Reviewerly/exHarmony huggingface dataset:
A New Method of Low Amplitude Signal Detection and Its Application in Acoustic Emission

A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, integrity assessment of industrial facilities and/or microseismicity studies. In all these cases, the early and reliable detection of possible damage is of paramount importance and is practically limited by detectability of transient signals on the background of random noise. The proposed triggering algorithm is based on a logarithmic derivative of the power spectral density function. It was tested on the synthetic data, which mimics the actual ultrasonic acoustic emission signal recorded continuously with different signal-to-noise ratios (SNR). Considerable advantages of the proposed method over established fixed amplitude threshold and STA/LTA (Short Time Average / Long Time Average) techniques are demonstrated in comparative tests.
Q-Learning Based Particle Swarm Optimization Algorithm for Optimal Path Planning of Swarm of Mobile Robots

For a swarm of mobile robots in an unknown environment, the most challenging task is to plan the optimal path and also to learn the environmental parameters. Machine learning is one of the solutions to this problem. This paper proposes a combination of particle swarm optimization (PSO) and Q-value based reinforcement learning (Q-Learning) for a swarm of mobile robots to find the optimal path in an unknown environment and to learn the environment. Q-learning combined with PSO enable the robots to learn the unknown environment with reward and action selection policy while reducing the time to find the optimal path in the environment using the iterative improvement method of PSO. The proposed algorithm is simulated and found performing faster than Q-learning and PSO performing alone. We also compared with some other established algorithm where the proposed algorithm outperforms each of them in accuracy and speed.
Ethereum Analysis via Node Clustering

As an open source public blockchain with the capabilities of running smart contract, Ethereum provides decentralized Ethernet virtual machines to handle peer-to-peer contracts through its dedicated cryptocurrency Ether. And as the second largest blockchain, the amount of transaction data in Ethereum grows fast. Analysis of these data can help researchers better understand Ethereum and find attackers among the users. However, the analysis of Ethereum data at the present stage is mostly based on the statistical characteristics of Ethereum nodes and lacks analysis of the transaction behavior between them. In this paper, we apply machine learning in Ethereum analysis for the first time and cluster users and smart contract into groups by using transaction information in existing blocks. The clustering results are analyzed by using the identity information of the available Ethereum users and smart contracts. Based on the clustering results, we propose a new way of user identity discrimination and malicious user detection.
Would You Like A Chart With That? Incorporating Visualizations into Conversational Interfaces

Conversational interfaces, such as chatbots, are increasing in prevalence, and have been shown to be preferred by and help users to complete tasks more efficiently than standard web interfaces in some cases. However, little is understood about if and how information should be visualized during the course of an interactive conversation. This paper describes studies in which participants report their preferences for viewing visualizations in chat-style interfaces when answering questions about comparisons and trends. We find a significant split in preferences among participants; approximately 40% prefer not to see charts and graphs in the context of a conversational interface. For those who do prefer to see charts, most preferred to see additional supporting context beyond the direct answer to the question. These results have important ramifications for the design of conversational interfaces to data.
Uncertainty-Aware Anticipation of Activities

Anticipating future activities in video is a task with many practical applications. While earlier approaches are limited to just a few seconds in the future, the prediction time horizon has just recently been extended to several minutes in the future. However, as increasing the predicted time horizon, the future becomes more uncertain and models that generate a single prediction fail at capturing the different possible future activities. In this paper, we address the uncertainty modelling for predicting long-term future activities. Both an action model and a length model are trained to model the probability distribution of the future activities. At test time, we sample from the predicted distributions multiple samples that correspond to the different possible sequences of future activities. Our model is evaluated on two challenging datasets and shows a good performance in capturing the multi-modal future activities without compromising the accuracy when predicting a single sequence of future activities.
GimmeHop: A recommender system for mobile devices using ontology reasoners and fuzzy logic

This paper describes GimmeHop, a beer recommender system for Android mobile devices using fuzzy ontologies to represent the relevant knowledge and semantic reasoners to infer implicit knowledge. GimmeHop use fuzzy quantifiers to deal with incomplete data, fuzzy hedges to deal with the user context, and aggregation operators to manage user preferences. The results of our evaluation measure empirically the data traffic and the running time in the case of remote reasoning, the size of the ontologies that can be locally dealt with in a mobile device in the case of local reasoning, and the quality of the automatically computed linguistic values supported in the user queries.
HPC in the cloud: Performance comparison of function as a service (FaaS) vs infrastructure as a service (IaaS)

High performance computing (HPC) in the cloud has been shown to be a viable alternative to on‐premise clusters, especially for loosely coupled or embarrassingly parallel jobs. The traditional approach for a user would be to use the cloud provider's IaaS to provision virtual machines (VM) and use it in a similar manner to an on‐premise cluster. A new paradigm of serverless cloud computing, primarily offered as FaaS, allows a user to execute code in the cloud without any system administration overhead. A comparison is needed to better understand the cost vs performance trade‐off between FaaS and IaaS such that cloud users can decide which approach is suitable for them, however, such a study is lacking. In this paper, we compare Google cloud's FaaS (Cloud Functions) with its IaaS (Compute Engine) in terms of cost and performance for an embarrassingly parallel task. We find that FaaS can be 14% to 40% less expensive than IaaS for the same level of performance. However, performance of FaaS exhibits higher variation since the number of CPUs allocated (scalability) depends on the cloud provider.
Cybersecurity vulnerability mitigation framework through empirical paradigm: Enhanced prioritized gap analysis

Existing cybersecurity vulnerability assessment tools were designed based on the policies and standards defined by organizations such as the U.S. Department of Energy and the National Institute of Standards and Technology (NIST). Frameworks such as the cybersecurity capability maturity model (C2M2) and the NIST Cybersecurity Framework (CSF) are often used by the critical infrastructure owners and operators to determine the cybersecurity maturity of their facility. Although these frameworks are exceptional at performing qualitative cybersecurity analysis and identifying vulnerabilities, they do not provide a means to perform prioritized mitigation of those vulnerabilities in order to achieve a desired cybersecurity maturity. To address that challenge, we developed a framework and software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). This paper presents the detailed architecture of CyFEr's enhanced prioritized gap analysis (EPGA) methodology and its application to CSF. The efficacy of the presented framework is demonstrated by comparing against existing similar models and testing against the cyber injects from a real-world cyber-attack that targeted industrial control systems (ICS) in critical infrastructures.
Performance characteristics of an artificial intelligence based on convolutional neural network for screening conventional Papanicolaou-stained cervical smears

The conventional Papanicolaou-stained cervical smear is the most common screening test for cervical cancer. The sensitivity of the test in detecting abnormal cells is 67-75% in various studies. Owing to the volume of smears at cancer screening centres, significant man-hours are expended in the test. We have developed a software program for identification of foci of abnormal cells from conventional smears. We have chosen the convolutional neural network (CNN) model for its efficacy in image classification.A total of 1838 microphotographs from cervical smears, containing 1301 'normal' foci and 537 'abnormal' foci were included in the study. The data set was split into training, testing and validation sets. A CNN was developed in the Python programming language. The CNN was trained with the training and testing set. At the end of training, 94.64% accuracy was achieved in the testing set. The CNN was then run on the validation set (441 images).The CNN showed 94.28% sensitivity, 96.01% specificity, 91.66% positive predictive value and 97.30% negative predictive value. The CNN could recognise normal squamous cells, overlapping cells, neutrophils and debris and classify the focus appropriately. False positives were reported when the CNN failed to recognise overlapping cells (2.7% microphotographs). It could correctly label cell clusters with high nuclear cytoplasmic ratio and hyperchromasia. In 1.8% of microphotographs, a false negative was reported.The CNN showed 95.46% diagnostic accuracy, suggesting potential use in screening.
Experience of Developing a Mobile Application Using Flutter

The development of mobile applications always implies the development of two versions—for Android and for iOS. Flutter, new technology from Google, allows you to build applications for both systems from single code base. At the same time it saves possibility to create native part of applications. In this article we describe our experience of using this technology to create a mobile application for investment hotels.
POPLMark reloaded: Mechanizing proofs by logical relations

Abstract We propose a new collection of benchmark problems in mechanizing the metatheory of programming languages, in order to compare and push the state of the art of proof assistants. In particular, we focus on proofs using logical relations (LRs) and propose establishing strong normalization of a simply typed calculus with a proof by Kripke-style LRs as a benchmark. We give a modern view of this well-understood problem by formulating our LR on well-typed terms. Using this case study, we share some of the lessons learned tackling this problem in different dependently typed proof environments. In particular, we consider the mechanization in Beluga, a proof environment that supports higher-order abstract syntax encodings and contrast it to the development and strategies used in general-purpose proof assistants such as Coq and Agda. The goal of this paper is to engage the community in discussions on what support in proof environments is needed to truly bring mechanized metatheory to the masses and engage said community in the crafting of future benchmarks.
Intelligent Decision-Making for 3-Dimensional Dynamic Obstacle Avoidance of UAV Based on Deep Reinforcement Learning

With the growing utilization of UAV in reconnaissance, agriculture, logistics and entertainment, Autonomous collision avoidance during flight has become a necessary capability for modern UAV to detect the surrounding environment and guarantee their own safety. Autonomous obstacle avoidance is a typical agent decision-making problem. Unfortunately, existing traditional decision-making methods perform poorly in this specific realm, In particular, it is unable to meet the requirements of three-dimensional obstacle avoidance of UAV, so we introduce the deep reinforcement learning (DRL) technique into autonomous obstacle avoidance. We model the obstacle avoidance process as a Markov Decision Process and introduce a structure composed of double joint neural network estimators as the decision-maker, whose input is omnidirectional sonar readings and whose output is a value function estimating future rewards. Also, we propose an adaption in the procedure of memory replay to optimize the sampling, where we assign weights to the transitions and sample them accordingly. Our method is applied in a 3-dimensional physic environment, which contains both random dynamic obstacles and floating bouncing obstacles. The goal of the drone is to reach the terminal point without crash. Double Q Learning method with priority sampling, by comparison, achieves the most excellent performance in our simulation. Compared with the traditional algorithms, the proposed algorithm not only ensures the quality of decision making, enabling the agent to learn the optimal strategy, but also effectively improves the performance of the task and the efficiency of decision making. Simulation results demonstrate its effectiveness.
HGAN

In this article, we propose a novel method to address the two-dimensional (2D) image-based 3D object retrieval problem. First, we extract a set of virtual views to represent each 3D object. Then, a soft-attention model is utilized to find the weight of each view to select one characteristic view for each 3D object. Second, we propose a novel Holistic Generative Adversarial Network (HGAN) to solve the cross-domain feature representation problem and make the feature space of virtual characteristic view more inclined to the feature space of the real picture. This will effectively mitigate the distribution discrepancies across the 2D image domains and 3D object domains. Finally, we utilize the generative model of the HGAN to obtain the “virtual real image” of each 3D object and make the characteristic view of the 3D object and real picture possess the same feature space for retrieval. To demonstrate the performance of our approach, We established a new dataset that includes pairs of 2D images and 3D objects, where the 3D objects are based on the ModelNet40 dataset. The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.
A novel and fast three-dimensional measurement technology for the objects surface with non-uniform reflection

Optical three-dimensional (3D) measurement of non-diffuse objects is challenging. At present, a common used method is to adjust the camera's sensitivity several times to get fringe sequences with different brightness, and then extract the appropriate pixels for 3D shape reconstruction. However, the disadvantages of this method are obvious, including the large number of fringes needed, many useless redundant images, large amount of computation and time-consuming. In this paper, a novel method is proposed, which includes four major steps. Firstly, we set the intensity of the projected fringes to be maximum to ensure that the captured fringes have a good signal-to-noise ratio (SNR). Secondly, two sets of additional fringes are pre-captured to estimate the reasonable exposure time interval. Thirdly, appropriate exposure time is selected and the high-speed projection technology is used. Finally, appropriate pixels are extracted from the fringe sequences for 3D reconstruction, and then multiple phases are calculated and fused. Compared with the existing methods, the proposed method does not capture useless fringe patterns, and greatly improves the measurement efficiency while guaranteeing satisfactory 3D measurement results.
Automated Relational Meta-learning

In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks. In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner. As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.
Parallax-Based Spatial and Channel Attention for Stereo Image Super-Resolution

Stereo images can provide more extra information to enhance performance of super-resolution (SR) owing to two different visual angles. Due to the attention mechanism has been widely employed in various computer vision tasks and achieved excellent performance, the recent methods for stereo SR utilize attention mechanism to obtain stereo correspondence. However, their attention mechanisms are generally spatial since they treat channel-wise features equally, which result in ignoring a lot of valuable information contained in the channels. In this paper, we propose a parallax-based spatial and channel attention stereo SR network (PSCASSRnet) to combine the stereo correspondence with the SR task. Specifically, we present a parallax-based spatial and channel attention module (PSCAM) to take full advantage of spatial and channel-wise characteristics to obtain the stereo correspondence. The extensive experiments prove that our PSCAM can produce more appropriate attention map with all possible disparities between the stereo image pair to achieve better SR performance, and our PSCASSRnet outperforms other state-of-the-art methods in the aspects of visual quality and quantitive on the three mainstream public datasets named KITTI2012, KITTI2015 and Middlebury respectively.
Toward Smart Treatment Management for Personalized Healthcare

A remarkable increase of personalized healthcare services has been powered by the broad implementation of the networks and their ramifications. The core value of using personalized healthcare systems is to create customized medical service offerings and establish interconnections between patients and physicians. However, the treatment delivery for individual patients is encountering a challenge in effectively creating plans in which medical services can be retrieved from multiple sources at various costs. This article focuses on the problem of minimizing total cost of service retrieval and proposes an approach that uses intelligent agents to dynamically make service retrieval strategies. The approach is called the Smart Treatment for Personalized Healthcare (STPH) model, which is designed to produce an optimal solution to minimizing total cost on the intelligent agent. The performance of the proposed approach has been validated by experimental evaluations.
Automatic segmentation of 3D prostate MR images with iterative localization refinement

Accurate segmentation of the prostate gland from Magnetic Resonance (MR) images is still a challenging problem due to large variability and heterogeneity in the prostate appearance. To overcome this problem, we present a coarse-to-fine prostate segmentation approach with iterative localization refinement. Specifically, we first propose a resolution-aware 3D U-shaped network to balance the difference between the in-plane resolution and the through-plane distance. Then a case-wise loss function is introduced to alleviate the data imbalance problem and individual differences of the prostate MR images. In the inference stage, we extract a shrunk prostate region and improve the segmentation results in an iterative manner. Evaluation experiments are carried out on the MICCAI 2012 Prostate Segmentation Challenge Dataset (PROMISE12) and the NCI-ISBI 2013 Prostate Segmentation Challenge Dataset. Comparison results demonstrate that our method achieves significant improvements over the state-of-the-art approaches, and outperforms more than 290 submissions on the website of PROMISE12.
Problem Solvers Adjust Cognitive Offloading Based on Performance Goals

Abstract When incorporating the environment into mental processing ( cf ., cognitive offloading ), one creates novel cognitive strategies that have the potential to improve task performance. Improved performance can, for example, mean faster problem solving, more accurate solutions, or even higher grades at university. 1 Although cognitive offloading has frequently been associated with improved performance, it is yet unclear how flexible problem solvers are at matching their offloading habits with their current performance goals (can people improve goal‐related instead of generic performance, e.g., when being in a hurry and aiming for a “quick and dirty” solution?). Here, we asked participants to solve a cognitive task, provided them with different goals—maximizing speed (SPD) or accuracy (ACC), respectively—and measured how frequently (Experiment 1) and how proficiently (Experiment 2) they made use of a novel external resource to support their cognitive processing. Experiment 1 showed that offloading behavior varied with goals: Participants offloaded less in the SPD than in the ACC condition. Experiment 2 showed that this differential offloading behavior was associated with high goal‐related performance: fast answers in the SPD, accurate answers in the ACC condition. Simultaneously, goal‐unrelated performance was sacrificed: inaccurate answers in the SPD, slow answers in the ACC condition. The findings support the notion of humans as canny offloaders who are able to successfully incorporate their environment in pursuit of their current cognitive goals. Future efforts should be focused on the finding's generalizability, for example, to settings without feedback or with high mental workload.