Reviewerly/exHarmony huggingface dataset:
Consensus in Lens of Consortium Blockchain: An Empirical Study

Blockchain emerges as a public decentralized ledger system in recent years. Compared to the traditional distributed database, the blockchain realizes trustless property over the distributed network but consumes more computing resources and processing time. In blockchain, the consensus algorithm is the key component that guarantees such a significant property. To reach better performance, people adjust the network assumptions and classifies the blockchain into three types of the public, consortium, and private. Since consortium blockchain is prevalent studied and more practical, in this paper we investigate various representative consensus in the lens of consortium blockchain. By comparative analyzing those consensus algorithms, we find that deterministic consensus can speed up the transaction process in consortium blockchain. The related experiments are also conducted to understand what mainly causes the consensus delay. The results show that communication complexity seriously influences the algorithm performance. From this empirical study, we suggest that the message transmission path can be an optimized method to make research in future work.
Towards distortion based underwater domed viewport camera calibration

Photogrammetry techniques used for 3D reconstructions and motion estimation from images are based on projective geometry that models the image formation process. However, in the underwater setting, refraction of light rays at the housing interface introduce non-linear effects in the image formation. These effects produce systematic errors if not accounted for, and severely degrade the quality of the acquired images. In this paper, we present a novel approach to the calibration of cameras inside spherical domes with large offsets between dome and camera centers. Such large offsets not only amplify the effect of refraction, but also introduce blur in the image that corrupts feature extractors used to establish image-world correspondences in existing refractive calibration methods. We propose using the point spread function (PSF) as a complete description of the optical system and introduce a procedure to recover the camera pose inside the dome based on the measurement of the distortions. Results on a collected dataset show the method is capable of recovering the camera pose with high accuracy.
Semi-supervised Pathology Segmentation with Disentangled Representations

Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semi-supervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatomy-Pathology Disentanglement Network (APD-Net), a pathology segmentation model that attempts to learn jointly for the first time: disentanglement of anatomy, modality, and pathology. The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations. In addition, a joint optimization strategy is proposed to fully take advantage of the available annotations. We evaluate our methods with two private cardiac infarction segmentation datasets with LGE-MRI scans. APD-Net can perform pathology segmentation with few annotations, maintain performance with different amounts of supervision, and outperform related deep learning methods.
Feature selection in classification using self-adaptive owl search optimization algorithm with elitism and mutation strategies

The fundamental aim of feature selection is to reduce the dimensionality of data by removing irrelevant and redundant features. As finding out the best subset of features from all possible subsets is computationally expensive, especially for high dimensional data sets, meta-heuristic algorithms are often used as a promising method for addressing the task. In this paper, a variant of recent meta-heuristic approach Owl Search Optimization algorithm (OSA) has been proposed for solving the feature selection problem within a wrapper-based framework. Several strategies are incorporated with an aim to strengthen BOSA (binary version of OSA) in searching the global best solution. The meta-parameter of BOSA is initialized dynamically and then adjusted using a self-adaptive mechanism during the search process. Besides, elitism and mutation operations are combined with BOSA to control the exploitation and exploration better. This improved BOSA is named in this paper as Modified Binary Owl Search Algorithm (MBOSA). Decision Tree (DT) classifier is used for wrapper based fitness function, and the final classification performance of the selected feature subset is evaluated by Support Vector Machine (SVM) classifier. Simulation experiments are conducted on twenty well-known benchmark datasets from UCI for the evaluation of the proposed algorithm, and the results are reported based on classification accuracy, the number of selected features, and execution time. In addition, BOSA along with three common meta-heuristic algorithms Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Binary Genetic Algorithm (BGA) are used for comparison. Simulation results show that the proposed approach outperforms similar methods by reducing the number of features significantly while maintaining a comparable level of classification accuracy.
Automated analysis of inter-parameter dependencies in web APIs

Web services often impose constraints that restrict the way in which two or more input parameters can be combined to form valid calls to the service, i.e. inter-parameter dependencies. Current web API specification languages like the OpenAPI Specification (OAS) provide no support for the formal description of such dependencies, making it hardly possible to interact with the services without human intervention. We propose specifying and automatically analyzing inter-parameter dependencies in web APIs. To this end, we propose a domain-specific language to describe these dependencies, a constraint programming-aided tool supporting their automated analysis, and an OAS extension integrating our approach and easing its adoption. Together, these contributions open a new range of possibilities in areas such as source code generation and testing.
Uncertainty Measured Markov Decision Process in Dynamic Environments

Successful robot path planning is challenging in the presence of visual occlusions and moving targets. Classical methods to solve this problem have used visioning and perception algorithms in addition to partially observable markov decision processes to aid in path planning for pursuitevasion and robot tracking.We present a predictive path planning process that measures and utilizes the uncertainty present during robot motion planning. We develop a variant of subjective logic in combination with the Markov decision process (MDP) and provide a measure for belief, disbelief, and uncertainty in relation to feasible trajectories being generated. We then model the MDP to identify the best path planning method from a list of possible choices. Our results show a high percentage accuracy based on the closest acquired proximity between a target and a tracking robot and a simplified pursuer trajectory in comparison with related work.
Lock-out Algorithm of Active-Intervention-Type Arc Suppression Device Based on ELM

The active-intervention-type arc suppression device can effectively prevent arc re-ignition and arc-overvoltage at the fault point in the distribution network. However, under some unfavorable working conditions, it is difficult for the device to extinguish the arc, so the corresponding equivalent circuit is established to derive the expression of the fault current. The expression shows that when low resistance ground fault occurs at the end of a long transmission line with a heavy load, the fault current may increase, so the device needs to be locked in these working conditions. The impact factors of fault current are used as the input characteristics to train the extreme learning machine (ELM) neural network, and then the performance of the neural network is verified by the test set. Finally, the simulation results show that the proposed device locking algorithm based on ELM has a high accuracy, which makes the device locked under unfavorable working conditions.
Generalized Critic Policy Optimization: A Model For Combining Advantage Estimates In Actor Critic Methods

We present a general model for actor critic methods that represent the possibility of combining value function estimations as a means to further reduce the policy gradient’s variance and improve the learning result. We show the potential of this architecture by implementing an example case to learn some of the Pybullet continous control robotic tasks with OpenAI Gym. We show by experimenting with a special case the effect of the external parameters on the overall performance of the policy optimization algorithm.
Microseismic event or noise: Automatic classification with convolutional neural networks

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Microseismic event or noise: Automatic classification with convolutional neural networksAuthors: Benjamin ConsolvoMichael ThorntonBenjamin ConsolvoMicroSeismic, Inc.Search for more papers by this author and Michael ThorntonMicroSeismic, Inc.Search for more papers by this authorhttps://doi.org/10.1190/segam2020-3414896.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractMicroseismic monitoring is a crucial element to understanding hydraulic fracturing operations prior to oil and gas production. One of the more tedious quality control (QC) measures that must often be performed following a microseismic processing workflow is a visual inspection of seismic data to determine whether the data contain microseismic events or only noise. The manual nature of these inspections can take many weeks, sometimes over a month, to perform for one geophysicist. Automated approaches usually use a short-term-average long-termaverage (STA/LTA) ratio, but end up picking false positives on noisy data. We propose using a supervised deep learning algorithm, a convolutional neural network (CNN), to automatically classify microseismic events from noise. Using our deep learning approach, we show that the time for QC can be reduced from weeks to hours with high accuracy.Presentation Date: Monday, October 12, 2020Session Start Time: 1:50 PMPresentation Time: 3:30 PMLocation: Poster Station 1Presentation Type: PosterKeywords: machine learning, microseismic, hydraulic fracturing, artificial intelligence, edge detectionPermalink: https://doi.org/10.1190/segam2020-3414896.1FiguresReferencesRelatedDetailsCited byConvolutional neural network‐based classification of microseismic events originating in a stimulated reservoir from distributed acoustic sensing data28 April 2022 | Geophysical Prospecting, Vol. 70, No. 5An introduction to distributed training of deep neural networks for segmentation tasks with large seismic data setsClaire Birnie, Haithem Jarraya, and Fredrik Hansteen1 October 2021 | GEOPHYSICS, Vol. 86, No. 6 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Benjamin Consolvo and Michael Thornton, (2020), "Microseismic event or noise: Automatic classification with convolutional neural networks," SEG Technical Program Expanded Abstracts : 1616-1620. https://doi.org/10.1190/segam2020-3414896.1 Plain-Language Summary Keywordsmachine learningmicroseismichydraulic fracturingartificial intelligenceedge detectionPDF DownloadLoading ...
Interactive Reinforcement Learning with Inaccurate Feedback

Interactive Reinforcement Learning (RL) enables agents to learn from two sources: rewards taken from observations of the environment, and feedback or advice from a secondary critic source, such as human teachers or sensor feedback. The addition of information from a critic during the learning process allows the agents to learn more quickly than non-interactive RL. There are many methods that allow policy feedback or advice to be combined with RL. However, critics can often give imperfect information. In this work, we introduce a framework for characterizing Interactive RL methods with imperfect teachers and propose an algorithm, Revision Estimation from Partially Incorrect Resources (REPaIR), which can estimate corrections to imperfect feedback over time. We run experiments both in simulations and demonstrate performance on a physical robot, and find that when baseline algorithms do not have prior information on the exact quality of a feedback source, using REPaIR matches or improves the expected performance of these algorithms.
Perceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning

In recent times, advanced driver assistance system (ADAS) and autonomous driving have received significant interest in the automotive community. In this regard, lane assistance system and vehicle detection are considered as core modules of ADAS. However, the drawback is that the conventional image processing and vision-based techniques are quite slow and computationally expensive. The proposed work circumvents this limitation with the practical use of deep learning (CNN) based detection architecture. This paper proposes the use of CNN inspired detection methods such as Faster RCNN and YOLO for visual perception of road traffic. Notably, they were applied for effective vehicle detection in non-disciplined, heterogeneous Indian road traffic. This involved collecting own dataset for Indian urban heterogeneous traffic in both day and night time. An application of YOLO with VGG network resulted in a mAP score of 78.57%. On the other hand, Faster RCNN with Inception v2 and ResNet networks resulted in mAP score of 88% and 89.44%, on Indian road traffic datasets. This is a significant result; that shows the use of Deep Learning techniques for an efficient visual modelling of unconstrained road traffic scenarios.
Budgeted Subset Selection for Fine-tuning Deep Learning Architectures in Resource-Constrained Applications

The growing success and popularity of deep learning in computer vision have resulted in the availability of several pretrained deep learning architectures (such as AlexNet, ResNet, VGGNet among others). A common practice in deep learning research is to use one of the pre-trained models and fine-tune it to a given target task, using training data from the target. However, training a deep learning model efficiently necessitates expensive, high-quality GPUs and distributed computing infrastructures. Some applications (such as those running on mobile platforms) are severely limited in terms of memory and computational resources; in these applications, it is a significant challenge to fine-tune a pre-trained deep learning model to a target task, using large amounts of target training data. Cloud services can be leveraged for training, but involve issues with data privacy and cost. In such applications, it is important to select an informative subset of the training data and fine-tune the deep model using only the selected subset. In this paper, we propose a novel framework to address this problem. We pose subset selection as a constrained NP-hard integer quadratic programming problem and derive an efficient linear relaxation to select a subset of exemplar instances. Our extensive empirical studies on three challenging vision datasets (from different application domains) using three commonly used pre-trained deep learning models corroborate the potential of our framework for real-world, resource-constrained applications.
Noisy Importance Sampling Actor-Critic: An Off-Policy Actor-Critic With Experience Replay

This paper presents Noisy Importance Sampling Actor-Critic (NISAC), a set of empirically validated modifications to the advantage actor-critic algorithm (A2C), allowing off-policy reinforcement learning and increased performance. NISAC uses additive action space noise, aggressive truncation of importance sample weights, and large batchsizes. We see that additive noise drastically changes how off-sample experience is weighted for policy updates. The modified algorithm achieves an increase in convergence speed and sample efficiency compared to both the on-policy actor-critic A2C and the importance weighted off-policy actor-critic algorithm. In comparison to state-of-the-art (SOTA) methods, such as actor-critic with experience replay (ACER), NISAC nears the performance on several of the tested environments while training 40% faster and being significantly easier to implement. The effectiveness of NISAC is demonstrated against existing on-policy and off-policy actor-critic algorithms on a subset of the Atari domain.
Improving the Accuracy of Pedestrian Detection in Partially Occluded or Obstructed Scenarios

More than 90% of traffic accidents are caused by humans. Therefore, the wide adoption of autonomous vehicles and advanced driver assistance systems (ADAS) is expected to save hundreds of thousands of lives in the next decades. One important capability of autonomous vehicles and ADAS is pedestrian detection. However, such detection becomes very challenging in scenarios where pedestrians are partially occluded, resulting in high rates of non-detection. In this paper, we improve the performance of a pedestrian classifier by proposing frameworks composed of Histogram of Oriented Gradients (HOG) feature extraction, combined with Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) learning models. In order to validate the performance of the improved approach, we consider the PSU and the INRIA pedestrian datasets. The state-of-the-art detection rates for the PSU and INRIA datasets are 55% and 54%, respectively. The proposed approach achieves detection rates of 86% and 82%, respectively, considerably outperforming the state-of-the-art results.
Automatic Detection of Backchannels in Russian Dialogue Speech

This paper deals with acoustic properties of backchannels – those turns within a dialogue which do not convey information but signify that the speaker is listening to his/her interlocutor (uh-huh, hm etc.). The research is based on a Russian corpus of dialogue speech, SibLing, a part of which (339 min of speech) was manually segmented into backchannels and non-backchannels. Then, a number of acoustic parameters was calculated: duration, intensity, fundamental frequency, and pause duration. Our data have shown that in Russian speech backchannels are shorter and have lower loudness and pitch than non-backchannels. After that, two classifiers were tested: CART and SVM. The highest efficiency was achieved using SVM (F\(_1\) = 0.651) and the following feature set: duration, maximum fundamental frequency, melodic slope. The most valuable feature was duration.
Testing the Within-State Distribution in Mixture Models for Responses and Response Times

Mixture models have been developed to enable detection of within-subject differences in responses and response times to psychometric test items. To enable mixture modeling of both responses and response times, a distributional assumption is needed for the within-state response time distribution. Since violations of the assumed response time distribution may bias the modeling results, choosing an appropriate within-state distribution is important. However, testing this distributional assumption is challenging as the latent within-state response time distribution is by definition different from the observed distribution. Therefore, existing tests on the observed distribution cannot be used. In this article, we propose statistical tests on the within-state response time distribution in a mixture modeling framework for responses and response times. We investigate the viability of the newly proposed tests in a simulation study, and we apply the test to a real data set.
Bidirectional Independently Recurrent Neural Network for Skeleton-Based Hand Gesture Recognition

Gestures are a common form of human communication and important for Human-Computer Interaction (HCI). In this paper, we propose a new approach for skeleton-based hand gesture recognition based on the Independently Recurrent Neural Network (IndRNN). First, a bidirectional IndRNN (Bi-IndRNN) is developed to extend the IndRNN with the capability of bidirectional processing. Then, a deep Bi-IndRNN network is constructed for gesture recognition, where, in addition to the joint coordinates, the temporal displacement of each joint is also used to enhance the input features. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on the widely used DHG dataset with an accuracy of 93.15% for the 14 gesture classes case and 91.13% for the 28 gesture classes case.
Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking

Due to the increase in the importance of giving real-time recommendation to e-commerce users, session-based recommender systems become more popular. Session-based recommendation systems have the ability to adapt quickly to respond to changes in user interests and newly added items. The ranking is the core part of recommender systems regardless of recommender system type. Re-ranking is applied to recommender systems to have more personalised recommendations by considering context-awareness. In this paper, we proposed an approach to re-rank recommended items by using a linear regression model. In our approach, we use users' current session features and temporal features of recommended items to measure a user's interest level on a recommended item. We focus on having better recall and precision scores with fewer recommendations to able to prove the success of our re-ranking strategy. We conduct computational experiments on six real-world datasets and show that after applying re-ranking, we can get higher recall and precision scores. These results confirm that taking user interest level on an item in a session into account can improve the chance of getting correct items in top 5 recommendations.
A Study of Machine Learning Approaches to Detect Cyberbullying

Social media networks like Facebook and Twitter create a great platform to share public views, opinions, feelings by text message, image, video. The public is very much interested to use these networks because of the comfortable Graphical User Interface (GUI) by a single click and taps to share content from their electric gadgets, gizmos, and mostly by their smartphones. On the other hand, some people performing cyberbullying activities like aggressive comments, abusing, trolling. Sometimes, these negative activities lead to cyberbullying victims to attempt suicide. In this paper, the authors are presenting essential approaches to recognize cyberbullying over social media using advanced machine learning and deep learning algorithms.