, VP9, and AV1

Areas with social and business impact such as entertainment, healthcare, surveillance, and e-learning would benefit from improvements in video coding and transcoding services. New codecs, such as AV1, are being developed to deal with new demands for high video resolutions with bandwidth constraints and quality requirements. However, these new codecs have high computational requirements and new strategies are needed to accelerate their processing. Cloud computing offers interesting features such as on-demand resource allocation, multitenancy, elasticity, and resiliency among others. Deploying video coding and transcoding services on these infrastructures is suitable because it allows the adaptation of the resources to the workload, offers high availability, and provides ubiquitous access. This work proposes a cloud-based distributed architecture, tuned for video coding, that relies on an elastic pool of workers and media servers to provide fault-tolerance. A distributed application is deployed on the top of the architecture to split the video encoding process of a video in several jobs that can be dynamically assigned among the elastic pool of workers. The proposed solution is analyzed in terms of scalability, resource usage, and job distribution varying the number of workers for three well-known video codecs: H.265, VP9, and AV1. Moreover, the quality of the encoded videos has been evaluated for different bit rates and number of frames per job using full reference metrics like: PSNR, MS-SSIM, and VIF. Results show that our solution achieve similar quality and bitrate compared with full video coding while improving the total encoding time, which can decrease more than 90% depending on the encoder and the number of workers.
Image Encryption Algorithm Based on Bit-Level Permutation and Dynamic Overlap Diffusion

In order to guarantee the security of digital images, image encryption is a good tool. This article puts forward a novel image encryption/decryption algorithm based on bit-level permutation and dynamic overlap diffusion. Firstly, location information set by users is used to scramble the plain image in the bit-level, changing the pixel position of the image while changing the pixel value. Then, using the plaintext related method to control the iteration parameters of Arnold transform. These two parts together form the permutation process of the algorithm. Thirdly, the overlap steps are set according to the encryption security requirements, and dynamic overlap diffusion is performed on the permutation image. At the same time, the plaintext information entropy and variance are used to update the diffusion key in the five-dimensional hyperchaotic system to achieve the plaintext correlation of the encryption key. Arnold transform and dynamic overlap diffusion can ensure that different key streams are generated when encrypting different images. Finally, experimental analysis and numerical simulation show that our algorithm has high efficiency, high security, high sensitivity, and can effectively resist various attacks.
Software Effort Estimation Using Machine Learning Techniques

The product/software effort/cost-estimation techniques are applied to predict the effort required to finish the project. An incorrect estimation leads to increase in deadline and budget of the project which may further consequence to failure of the project. The estimation models and techniques are used in different phases of software engineering like budgeting, risk analysis, planning, etc. The effort estimation must be done meticulously in SDLC to avoid any slippage to timelines and over budgeting problems. Techniques of effort estimation can be grouped into two categories, i.e. parametric/algorithmic and non-parametric/non-algorithmic models. To overcome the limitations of algorithmic models, non-algorithmic methodologies have been explored which are based on soft-computing methods. Non-algorithmic techniques include Parkinson, expert judgement, machine learning (ML) and price to win. The ML models have been introduced to handle the flaws of parametric estimation models. These models also complement the modern project development and management. Neural networks, fuzzy logic, genetic algorithms, case-based reasoning, etc., are part of the non-algorithmic models. This review paper focuses on software effort estimation techniques based on machine learning techniques, their application domain, method to calculate software cost estimation and analysis on existing ML techniques to explore possible areas of further research.
Dynamic Weighted Heuristic Trust Path Search Algorithm

Trust propagation is being increasingly adopted to assist recommendation systems in providing more reliable information, upon which, users can make more accurate decisions. Optimal trust path search integrating trust value and path length plays a critical role in trust propagation, but suffers from insufficient performance regarding search accuracy and time. Generally, the quality of trust propagation is affected by the path length, and the longer the path is, the worse the trust quality is. However, the length of the path is not the unique crucial factor. Some longer paths with greater trust values may be more credible. In addition, the A* algorithm can find an optimal solution, but it expends much time to distinguish some similar paths. The A* algorithm is improved and a dynamic weighted heuristic trust path search algorithm is proposed. According to the six-degree space theory, the paths are extended to six-degree admissible trust paths. Then, according to the depths of the nodes in the search path, it relaxes the evaluation functionf(n) by devising a dynamic weighted factor w, inserts all nodes satisfied specific conditions into the FOCAL list. Furthermore, it sets the secondary heuristic factor and selects the nodes with the minimum heuristic factor value to reach the target node, and outputs the optimal trust path. Experiments on the public Advogato and FilmTrust datasets demonstrated that the proposed algorithm could efficiently identify the reliable trust paths and predict trust value with high accuracy and reduced computational complexity. The proposed algorithm could be applied to recommendation systems in the future.
Serverless Computing Security: Protecting Application Logic

Serverless computing enables organisations to avail of the inherent and unlimited flexibility and scalability that serverless provides, without having to consider the underlying infrastructure. However, there are security considerations that are unique to serverless architectures, that if not included early in application design, can lead to vulnerabilities which could be exposed to common attack vectors. While cloud service providers manage the security of the underlying infrastructure, it is up to the consumer to ensure that serverless applications are fully protected. We go on to discuss common attack vectors, the risks associated with misconfiguration within security and application setup, how attackers target vulnerabilities within the workflow logic of serverless applications and their functions to focus their attacks, and how consumers can implement measures to protect their applications within a serverless architecture.
Morphological extreme learning machines applied to the detection and classification of mammary lesions

Abstract According to the World Health Organization, breast cancer is one of the deadliest types of cancer, affecting adult women around the world. Early diagnosis can contribute to reduced mortality rates and increased treatment options. Computer-aided image diagnosis has increased in recent decades. Mathematical morphology is composed of a set of techniques for the nonlinear processing of digital images. It is based on the theory of sets and uses the operations of intersection and union of sets for the elaboration of more complex operators. Extreme learning machines are learning machines composed of at least one hidden layer with random-weight neurons and an output layer composed of nodes with linear kernels. The weights of the output neurons are obtained using the Moore–Penrose pseudoinverse. Extreme learning machine networks are able to solve complex classification problems by means of decision boundaries of greater complexity. For this, they use different kernels in the neurons of the hidden layers. In this work, we propose morphological extreme learning machines (mELMs) in which neurons of the hidden layers employ kernels based on morphological erosions and dilations. We validated our method using 2796 mammographies from the Image Recovery in Medical Applications (IRMA) database. We found that mELMs of erosion and dilation may present equivalent or even superior classification performance compared with classic and state-of-the-art classifiers. The IRMA database is composed of four mammary tissues: adipose, fibrous, dense heterogeneous, and dense. We perform experiments with each type of tissue and with the entire database. We extracted attributes using wavelet decomposition and then extracted Haralick moments from the resulting component images.
Cascading Scene and Viewpoint Feature Learning for Pedestrian Gender Recognition

Pedestrian gender recognition plays an important role in smart city. To effectively improve the pedestrian gender recognition performance, a new method, called cascading scene and viewpoint feature learning (CSVFL), is proposed in this article. The novelty of the proposed CSVFL lies on the joint consideration of two crucial challenges in pedestrian gender recognition, namely, scene and viewpoint variation. For that, the proposed CSVFL starts with the scene transfer (ST) scheme, followed by the viewpoint adaptation (VA) scheme in a cascading manner. Specifically, the ST scheme exploits the key pedestrian segmentation network to extract the key pedestrian masks for the subsequent key pedestrian transfer generative adversarial network, with the goal of encouraging the input pedestrian image to have the similar style to the target scene while preserving the image details of the key pedestrian as much as possible. Afterward, the obtained scene-transferred pedestrian images are fed to train the deep feature learning network with the VA scheme, in which each neuron will be enabled/disabled for different viewpoints depending on whether it has contribution on the corresponding viewpoint. Extensive experiments conducted on the commonly used pedestrian attribute data sets have demonstrated that the proposed CSVFL approach outperforms multiple recently reported pedestrian gender recognition methods.
A comparative analysis of denoising algorithms for extragalactic imaging surveys

Aims. We present a comprehensive analysis of the performance of noise-reduction (denoising) algorithms to determine whether they provide advantages in source detection, mitigating noise on extragalactic survey images. Methods. The methods we analyze here are representative of different algorithmic families: Perona-Malik filtering, bilateral filter, total variation denoising, structure-texture image decomposition, non-local means, wavelets, and block-matching We tested the algorithms on simulated images of extragalactic fields with resolution and depth typical of the Hubble , Spitzer , and Euclid Space Telescopes, and of ground-based instruments. After choosing their best internal parameters configuration, we assessed their performance as a function of resolution, background level, and image type, in addition to testing their ability to preserve the objects fluxes and shapes. Finally, we analyze, in terms of completeness and purity, the catalogs that were extracted after applying denoising algorithms on a simulated Euclid Wide Survey VIS image and on real H160 and K -band (HAWK-I) observations of the CANDELS GOODS-South field. Results. Denoising algorithms often outperform the standard approach of filtering with the point spread function (PSF) of the image. Applying structure-texture image decomposition, Perona-Malik filtering, the total variation method by Chambolle, and bilateral filtering on the Euclid -VIS image, we obtain catalogs that are both more pure and complete by 0.2 magnitude than those based on the standard approach. The same result is achieved with the structure-texture image decomposition algorithm applied on the H160 image. The relative advantage of denoising techniques with respect to PSF filtering rises with increasing depth. Moreover, these techniques better preserve the shape of the detected objects with respect to PSF smoothing. Conclusions. Denoising algorithms provide significant improvements in the detection of faint objects and enhance the scientific return of current and future extragalactic surveys. We identify the most promising denoising algorithms among the 20 techniques considered in this study.
Developing shared qualitative models for complex systems

Abstract Understanding complex systems is essential to ensure their conservation and effective management. Models commonly support understanding of complex ecological systems and, by extension, their conservation. Modeling, however, is largely a social process constrained by individuals’ mental models (i.e., a small‐scale internal model of how a part of the world works based on knowledge, experience, values, beliefs, and assumptions) and system complexity. To account for both system complexity and the diversity of knowledge of complex systems, we devised a novel way to develop a shared qualitative complex system model. We disaggregated a system (carbonate coral reefs) into smaller subsystem modules that each represented a functioning unit, about which an individual is likely to have more comprehensive knowledge. This modular approach allowed us to elicit an individual mental model of a defined subsystem for which the individuals had a higher level of confidence in their knowledge of the relationships between variables. The challenge then was to bring these subsystem models together to form a complete, shared model of the entire system, which we attempted through 4 phases: develop the system framework and subsystem modules; develop the individual mental model elicitation methods; elicit the mental models; and identify and isolate differences for exploration and identify similarities to cocreate a shared qualitative model. The shared qualitative model provides opportunities to develop a quantitative model to understand and predict complex system change.
Facial Expression Imitation Method for Humanoid Robot Based on Smooth-Constraint Reversed Mechanical Model (SRMM)

To improve the space-time similarity and motion smoothness of facial expression imitation (FEI), a real-time FEI method for a humanoid robot is proposed based on smooth-constraint reversed mechanical model (SRMM) by combining a sequence-to-sequence deep learning model and a motion-smoothing constraint. First, on the basis of facial data from a Kinect capture device, a facial feature vector is characterized based on 3 head postures, 17 facial animation units, and facial geometric deformation cascaded by Laplace coordinates. Second, a reversed mechanical model is constructed via a multilayer long short-term memory neural network to accomplish direct mapping from facial feature sequences to motor position sequences. Additionally, to overcome the motor chattering phenomenon during real-time FEI, a high-order polynomial is constructed to fit the position sequence of motors, and an SRMM is proposed and designed based on the deviation of position, velocity, and acceleration. Finally, aiming to imitate the real-time facial feature sequences of a performer captured from Kinect, the optimal position sequences generated based on the SRMM is sent to the hardware system to keep the space-time characteristics consistent with those of the performer. The experimental results demonstrate that the motor position deviation of the SRMM is less than 8%. The space-time similarity between the robot and the performer is greater than 85%, and the motion smoothness of the online FEI exceeded 90%. Compared with other related methods, the proposed method achieves a remarkable improvement in motor position deviation, space-time similarity, and motion smoothness.
From Particles to Self-Localizing Tracklets: A Multilayer Particle Filter-Based Estimation for Dynamic Grid Maps

One of the indispensable functions of a self-driving vehicle is to estimate its dynamic world, which includes various traffic participants within complex driving scenarios. The estimation mechanism has to be flexible, fast, and robust; however, achieving these requirements is still challenging. Dynamic grid maps are one of the possible ways to combine and estimate multisensory information at an intermediate level. In this article, we present a particle filter (PF)-based grid map estimation that addresses several challenges. First, we propose a multilayer PF-based tracking solution that uses two measurement grid channels as inputs: an occupancy grid and a semantic grid. Second, we introduce the concept of structuring the particle population into batches, where each batch represents an individual tracklet. Rather than using one PF for estimating the entire grid, we employ multiple individual PFs (tracklets) that share the same world. Third, a concept of "self-localizing" tracklets is presented. Similar to simultaneous localization and mapping approaches, in our tracking solution, every particle state is extended with a small set of landmarks. This allows a tracklet to "selflocalize" itself with respect to a tracked-object boundary and leads to a more precise velocity estimation. Finally, we introduce an advanced tracklet-management mechanism that allows executing some specific PF operations at the tracklet level. This optimization provides multiple advantages. Experimental results with ground-truth data show improvement in estimation accuracy when compared to similar techniques.
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial optimization

In recent years, Multifactorial optimization (MFO) has gained a notable momentum in the research community. MFO is known for its inherent capability to efficiently address multiple optimization tasks at the same time, while transferring information among such tasks to improve their convergence speed. On the other hand, the quantum leap made by Deep Q Learning (DQL) in the Machine Learning field has allowed facing Reinforcement Learning (RL) problems of unprecedented complexity. Unfortunately, complex DQL models usually find it difficult to converge to optimal policies due to the lack of exploration or sparse rewards. In order to overcome these drawbacks, pre-trained models are widely harnessed via Transfer Learning, extrapolating knowledge acquired in a source task to the target task. Besides, meta-heuristic optimization has been shown to reduce the lack of exploration of DQL models. This work proposes a MFO framework capable of simultaneously evolving several DQL models towards solving interrelated RL tasks. Specifically, our proposed framework blends together the benefits of meta-heuristic optimization, Transfer Learning and DQL to automate the process of knowledge transfer and policy learning of distributed RL agents. A thorough experimentation is presented and discussed so as to assess the performance of the framework, its comparison to the traditional methodology for Transfer Learning in terms of convergence, speed and policy quality, and the intertask relationships found and exploited over the search process.