Reviewerly/exHarmony huggingface dataset:
Fractional-Order Anisotropic Diffusion for Defogging of RGB Images

This paper proposes a novel and efficient algorithm for defogging of color (RGB) images. The fog in a scene is mostly due to the attenuation and airlight map, which decrease the quality of the image of the scene. To enhance such images from the visual point of view, a fractional-order anisotropic diffusion algorithm with [Formula: see text]-Laplace norm is proposed for removing the fog effect. In particular, a coupling term is added in order to model the inter-channel correlations. The weights used in the coupling term stop the transmission of diffusion with in the edges, thus balances the inter-channel data in the diffusion procedure. Experimental results validate the better performance of the proposed algorithm over some of the existing anisotropic diffusion-based methods. The proposed method is independent of the measure of fog in the images, thus images with different amount of fog can be enhanced.
SANUB: A new method for Sharing and Analyzing News Using Blockchain

Millions of news are being exchanged daily among people. With the appearance of the Internet, the way of broadcasting news has changed and become faster, however it caused many problems. For instance, the increase in the speed of broadcasting news leads to an increase in the speed of fake news creation. Fake news can have a huge impression on societies. Additionally, the existence of a central entity, such as news agencies, could lead to fraud in the news broadcasting process, e.g. generating fake news and publishing them for their benefits. Since Blockchain technology provides a reliable decentralized network, it can be used to publish news. In addition, Blockchain with the help of decentralized applications and smart contracts can provide a platform in which fake news can be detected through public participation. In this paper, we proposed a new method for sharing and analyzing news to detect fake news using Blockchain, called SANUB. SANUB provides features such as publishing news anonymously, news evaluation, reporter validation, fake news detection and proof of news ownership. The results of our analysis show that SANUB outperformed the existing methods.
Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration

Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a real-world setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that one's choice of which to use should depend on the sample size.
Summarization and visualization of multi-level and multi-dimensional itemsets

Frequent itemset (FI) mining aims at discovering relevant patterns from sets of transactions. In this work we focus on multi-level and multi-dimensional data, which provide a rich description of subjects through multiple features each at different levels of detail. Summarization of FIs has been only marginally addressed so far with specific reference to multi-level and multi-dimensional FIs. In this paper we fill this gap by proposing SUSHI, a framework for summarizing and visually exploring multi-level and multi-dimensional FIs. Specifically, SUSHI is based on (i) a similarity function for FIs which takes into account both their extensional (support-based) and intensional (feature-based) natures; (ii) theoretical results concerning antimonotonicity of support and similarity in multi-level settings, which allow us to propose an efficient clustering algorithm to generate hierarchical summaries; and (iii) two integrated approaches to summary visualization and exploration: a graph-based one, which highlights inter-cluster relationships, and a tree-based one, which emphasizes the relationships between the representative of each cluster and the other FIs in that cluster. SUSHI is evaluated using both a real and a synthetic dataset in terms of effectiveness, efficiency, and understandability of the summary, with reference to three different strategies for choosing cluster representatives. Overall, SUSHI shows to outperform previous approaches and to be a valuable tool to expedite the analysis of FIs. Besides, one of the three strategies for choosing cluster representatives shows to be the most effective one.
Lazy Steering RRT*: An Optimal Constrained Kinodynamic Neural Network Based Planner with no In-Exploration Steering

Kinodynamic-RRT* provides a sampling based asymptotically-optimal solution for motion planning of kinematically- and dynamically-constrained robots. For nonlinear systems, normally, the time- and energy-clamped steering function solutions needed within the RRT* use numerical iterative schemes such as shooting methods, which are computationally cumbersome. The number of calls to these solvers increases with the size of the tree. Hence, the time complexity of finding feasible steering functions prevents kinodynamic-RRT* for non-linear systems from being utilized in realtime or in situations where fast planning and re-planning are needed. Kinematic/dynamic constraints reduction to make the steering functions solvable in real time has been proposed in literature, however, these methods would affect the optimality of the solution. In this paper, we propose a lazy-steering kinodynmaic RRT* in which, machine learning techniques are used to (1) predict if a randomly-selected node is steerable to, and (2) if the steering is deemed feasible, what would be the estimated energy cost associated, when executing it. This provides a promising framework for implementing Kinodynamic-RRT* in which the use of numerical methods is delayed (hence the name lazy steering) until a potential collision free path has been found, and only then the numerical techniques are invoked. This results in a huge improvement in the run time with little trade off on optimality. Our proposed method was tested via simulation for motion planning of an under-actuated, non-holonomic, quadcopter with nonlinear dynamics in an environment cluttered with obstacles. The lazy-steering RRT* was faster than its counterpart (which was based on some recent works) by two orders of magnitude.
Knowledge Abstraction Matching for Medical Question Answering

Medical Question Answering (medical QA), which studies the problem of automatically answering patients' medical questions online, is one of the major applications of bioinformatics. Though many efforts have been made before, the medical QA system still deserves delicate algorithm optimization due to the serious application scenario and strict requirement for the answer quality. In this paper, we introduce a novel Knowledge Abstraction Matching (KAM) method for the medical QA problem. The intuition of KAM is that there are many frequent repeat text segments appearing in the answers across different questions. From this view, we propose a new method that consists of frequent segment <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> -gram mining, medical knowledge abstraction, medical segment matching and answer re-retrieval. KAM has been incorporated into Baidu's enterprise medical QA system MelodyQA deployed on the backend of Muzhi Doctor. The evaluation shows that the proposed method can generate more quality answers for MelodyQA with a significant improvement of question coverage under acceptable accuracy.
Temporal Saliency-Based Suspicious Behavior Pattern Detection

The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, the proposed method detected suspicious behavior with a temporal saliency map by combining the moving reactivity features of motion magnitude and gradient extracted by optical flow. It has been tested on various video clips that contain suspicious behavior. The experimental results show that the performance of the proposed method is good at detecting the six designated types of suspicious behavior examined: sudden running, colliding, falling, jumping, fighting, and slipping. The proposed method achieved an average accuracy of 93.89%, a precision of 96.21% and a recall of 94.90%.
Information Geometric Perspective on Off-Resonance Effects in Driven Two-Level Quantum Systems

We present an information geometric analysis of off-resonance effects on classes of exactly solvable generalized semi-classical Rabi systems. Specifically, we consider population transfer performed by four distinct off-resonant driving schemes specified by su 2 ; ℂ time-dependent Hamiltonian models. For each scheme, we study the consequences of a departure from the on-resonance condition in terms of both geodesic paths and geodesic speeds on the corresponding manifold of transition probability vectors. In particular, we analyze the robustness of each driving scheme against off-resonance effects. Moreover, we report on a possible tradeoff between speed and robustness in the driving schemes being investigated. Finally, we discuss the emergence of a different relative ranking in terms of performance among the various driving schemes when transitioning from on-resonant to off-resonant scenarios.
A Model of Preprocessing For Social Media Data Extraction

Tropical disease grows fast and requires detection. One source of data for detections is social media Twitter. However, social media data has data with diverse data structures primarily from unstructured user syntax and grammar. Therefore the twitter message (tweet) must be purified by a preprocessing method involving part of speech (POS) rule. This paper proposes a preprocessing model for twitter data to obtain a clean dataset. There are some steps. Firstly, we use Out-of-Vocabulary (OOV) word to analyse the tweets from Indonesian texts. Secondly, in stemming step, we use Sastrawi library. We also compare the result of tokenization and combining with Out-of-vocabulary (OOV) Word, Stemming N-Gram, and Stop Word Removal Sastrawi library into a well preprocessing approach. From the experimental result, we can get the result of Preprocessing task related to tweet data characteristic in Indonesia language. We can conclude that our form get more valuable result in terms of meaningful word occurrence comparing to the result obtained by just running common preprocessing tasks.
A Bayesian nonparametric testing procedure for paired samples

We propose a Bayesian hypothesis testing procedure for comparing the distributions of paired samples. The procedure is based on a flexible model for the joint distribution of both samples. The flexibility is given by a mixture of Dirichlet processes. Our proposal uses a spike-slab prior specification for the base measure of the Dirichlet process and a particular parametrization for the kernel of the mixture in order to facilitate comparisons and posterior inference. The joint model allows us to derive the marginal distributions and test whether they differ or not. The procedure exploits the correlation between samples, relaxes the parametric assumptions, and detects possible differences throughout the entire distributions. A Monte Carlo simulation study comparing the performance of this strategy to other traditional alternatives is provided. Finally, we apply the proposed approach to spirometry data collected in the United States to investigate changes in pulmonary function in children and adolescents in response to air polluting factors.
A Parallel Connected Component Labeling Architecture for Heterogeneous Systems-on-Chip

Connected component labeling is one of the most important processes for image analysis, image understanding, pattern recognition, and computer vision. It performs inherently sequential operations to scan a binary input image and to assign a unique label to all pixels of each object. This paper presents a novel hardware-oriented labeling approach able to process input pixels in parallel, thus speeding up the labeling task with respect to state-of-the-art competitors. For purposes of comparison with existing designs, several hardware implementations are characterized for different image sizes and realization platforms. The obtained results demonstrate that frame rates and resource efficiency significantly higher than existing counterparts are achieved. The proposed hardware architecture is purposely designed to comply with the fourth generation of the advanced extensible interface (AXI4) protocol and to store intermediate and final outputs within an off-chip memory. Therefore, it can be directly integrated as a custom accelerator in virtually any modern heterogeneous embedded system-on-chip (SoC). As an example, when integrated within the Xilinx Zynq-7000 X C7Z020 SoC, the novel design processes more than 1.9 pixels per clock cycle, thus furnishing more than 30 2k × 2k labeled frames per second by using 3688 Look-Up Tables (LUTs), 1415 Flip Flops (FFs), and 10 kb of on-chip memory.
PeckVis: A Visual Analytics Tool to Analyze Dominance Hierarchies in Small Groups

The formation of social groups is defined by the interactions among the group members. Studying this group formation process can be useful in understanding the status of members, decision-making behaviors, spread of knowledge and diseases, and much more. A defining characteristic of these groups is the pecking order or hierarchy the members form which help groups work towards their goals. One area of social science deals with understanding the formation and maintenance of these hierarchies, and in our work we provide social scientists with a visual analytics tool - PeckVis - to aid this process. While online social groups or social networks have been studied deeply and lead to a variety of analyses and visualization tools, the study of smaller groups in the field of social science lacks the support of suitable tools. Domain experts believe that visualizing their data can save them time as well as reveal findings they may have failed to observe. We worked alongside domain experts to build an interactive visual analytics system to investigate social hierarchies. Our system can discover patterns and relationships between the members of a group as well as compare different groups. The results are presented to the user in the form of an interactive visual analytics dashboard. We demonstrate that domain experts were able to effectively use our tool to analyze animal behavior data.
Extracting OLAP Cubes From Document-Oriented NoSQL Database Based on Parallel Similarity Algorithms

Today, the relational database is not suitable for data management due to the large variety and volume of data which are mostly untrusted. Therefore, NoSQL has attracted the attention of companies. Despite it being a proper choice for managing a variety of large volume data, there is a big challenge and difficulty in performing online analytical processing (OLAP) on NoSQL since it is schema-less. This article aims to introduce a model to overcome null value in converting document-oriented NoSQL databases into relational databases using parallel similarity techniques. The proposed model includes four phases, shingling, chunck, minhashing, and locality-sensitive hashing MapReduce (LSHMR). Each phase performs a proper process on input NoSQL databases. The main idea of LSHMR is based on the nature of both locality-sensitive hashing (LSH) and MapReduce (MR). In this article, the LSH similarity search technique is used on the MR framework to extract OLAP cubes. LSH is used to decrease the number of comparisons. Furthermore, MR enables efficient distributed and parallel computing. The proposed model is an efficient and suitable approach for extracting OLAP cubes from an NoSQL database.
Online feedback motion planning for spacecraft obstacle avoidance using positively invariant sets

This paper investigates a novel reliable feedback motion planning algorithm for steering a spacecraft to the desired position while satisfying dynamics and environmental constraints, such as actuator constraints, unexpected state constraints, bounded disturbances, etc. The proposed algorithm, termed safeRRT∗, applies positively invariant (PI) set transitions to an online sampling approach based on the asymptotically optimal rapidly-exploring random tree (RRT∗). A time-varying safe corridor is generated which consists of a sequence of PI sets under the instantaneous constraints. A spacecraft is guaranteed to remain within the corridor when the feedback controllers corresponding to the PI set transitions are executed. Simulations of the spacecraft avoiding obstacles validate the feasibility and effectiveness of the proposed safeRRT∗ algorithm.
Cutting a Path Through the IoT Ontology Jungle - A Meta-Survey

Interoperability of Internet-of-Things(IoT) applications, being characterized by massive heterogeneity of “things”, has become a key challenge in IoT research due to the omnipresence of smart devices. In order to achieve adequate interoperability levels, semantic technologies in terms of ontologies are a promising paradigm. Due to the plethora of IoT ontologies available, one gets, however, easily lost in the ontology jungle due to the lack of an overall and up-to-date view on the IoT ontology landscape. In this respect, the aim of this paper is to act as an umbrella across several up-to-date IoT ontology survey papers in terms of a meta-survey comprising a threefold contribution: Firstly, addressing the survey landscape, we provide insight into commonalities and differences regarding the scope of eight survey papers and their rationale behind. Secondly, addressing the IoT ontology landscape, we analyze the IoT ontologies discussed in the survey papers on basis of quantitative and qualitative aspects. Thirdly, addressing the state-of-research of ontologies for IoT, we identify commonalities and differences regarding conclusions and lessons learned of the survey papers.
Intelligent autonomous vehicles with an extendable knowledge base and meaningful human control

Intelligent robotic autonomous systems (unmanned aerial/ground/surface/underwater vehicles) are attractive for military application to relieve humans from tedious or dangerous tasks. These systems require awareness of the environment and their own performance to reach a mission goal. This awareness enables them to adapt their operations to handle unexpected changes in the environment and uncertainty in assessments. Components of the autonomous system cannot rely on perfect awareness or actuator execution, and mistakes of one component can affect the entire system. To obtain a robust system, a system-wide approach is needed and a realistic model of all aspects of the system and its environment. In this paper, we present our study on the design and development of a fully functional autonomous system, consisting of sensors, observation processing and behavior analysis, information database, knowledge base, communication, planning processes, and actuators. The system behaves as a teammate of a human operator and can perform tasks independently with minimal interaction. The system keeps the human informed about relevant developments that may require human assistance, and the human can always redirect the system with high-level instructions. The communication behavior is implemented as a Social AI Layer (SAIL). The autonomous system was tested in a simulation environment to support rapid prototyping and evaluation. The simulation is based on the Robotic Operating System (ROS) with fully modelled sensors and actuators and the 3D graphics-enabled physics simulation software Gazebo. In this simulation, various flying and driving autonomous systems can execute their tasks in a realistic 3D environment with scripted or user-controlled threats. The results show the performance of autonomous operation as well as interaction with humans.
Structural evolution of the orogenic gold deposits in central Victoria, Australia: The role of regional stress change and the tectonic regime

This review synthesizes the main characteristics of the orogenic gold deposits in western and central Victoria, Australia, and presents new insights into the relationships between orogen-scale deformation and the 12 hard-rock gold deposits that have each produced >30 t Au. We examine five of these large deposits, Stawell, Ballarat, Bendigo, Fosterville and Costerfield. From the start of the Delamerian Orogeny at ca. 515 Ma to the end of the Tabberabberan Orogeny at ca. 370 Ma, the eastern continental margin of Gondwanan Australia experienced both accretionary growth and the development of the Lachlan Orocline. Central western Victoria lies on the western edge of this oroclinal structure, and is partly underlain by a Proterozoic continental fragment, the Selwyn Block. All of the >30 t orogenic gold deposits in the region lie either on or to the west of the Selwyn Block. From the Delamerian Orogeny onwards, the deformation front progressively moved eastwards and the principal stress direction rotated clockwise from NE-SW through E-W and ultimately to N-S, so that the initial faulting and folding created pathways and traps for the subsequent mineralizing fluids through the brittle to brittle-ductile crust. Thus, the 455–440 Ma Benambran Orogeny was the major mineralizing event at Stawell and resulted in some early mineralization in the Bendigo Zone. The 420–410 Ma Bindian Orogeny gave rise to significant additional mineralization at Ballarat, Bendigo and Fosterville, while the 385–370 Ma Tabberabberan Orogeny drove mineralization at Costerfield and a second, bonanza event at Fosterville. The largest gold deposits formed where the fluid pathways either remained open or reopened in this rotating stress field, allowing multiple pulses of gold-rich fluids.
Solar and PV forecasting for large PV power plants using numerical weather models, satellite data and ground measurements

We develop a system for real time forecasting of solar irradiance and PV power for large power plants (> 500MW). Our forecast system is designed to combine numerical weather prediction models with both irradiance and power measurements as well as satellite based irradiance forecasts. Ir-radiance forecasts are processed with PV simulations to compute PV power forecasts for different sub-plants. Challenges that are typical for large power plants are taken into consideration. We evaluate irradiance forecasts for a site in China and irradiance and PV power forecasts for a PV power plant in Germany.