Reviewerly/exHarmony huggingface dataset:
Transferring structural knowledge across cognitive maps in humans and models

Abstract Relations between task elements often follow hidden underlying structural forms such as periodicities or hierarchies, whose inferences fosters performance. However, transferring structural knowledge to novel environments requires flexible representations that are generalizable over particularities of the current environment, such as its stimuli and size. We suggest that humans represent structural forms as abstract basis sets and that in novel tasks, the structural form is inferred and the relevant basis set is transferred. Using a computational model, we show that such representation allows inference of the underlying structural form, important task states, effective behavioural policies and the existence of unobserved state-trajectories. In two experiments, participants learned three abstract graphs during two successive days. We tested how structural knowledge acquired on Day-1 affected Day-2 performance. In line with our model, participants who had a correct structural prior were able to infer the existence of unobserved state-trajectories and appropriate behavioural policies.
Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark

Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.
Developer-Driven Code Smell Prioritization

Code smells are symptoms of poor implementation choices applied during software evolution. While previous research has devoted effort in the definition of automated solutions to detect them, still little is known on how to support developers when prioritizing them. Some works attempted to deliver solutions that can rank smell instances based on their severity, computed on the basis of software metrics. However, this may not be enough since it has been shown that the recommendations provided by current approaches do not take the developer's perception of design issues into account. In this paper, we perform a first step toward the concept of developer-driven code smell prioritization and propose an approach based on machine learning able to rank code smells according to the perceived criticality that developers assign to them. We evaluate our technique in an empirical study to investigate its accuracy and the features that are more relevant for classifying the developer's perception. Finally, we compare our approach with a state-of-the-art technique. Key findings show that the our solution has an F-Measure up to 85% and outperforms the baseline approach.
Blockchain and cryptocurrencies: A classification and comparison of architecture drivers

Abstract Blockchain is a decentralized transaction and data management solution, the technological leap behind the success of Bitcoin and other cryptocurrencies. As the variety of existing blockchains and distributed ledgers continues to increase, adopters should focus on selecting the solution that best fits their needs and the requirements of their decentralized applications, rather than developing yet another blockchain from scratch. In this article we present a conceptual framework to aid software architects, developers, and decision makers to adopt the right blockchain technology. The framework exposes the interrelation between technological decisions and architectural features, capturing the knowledge from existing academic literature, industrial products, technical forums/blogs, and experts' feedback. We empirically show the applicability of our framework by dissecting the platforms behind Bitcoin and other top 10 cryptocurrencies, aided by a focus group with researchers and industry practitioners. Then, we leverage the framework together with key notions of the architectural tradeoff analysis method to analyze four real‐world blockchain case studies from industry and academia. Results shown that applying our framework leads to a deeper understanding of the architectural tradeoffs, allowing to assess technologies more objectively and select the one that best fit developers' needs, ultimately cutting costs, reducing time‐to‐market and accelerating return on investment.
A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization

Medical image segmentation demonstrates a significant part in curative image exploration and dispensation, is a multifaceted and perplexing assignment for reckoning efficiency and dissection precision. Segmenting an image is essential to dissection different components of the image, which is prominent fact to identify region of defect accurately. An Intelligent Fuzzy Level Set Method (IFLSM) along with an over-all search proficiency of Improved Quantum Particle Swarm Optimization (IQPSO) for image segmentation is proposed to improve the steadiness and meticulousness thus aiming at reduction of opening sensitivity. The proposed algorithm aims at optimizing the opening contours by utilizing the IQPSO method in addition with intelligent fuzzy clustering method, and segments the image using enhanced Level Set Method (LSM). A stable cluster head is identified using the comprehensive quest aptitude of IQPSO. The iteration period will also provide a pre-segmentation contour which is nearer to Region of Interest (ROI). The implementation of the proposed work for segmenting brain tissues through Magnetic Image Resonance (MRI) images provides an optimized result which is 15% more than the original FLSM algorithm. The obtained contours from the proposed work shows more stability than the original FLSM. The proposed work shows a promising significant improvement in the image segmentation process.
Gang of eight

Defects in infrastructure as code (IaC) scripts can have serious consequences, for example, creating large-scale system outages. A taxonomy of IaC defects can be useful for understanding the nature of defects, and identifying activities needed to fix and prevent defects in IaC scripts. The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by developing a defect taxonomy for IaC scripts through qualitative analysis. We develop a taxonomy of IaC defects by applying qualitative analysis on 1,448 defect-related commits collected from open source software (OSS) repositories of the Openstack organization. We conduct a survey with 66 practitioners to assess if they agree with the identified defect categories included in our taxonomy. We quantify the frequency of identified defect categories by analyzing 80,425 commits collected from 291 OSS repositories spanning across 2005 to 2019.
A new structure for representing and tracking version information in a deep time knowledge graph

Ontologies and vocabularies are an effective way to promote data interoperability in open data and open science. The deep time knowledge graph is one of the most discussed and studied topics in geoscience ontologies and vocabularies. The continuous evolution of deep time concepts calls for a mechanism of version control and organization to reduce the semantic ambiguity. In this paper we propose a new structure for version control and tracking of concepts, attributes and topological relationships in the deep time knowledge graph. In our work we have reused the existing ontologies for geologic time scale and vocabularies for the International (Chrono)stratigraphic Chart (ISC). Through the new structure, we are able to represent the whole version history of the ISC charts (from 2004 to 2018) in a single knowledge graph. Moreover, the resulting knowledge graph is consistent with the existing ontologies and vocabularies. Experiments of SPARQL queries prove the efficiency of this structure for version tracking of concepts and attributes. We are now extending the knowledge graph with concepts from regional and local geologic time standards, such as North America, Europe, Britain, China, and Australia, and building a graphic user interface for the services. In a future work, we will implement the knowledge graph in data integration workflows. We hope this research will spur more discussion and development of methods for version control of knowledge graphs in geoscience and other disciplines.
Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions

In this paper, we present Neural-Swarm, a nonlinear decentralized stable controller for close-proximity flight of multirotor swarms. Close-proximity control is challenging due to the complex aerodynamic interaction effects between multirotors, such as downwash from higher vehicles to lower ones. Conventional methods often fail to properly capture these interaction effects, resulting in controllers that must maintain large safety distances between vehicles, and thus are not capable of close-proximity flight. Our approach combines a nominal dynamics model with a regularized permutation-invariant Deep Neural Network (DNN) that accurately learns the high-order multi-vehicle interactions. We design a stable nonlinear tracking controller using the learned model. Experimental results demonstrate that the proposed controller significantly outperforms a baseline nonlinear tracking controller with up to four times smaller worst-case height tracking errors. We also empirically demonstrate the ability of our learned model to generalize to larger swarm sizes.
Augmented reality integration into MES for connected workers

In this paper an overview of recent augmented reality (AR) solutions for manufacturing execution systems (MES) is presented. The first part of the paper describes the challenges of integrating AR into MES, while the second part focuses on custom AR solutions. The last part of the paper highlights the advantages of the proposed approaches, as well as real life experimental results. Experiments are described in detail while the code for these applications is made public on author’s website.
Human Cognition Through the Lens of Social Engineering Cyberattacks

Social engineering cyberattacks are a major threat because they often prelude sophisticated and devastating cyberattacks. Social engineering cyberattacks are a kind of psychological attack that exploits weaknesses in human cognitive functions. Adequate defense against social engineering cyberattacks requires a deeper understanding of what aspects of human cognition are exploited by these cyberattacks, why humans are susceptible to these cyberattacks, and how we can minimize or at least mitigate their damage. These questions have received some amount of attention, but the state-of-the-art understanding is superficial and scattered in the literature. In this paper, we review human cognition through the lens of social engineering cyberattacks. Then, we propose an extended framework of human cognitive functions to accommodate social engineering cyberattacks. We cast existing studies on various aspects of social engineering cyberattacks into the extended framework, while drawing a number of insights that represent the current understanding and shed light on future research directions. The extended framework might inspire future research endeavor toward a new sub-field that can be called Cybersecurity Cognitive Psychology, which tailors or adapts principles of Cognitive Psychology to the cybersecurity domain while embracing new notions and concepts that are unique to the cybersecurity domain.
Path planning for autonomous underwater vehicle based on an enhanced water wave optimization algorithm

The water wave optimization (WWO) algorithm is inspired by shallow water wave theory and mainly simulates propagation, refraction and breaking to obtain the global optimal solution in the search space. Due to its premature convergence and low optimization efficiency, the basic WWO has a slow convergence speed and low calculation accuracy. To improve the overall optimization performance of the basic WWO, an enhanced WWO based on the elite opposition-based learning strategy and the simplex method (ESWWO) is proposed to solve the function optimization problem and path planning problem for an autonomous underwater vehicle (AUV). The elite opposition-based learning strategy increases the diversity of the population and enhances the global search ability to avoid falling into the local optimum. The simplex method has a fast search speed and strong local search ability to obtain a very accurate solution. The ESWWO algorithm can not only achieve complementary advantages to improve the optimization efficiency of the basic WWO but can also balance exploration and exploitation to obtain the global optimal solution. For the function optimization problem, the ESWWO has strong stability and robustness, and the fitness values of the ESWWO are better than those of other algorithms. For the AUV path planning problem, the ESWWO can avoid threat areas with a minimum fuel cost to obtain the optimal path. The experimental results show that the overall optimization performance of the ESWWO algorithm is superior to that of other algorithms, and thus, ESWWO is an effective and feasible method for solving the function optimization problem and AUV path planning problem.
OmniPhotos

Virtual reality headsets are becoming increasingly popular, yet it remains difficult for casual users to capture immersive 360° VR panoramas. State-of-the-art approaches require capture times of usually far more than a minute and are often limited in their supported range of head motion. We introduce OmniPhotos, a novel approach for quickly and casually capturing high-quality 360° panoramas with motion parallax. Our approach requires a single sweep with a consumer 360° video camera as input, which takes less than 3 seconds to capture with a rotating selfie stick or 10 seconds handheld. This is the fastest capture time for any VR photography approach supporting motion parallax by an order of magnitude. We improve the visual rendering quality of our OmniPhotos by alleviating vertical distortion using a novel deformable proxy geometry, which we fit to a sparse 3D reconstruction of captured scenes. In addition, the 360° input views significantly expand the available viewing area, and thus the range of motion, compared to previous approaches. We have captured more than 50 OmniPhotos and show video results for a large variety of scenes. We will make our code available.
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition

Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data, e.g., skeletal data in human action recognition, providing an exciting new way to fuse rich structural information for nodes residing in different parts of a graph. In human action recognition, current works introduce a dynamic graph generation mechanism to better capture the underlying semantic skeleton connections and thus improves the performance. In this paper, we provide an orthogonal way to explore the underlying connections. Instead of introducing an expensive dynamic graph generation paradigm, we build a more efficient GCN on a Riemann manifold, which we think is a more suitable space to model the graph data, to make the extracted representations fit the embedding matrix. Specifically, we present a novel spatial-temporal GCN (ST-GCN) architecture which is defined via the Poincaré geometry such that it is able to better model the latent anatomy of the structure data. To further explore the optimal projection dimension in the Riemann space, we mix different dimensions on the manifold and provide an efficient way to explore the dimension for each ST-GCN layer. With the final resulted architecture, we evaluate our method on two current largest scale 3D datasets, i.e., NTU RGB+D and NTU RGB+D 120. The comparison results show that the model could achieve a superior performance under any given evaluation metrics with only 40% model size when compared with the previous best GCN method, which proves the effectiveness of our model.
A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image

Fuzzy c-means (FCM) is a well-known unsupervised clustering algorithm based on fuzzy logic and used in many applications. However, it has some disadvantages. One disadvantage of FCM is that, while dealing with complex problems such as medical image data, it is frequently trapped into local minima during execution, which leads the undesired clustering results. Particle swarm optimization (PSO) is a population based metaheuristic optimization algorithm regarded as a global search approach and used in many optimization problems. To overcome the problem in FCM and in order to achieve better results, a hybrid FCM-PSO algorithm has been proposed by combining the excellent features of FCM and PSO algorithms. The experiment has been executed on a triangular dataset and publicly available real brain datasets and compared their results numerically and visually. The obtained experimental results demonstrate the efficacy of the proposed hybrid FCM-PSO algorithm. Friedman’s statistical test is also carried out to demonstrate the statistically significant performance of all discussed algorithms.
End-to-end multimodal image registration via reinforcement learning

Multimodal image registration is a vital initial step in several medical image applications for providing complementary information from different data modalities. Since images with different modalities do not exhibit the same characteristics, finding their accurate correspondences remains a challenge. For convolutional multimodal registration methods, two components are quite significant: descriptive image feature as well as the suited similarity metric. However, these two components are often custom-designed and are infeasible to the high diversity of tissue appearance across modalities. In this paper, we translate image registration into a decision-making problem, where registration is achieved via an artificial agent trained by asynchronous reinforcement learning. More specifically, convolutional long-short-term-memory is incorporated after stacked convolutional layers in this method to extract spatial-temporal image features and learn the similarity metric implicitly. A customized reward function driven by landmark error is advocated to guide the agent to the correct registration direction. A Monte Carlo rollout strategy is also leveraged to perform as a look-ahead inference in the testing stage, to increase registration accuracy further. Experiments on paired CT and MR images of patients diagnosed as nasopharyngeal carcinoma demonstrate that our method achieves state-of-the-art performance in medical image registration.
Back-Hand-Pose

The automatic recognition of how people use their hands and fingers in natural settings -- without instrumenting the fingers -- can be useful for many mobile computing applications. To achieve such an interface, we propose a vision-based 3D hand pose estimation framework using a wrist-worn camera. The main challenge is the oblique angle of the wrist-worn camera, which makes the fingers scarcely visible. To address this, a special network that observes deformations on the back of the hand is required. We introduce DorsalNet, a two-stream convolutional neural network to regress finger joint angles from spatio-temporal features of the dorsal hand region (the movement of bones, muscle, and tendons). This work is the first vision-based real-time 3D hand pose estimator using visual features from the dorsal hand region. Our system achieves a mean joint-angle error of 8.81 degree for user-specific models and 9.77 degree for a general model. Further evaluation shows that our system outperforms previous work with an average of 20% higher accuracy in recognizing dynamic gestures, and achieves a 75% accuracy of detecting 11 different grasp types. We also demonstrate 3 applications which employ our system as a control device, an input device, and a grasped object recognizer.