Reviewerly/exHarmony huggingface dataset:
A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

In order to effectively reduce the redundant information transmission in the network, a data fusion algorithm based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks is proposed. In this paper, the data fusion process of mobile heterogeneous wireless sensor networks is mainly studied, and regards the nodes of wireless sensor networks as neurons in the neural network of extreme learning machines. The neural network of the extreme learning machine extracts the sensory data collected by mobile heterogeneous wireless sensor network and combines the collected sensor data with the clustering route to greatly reduce the amount of network data sent to the sink node. Aiming at the problem that the extreme learning machine randomly generates the input layer weight and the hidden layer threshold before training, the output result is unstable, affecting the data fusion efficiency and the long delay, a new method of data fusion for mobile heterogeneous wireless sensor networks based on extreme learning machine optimized by bat algorithm is proposed. Simulation experiments are carried out from two aspects: mobile heterogeneous wireless sensor networks and heterogeneous mobile heterogeneous wireless sensor networks. The simulation results show that compared with the traditional SEP algorithm, BP neural network algorithm and ELM algorithm, the proposed BAT-ELM-based data fusion algorithm can effectively reduce network traffic, save network energy, improve network work efficiency, and significantly prolong network's lifetime.
Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues

Analysing and improving chatbot dialogues – so-called chatbot ‘training’ – is key to the successful implementation and maintenance of chatbots for customer service. Nevertheless, the details of this practice and what service providers may learn from the analysis of such dialogues is not investigated in current research on chatbots. As a first step towards bridging this gap in existing knowledge, we present a study of the qualitative analysis of chatbots dialogues in the context of the customer service department of a large telecom provider. In total 406 dialogues, randomly sampled from all chatbot dialogues during a four-week period, were included in the analysis. The analysis concerned the chatbot’s ability to resolve customers’ requests, the quality in the chatbot dialogues, and suggestions for improvements of the chatbot knowledge base generated through the analysis. The findings shed light on characteristics of successful and unsuccessful chatbot dialogues and the kind of improvements that may be derived from such analysis. On the basis of the findings we summarize implications for theory and practice, and suggest future research.
Mutual Explanations for Cooperative Decision Making in Medicine

Abstract Exploiting mutual explanations for interactive learning is presented as part of an interdisciplinary research project on transparent machine learning for medical decision support. Focus of the project is to combine deep learning black box approaches with interpretable machine learning for classification of different types of medical images to combine the predictive accuracy of deep learning and the transparency and comprehensibility of interpretable models. Specifically, we present an extension of the Inductive Logic Programming system Aleph to allow for interactive learning. Medical experts can ask for verbal explanations. They can correct classification decisions and in addition can also correct the explanations. Thereby, expert knowledge can be taken into account in form of constraints for model adaption.
Virtual reality: a use case for criminal justice practice

In recent years, virtual reality (VR) technology has convincingly demonstrated its potential for assessment, training, rehabilitation and treatment purposes in a variety of domains, including (mental) healthcare and education.This paper explores the possibilities for VR application within criminal justice practice.Criminological literature and literature of related disciplines on VR applications were reviewed with a special focus on risk assessment, correctional rehabilitation, and reintegration.We also devoted attention to the VR-based treatment of anxiety-related disorders among victims of crime.Literature shows that VR can provide safe learning environments that would otherwise involve risk, generate ethical and ecologically valid virtual alternatives for which real-life situations would be unethical to use, and develop environments that are impossible to create in the real life.These unique characteristics make VR a promising tool to expand the current toolbox of risk assessment instruments, and correctional rehabilitation and reintegration programs in important ways.Before implementing VR in criminal justice practice, an elementary understanding of both the advantages and disadvantages of VR technology is desirable but given a longstanding and effective VR tradition in other domains, the future of VR in criminal justice practice is bright.
A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling

Based on a software architecture for autonomous driving presented and tested in an autonomous level-5 race-car in 2018 this paper describes in detail the evolutionary enhancement of this software architecture. The architecture combines the autonomous software layers perception, planning and control, which were modularized in the core software. The focus of this paper is the detailed description of how we enhanced the software with a module for an object list creation, a module for the behavioral planning and a module for the creation of dynamic trajectories. These enhancements allow the car to overtake other cars and static obstacles autonomously when driving on the race track. Furthermore, we present with a high novelty value the software module for a vehicle performance maximization, which consists of a control performance assessment and a friction estimation. The software architecture displayed in this paper will be tested and evaluated in the Roborace Season Alpha in 2019.
Predicting radioactive waste glass dissolution with machine learning

The vitrification of high-level nuclear waste within borosilicate glass and its disposition within a multi-barrier repository deep underground is accepted as the best form of disposal. Here, the ability of machine learning to predict both static and dynamic glass leaching behavior is analysed using large-scale unstructured multi-source data, covering a diverse range of experimental conditions and glass compositions. Machine learning can accurately predict leaching behavior, predict missing data, and time forecast. Accuracy depends upon the type of learning algorithm, model input variables, and diversity or size of the underlying dataset. For static leaching, the bagged random forest method predicts well, even when either pH or glass composition are neglected as input variables, additionally showing potential in predicting independent glass dissolution data. For dynamic leaching, accuracy improves if replacing final pH with a species dissolution rate as an input variable, although results show no preferred output species (Si, Na, or Al).
Adaptive Prior Selection for Repertoire-Based Online Adaptation in Robotics

Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a planning or learning algorithm chooses the most appropriate policies according to the current situation (e.g., a damaged robot, a new object, etc.). In this paper, we relax the assumption of previous works that a single repertoire is enough for adaptation. Instead, we generate repertoires for many different situations (e.g., with a missing leg, on different floors, etc.) and let our algorithm selects the most useful prior. Our main contribution is an algorithm, APROL (Adaptive Prior selection for Repertoire-based Online Learning) to plan the next action by incorporating these priors when the robot has no information about the current situation. We evaluate APROL on two simulated tasks: (1) pushing unknown objects of various shapes and sizes with a robotic arm and (2) a goal reaching task with a damaged hexapod robot. We compare with "Reset-free Trial and Error" (RTE) and various single repertoire-based baselines. The results show that APROL solves both the tasks in less interaction time than the baselines. Additionally, we demonstrate APROL on a real, damaged hexapod that quickly learns to pick compensatory policies to reach a goal by avoiding obstacles in the path.
Will deepfakes do deep damage?

The ability to produce fake videos that appear amazingly real is here. Researchers are now developing ways to detect and prevent them.
Multiple scale self-adaptive cooperation mutation strategy-based particle swarm optimization

Particle Swarm Optimization (PSO) algorithm has lately received great attention due to its powerful search capacity and simplicity in implementation. However, previous studies have demonstrated that PSO still suffers from two key drawbacks of premature convergence and slow convergence, especially when dealing with multi-modal optimization problems. In order to address these two issues, we propose a multiple scale self-adaptive cooperative mutation strategy-based particle swarm optimization algorithm (MSCPSO) in this paper. In the proposed approach, we adopt multi-scale Gaussian mutations with different standard deviations to promote the capacity of sufficiently searching the whole solution space. In the adopted multi-scale mutation strategy, large-scale mutation can make populations explore the global solution space and rapidly locate the better solution area at the early stage, thus avoiding the premature convergence and simultaneously speeding up the convergence, while small-scale mutation can allow the populations to more accurately exploit the local best solution area during the later stage, thus improving the accuracy of final solution. In order to guarantee the convergence speed while avoiding premature convergence, the standard deviations for multi-scale Gaussian mutations would be reduced with the increase of iterations, which can make populations pay more attention to local accurate solution exploitation during the later evolution stage and consequently speed up the convergence. In addition, the threshold for each dimension to execute mutation is also dynamically adjusted according to its previous mutation frequency, which can allow MSCPSO to better balance the global and local search capacities, thus avoiding premature convergence without reducing convergence speed. The extensive experimental results on various benchmark optimization problems demonstrate that the proposed approach is superior to other existing PSO techniques with good robustness.
Motor Synergy Development in High-Performing Deep Reinforcement Learning Algorithms

As human motor learning is hypothesized to use the motor synergy concept, we investigate if this concept could also be observed in deep reinforcement learning for robotics. From this point of view, we carried out a joint-space synergy analysis on multi-joint running agents in simulated environments trained using two state-of-the-art deep reinforcement learning algorithms. Although a synergy constraint has never been encoded into the reward function, the synergy emergence phenomenon could be observed statistically in the learning agent. To our knowledge, this is the first attempt to quantify the synergy development in detail and evaluate its emergence process during deep learning motor control tasks. We then demonstrate that there is a correlation between our synergy-related metrics and the performance and energy efficiency of a trained agent. Interestingly, the proposed synergy-related metrics reflected a better learning capability of SAC over TD3. It suggests that these metrics could be additional new indices to evaluate deep reinforcement learning algorithms for motor learning. It also indicates that synergy is required for multi-joints robots to move energy-efficiently.
A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi)

Traditionally hazard quotients (HQs) have been computed for ecological risk assessment, often without quantifying the underlying uncertainties in the risk estimate. We demonstrate a Bayesian network approach to quantitatively assess uncertainties in HQs using a retrospective case study of dietary mercury (Hg) risks to Florida panthers (Puma concolor coryi). The Bayesian network was parameterized, using exposure data from a previous Monte Carlo-based assessment of Hg risks (Barron et al., 2004. ECOTOX 13:223), as a representative example of the uncertainty and complexity in HQ calculations. Mercury HQs and risks to Florida panthers determined from a Bayesian network analysis were nearly identical to those determined using the prior Monte Carlo probabilistic assessment and demonstrated the ability of the Bayesian network to replicate conventional HQ-based approaches. Sensitivity analysis of the Bayesian network showed greatest influence on risk estimates from daily ingested dose by panthers and mercury levels in prey, and less influence from toxicity reference values. Diagnostic inference was used in a high-risk scenario to demonstrate the capabilities of Bayesian networks for examining probable causes for observed effects. Application of Bayesian networks in the computation of HQs provides a transparent and quantitative analysis of uncertainty in risks.
Veridical Data Science

Veridical data science extracts reliable and reproducible information from data, with an enriched technical language to communicate and evaluate empirical evidence in the context of human decisions and domain knowledge. Building and expanding on principles of statistics, machine learning, and the sciences, we propose the predictability, computability, and stability (PCS) framework forveridical data science. Our framework is comprised of both a workflow and documentation and aims to provide responsible, reliable, reproducible, and transparent results across the entire data science life cycle. Moreover, we propose the PDR desiderata for interpretable machine learning as part of veridical data science (with PDR standing for predictive accuracy, predictive accuracy and relevancy to a human audience and a particular domain problem).
A Simple Protocol for Certifying Graph States and Applications in Quantum Networks

We present a simple protocol for certifying graph states in quantum networks using stabiliser measurements. The certification statements can easily be applied to different protocols using graph states. We see, for example, how it can be used for measurement based verified quantum computation, certified sampling of random unitaries, quantum metrology and sharing quantum secrets over untrusted channels.
A NOVEL METHOD FOR HDR VIDEO ENCODING, COMPRESSION AND QUALITY EVALUATION

The high dynamic range imaging/video has become more prominent in the consumer electronic realm as the it allows the human to view a picture e or a video at a minimum eye adaption. The high dynamic ranging is capable enough to capture a full range light from the scene unlike the traditional methods that captures only 8-f-stops. Though the high dynamic range video are produced using more standard methodologies, it still needs continuous developments in the distribution phase. so the paper discourses the shortcomings in the present method of high dynamic range video coding and puts forth the novel method of two layered backward compatible video encoding methods accompanied by the Chroma subsampling and the luminance masking to improve the color accuracy and remove the nonvisible portion respectively of the video content. The performance measurement of the novel method HDR video encoding compression (N-HVEC) in term of mean opinion source, bit rate savings and he HDR-VQM metric is compared with the HDR10 and HDR-HLG
DIFFERENCE-MAKING CONDITIONALS AND THE RELEVANT RAMSEY TEST

Abstract This article explores conditionals expressing that the antecedent makes a difference for the consequent. A ‘relevantised’ version of the Ramsey Test for conditionals is employed in the context of the classical theory of belief revision. The idea of this test is that the antecedent is relevant to the consequent in the following sense: a conditional is accepted just in case (i) the consequent is accepted if the belief state is revised by the antecedent and (ii) the consequent fails to be accepted if the belief state is revised by the antecedent’s negation. The connective thus defined violates almost all of the traditional principles of conditional logic, but it obeys an interesting logic of its own. The article also gives the logic of an alternative version, the ‘Dependent Ramsey Test,’ according to which a conditional is accepted just in case (i) the consequent is accepted if the belief state is revised by the antecedent and (ii) the consequent is rejected (e.g., its negation is accepted) if the belief state is revised by the antecedent’s negation. This conditional is closely related to David Lewis’s counterfactual analysis of causation.
SSL-SVD

Recommendation systems have been widely used in large e-commerce websites, but cold start and data sparsity seriously affect the accuracy of recommendation. To solve these problems, we propose SSL-SVD, which works to mine the sparse trust between users and improve the performance of the recommendation system. Specifically, we mine sparse trust relationships by decomposing trust impact into fine-grained factors and employing the Transductive Support Vector Machine algorithm to combine these factors. Then, we incorporate both social trust and sparse trust information into the SVD++ model, which can effectively utilize the explicit and implicit influence of trust for rating prediction in the recommendation system. Experiments show that our SSL-SVD increases the trust density degree of each dataset by more than 65% and improves the recommendation accuracy by up to 4.3%.
A Framework for Creating Deployable Smart Contracts for Non-fungible Tokens on the Ethereum Blockchain

Non-fungible tokens are an up and coming application domain for smart contracts. Ethereum is the first blockchain-based decentralized computing platform that has standardized this type of tokens into a well-defined interface, namely ERC721. We propose a framework that provides developers with a smart contract suite that offers complete implementations of the ERC721 standard and common extensions and features frequently encountered in ERC721-based applications. We introduce a specification language that enables customization and configuration of the smart contract suite by including and excluding the supported features and extensions. We evaluate the smart contract suite for its extensibility and reusability and compare the metrics with four reference implementations tackling a similar problem. In addition to this, we evaluate and analyze the effort and efficiency of the specification language in comparison to manual configuration of the smart contract suite. Our contribution lies in examining quality metrics for code extensibility and reusability and determining the more insightful metrics for assessing these quality attributes in the context of Solidity smart contracts. Additionally, from the lines of code metric, We conclude that our specification language offers a simple and efficient alternative to manual smart contract suite customization.
Labeling, Cutting, Grouping: An Efficient Text Line Segmentation Method for Medieval Manuscripts

This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.
Action Recognition Based on 3D Skeleton and RGB Frame Fusion

Action recognition has wide applications in assisted living, health monitoring, surveillance, and human-computer interaction. In traditional action recognition methods, RGB video-based ones are effective but computationally inefficient, while skeleton-based ones are computationally efficient but do not make use of low-level detail information. This work considers action recognition based on a multimodal fusion between the 3D skeleton and the RGB image. We design a neural network that uses a 3D skeleton sequence and a single middle frame from an RGB video as input. Specifically, our method picks up one frame in a video and extracts spatial features from it using two attention modules, a self-attention module and a skeleton-attention module. Further, temporal features are extracted from the skeleton sequence via a BI-LSTM sub-network. Finally, the spatial features and the temporal features are combined via a feature fusion network for action classification. A distinct feature of our method is that it uses only a single RGB frame rather than an RGB video. Accordingly, it has a light-weighted architecture and is more efficient than RGB video-based methods. Comparative evaluation on two public datasets, NTU-RGBD and SYSU, demonstrates that, our method can achieve competitive performance compared with state-of-the-art methods.
Flying through a narrow gap using neural network: an end-to-end planning and control approach

In this paper, we investigate the problem of enabling a drone to fly through a tilted narrow gap, without a traditional planning and control pipeline. To this end, we propose an end-to-end policy network, which imitates from the traditional pipeline and is fine-tuned using reinforcement learning. Unlike previous works which plan dynamical feasible trajectories using motion primitives and track the generated trajectory by a geometric controller, our proposed method is an end-to-end approach which takes the flight scenario as input and directly outputs thrust-attitude control commands for the quadrotor. Key contributions of our paper are: 1) presenting an imitate-reinforce training framework. 2) flying through a narrow gap using an end-to-end policy network, showing that learning based method can also address the highly dynamic control problem as the traditional pipeline does (see attached video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ). 3) propose a robust imitation of an optimal trajectory generator using multilayer perceptrons. 4) show how reinforcement learning can improve the performance of imitation learning, and the potential to achieve higher performance over the model-based method.