Reviewerly/exHarmony huggingface dataset:
Dynamic Random Walk for Superpixel Segmentation

In this paper, we present a novel Random Walk model called Dynamic Random Walk (DRW) for superpixel segmentation. The proposed DRW adds a new type of node called dynamic node to enrich the features of labels and reduce redundant calculation. By greedily optimizing the Weighted Random Walk Entropy (WRWE), our DRW can consider the features of both seed nodes and dynamic nodes, which enhances the boundary adherence. In addition, a new seed initialization strategy, which can evenly distribute seed nodes in both 2D and 3D space, is proposed to extend our DRW for superpixel segmentation. With this strategy, our DRW can generate superpixels in only one iteration without updating seed nodes. The experiment results show that our DRW is faster than existing RW models, and better than the state-of-the-art superpixel segmentation algorithms in both efficiency and the performance.
An improved moth-flame algorithm based on differential evolution and shuffled frog leaping algorithm

Generally speaking, in swarm intelligence algorithm, keeping the diversity of the population and strengthening its ability to escape the local optima will help to enhance the performance of an optimization algorithm. To strengthen these abilities, an improved moth-flame optimization (MFO) algorithm combining differential evolution (DE) and shuffled frog leaping algorithm (SFLA), namely DEFMFO, is proposed. In the proposed algorithm, the DE is used to enrich the population diversity, and the SFLA is proposed to get the optimal solution which strengthens the ability to escape the local optima. The simulation results show that, compared with particle swarm optimization (PSO), bat algorithm (BA) and classic MFO by testing on eight benchmark functions, the DEFMFO algorithm has better performance in global search and convergence speed.
Artificial Neural Networks Training Acceleration Through Network Science Strategies

Deep Learning opened artificial intelligence to an unprecedented number of new applications. A critical success factor is the ability to train deeper neural networks, striving for stable and accurate models. This translates into Artificial Neural Networks (ANN) that become unmanageable as the number of features increases. The novelty of our approach is to employ Network Science strategies to tackle the complexity of the actual ANNs at each epoch of the training process. The work presented herein originates in our earlier publications, where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of our approach has also been recently confirmed by independent researchers, who managed to train a million-node ANN on non-specialized laptops. Encouraged by these results, we have now moved into having a closer look at some tunable parameters of our previous approach to pursue a further acceleration effect. We now investigate on the revise fraction parameter, to verify the necessity of the role of its double-check. Our method is independent of specific machine learning algorithms or datasets, since we operate merely on the topology of the ANNs. We demonstrate that the revise phase can be avoided in order to half the overall execution time with an almost negligible loss of quality.
Interval Type-2 Intuitionistic Fuzzy Logic System for Time Series and Identification Problems - A Comparative Study

This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems.Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems.The proposed model is based on a Takagi-Sugeno-Kang inference system.The evaluations of the model are conducted using both real world and artificially generated datasets.Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature.Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.
A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

Ship target classification from satellite images is a challenging task with its requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other type of images, and analyzing of images. The dissimilarity of results, enhanced dataset requirement, intricacy of the problem domain, general use of Synthetic Aperture Radar (SAR) images and problems on generalizability are some topics of the issues related to ship target detection. In this study, we propose a deep convolutional neural network model for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracy value by just using a pre-processed satellite image input. Visual and graphical results of features at various layers and deconvolutions are also demonstrated for a better understanding of the basic process.
ELM Neural Network-based Fault Diagnosis Method for Mechanical Equipment

To improve the safety of industrial machinery and equipment, a fault diagnosis method of mechanical equipment based on ELM neural network is proposed, which is a learning machine with many merits such as fast learning and strongly generalizing ability, and it can be well used in fault classification and diagnosis. This paper presents the weights training method of ELM neural network and the steps for fault diagnosis. Besides, the classification effects of ELM neural network and traditional neural network based on the gradient descending algorithm are compared. The simulation experiment shows that the ELM neural network has the advantage of fast learning speed and high studying accuracy. It can diagnose the fault category quickly and accurately.
Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm

The paper analyzes data sets containing images with labeled traffic signs, as well as modern approaches for their detection and classification on images of urban scenes. Particular attention is paid to the recognition of Russian types of traffic signs. Various modern architectures of deep neural networks for the simultaneous object detection and classification were studied, including Faster R-CNN, Mask R-CNN, Cascade R-CNN, RetinaNet. To increase the efficiency of neural network recognition of objects in a video sequence, the Seq-BBox Matching algorithm is used. Training and testing of the proposed approach was carried out on Russian Traffic Sign Dataset and IceVision Dataset containing over 150 types of road signs and more than 65,000 marked images. For all the approaches considered, quality metrics are defined: mean average precision mAP, mean average recall mAR and processing time of one frame. The highest quality performance was demonstrated by the architecture of Faster R-CNN with Seq-BBox Matching, while the highest performance is provided by the architecture of RetinaNet. Implementation was carried out using the Python 3.7 programming language and PyTorch deep learning library using NVidia CUDA technology. Performance indicators were obtained on the workstation with the NVidia Tesla V-100 32GB video card. The obtained results demonstrate the possibility of applying the proposed approach both for the resource-intensive procedure for automated labeling of road scene images for new data sets preparation, and for traffic sign recognition in on-board computer vision systems of unmanned vehicles.
Analysis and Prediction of Data Transfer Throughput for Data-Intensive Workloads

Scientific workflows are increasingly transferring large amounts of data between high performance computing (HPC) systems. Even though these HPC systems are connected via high-speed dedicated networks and use dedicated data transfer nodes (DTNs), it is still difficult to predict the data transfer throughput because of variations in data transfer protocols, host configurations, performance of file systems, and overlapping workloads. In order to provide reliable performance prediction for better resource management and job scheduling, we need models for predicting data transfer throughput under real-world conditions. In this paper, we explore different machine learning approaches for building data-driven models to improve performance and prediction of large-scale data transfer throughput. In addition to the variables already collected by the network monitoring system, we also develop heuristics to derive additional metrics for improving the prediction accuracy. We use the prediction results to identify the importance of different network parameters in predicting the throughput for large-scale data transfers. Through extensive tests, we identify key network parameters, discover interesting variations among different HPC sites, and show that we can predict throughput with high accuracy. We also analyze our models and results to provide recommendations for improving the performance of big data transfers.
Methodology of Mean Shift Clustering Algorithm Implementation Based on Dataflow Computer

The article discusses the methodology development for implementing on a dataflow computer the Mean shift clustering algorithm, namely its subtype - Mean shift with flat core, also called FOREL (Formal Element). We have formalized the Mean shift algorithm for the dataflow implementation. We have also developed architecture of dataflow computer, identified the types of execution units, formulated algorithms of their operation and the information exchanging. The proposed computing methodology allows to reduce information traffic in the dataflow computing system for solving the clustering problem by combining a set of points located in the features space into a computational grid and reduce the time of clusters finding by parallelizing calculations. The methodology provides finding several clusters in the linear metric space, the number of which is unknown in advance due to the convergence of the Mean Shift algorithm.
Extended Factorization Machines for Sequential Recommendation

Users' historical activities are usually contained in real-life sequential recommendation systems to predict their future behaviors. In this situation, traditional Factorization Machines (FMs) approaches may be not suitable. Recently, a new surge of interest aims to use recurrent neural networks(RNN) to encode users' dynamic features with temporal characteristics. However, most of these works fail to reproduce computational simplicity of FMs. In this paper, we propose an architecture of extended-FM for sequential recommendation, which presents temporal feature interactions in an explicit way as traditional FM's formula. Our approach also allows us to accomplish computation of the model in linear time. Furthermore, we merge extended-FM into higher-order interaction framework without significant changes to the deeper models themselves. We conduct comprehensive experiments on two real-world datasets. The results demonstrate that extended-FM outperforms traditional FMs as well as deep learning feature combination models on sequential recommendation tasks.
Differential Evolution Through Variable Neighborhood Search for Constrained Real-Parameter Optimization Problems

This paper presents a differential evolution algorithm with the variable neighborhood search (VNS), called DEVNS. In the VNS loop, two distinct mutation strategies are used to solve real-parameter constrained optimization problems. As well-known, the performances of DE algorithms depend on the chosen mutation strategies and their control parameters. For these reasons, the proposed DEVNS generates each trial individual through the use of VNS local search having two distinct mutation strategies as well as random crossover and mutation rates. The idea behind the DEVNS is to generate multiple candidate solutions among which the better one is chosen as the trial individual. Furthermore, the DEVNS is well equipped with constraint handling methods to end up with feasible solutions. The algorithm was tested using benchmark instances in Congress on Evolutionary Computation 2017 [1]. To the best of our knowledge, the DEVNS algorithm was able to generate new best-known solutions up to 8 to 10 benchmark functions for the first time in this paper in the literature.
A Sharing Platform of Emergency Cars Based on Blockchain Environment

In this study a sharing vehicle platform was proposed by using smart contract on Blockchain environment. Through the combination of the smart contract of Blockchain and the Internet of Vehicles, the transfer and record of vehicle driving privileges could be realized. And the privileges could be effectively removed after the vehicle completed the task of transportation to ensure the privileges were not misused or abused. To effectively implement the authorization and operation of the shared Emergency Cars (ECs), a Priority Passing Smart Contract (PPSC) flow framework was proposed also. It is an authorization authentication method based on the smart contract. The PPSC flow consists of two main parts: vehicle rescue and authorization-planning-broadcasting. These processes run on Blockchain environment to support the whole platform for the shared ECs.
AN ON-DEVICE FEDERATED LEARNING APPROACH FOR COOPERATIVE ANOMALY DETECTION

Most edge AI focuses on prediction tasks on resource-limited edge devices, while the training is done at server machines, so retraining a model on the edge devices to reflect environmental changes is a complicated task. To follow such a concept drift, a neural-network based on-device learning approach is recently proposed, so that edge devices train incoming data at runtime to update their model. In this case, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device. To address this issue, one approach is a cooperative learning or federated learning, where edge devices exchange their trained results and update their model by using those collected from the other devices. In this paper, as an on-device learning algorithm, we focus on OS-ELM (Online Sequential Extreme Learning Machine) and combine it with autoencoder for anomaly detection. We extend it for an on-device federated learning so that edge devices can exchange their trained results and update their model by using those collected from the other edge devices. Evaluation results using a driving dataset of cars and a human activity dataset demonstrate that the proposed on-device federated learning can produce a merged model by combining trained results from multiple edge devices as accurately as a conventional backpropagation based neural network. Latency for the merging is reasonable, and it can merge the models faster than continuously executing the sequential learning.
A143 IMPACT OF A SIMULATION-BASED AUGMENTED REALITY CURRICULUM ON POLYPECTOMY SKILLS AMONG NOVICE ENDOSCOPISTS: A RANDOMIZED CONTROLLED TRIAL

Abstract Background Polypectomy is an essential endoscopic skill. Training in polypectomy has been identified as a major deficiency for endoscopists worldwide as polypectomy occurs ad hoc during a colonoscopy when a polyp is detected, and a lack of standardized curricula. Augmented reality (AR), which superimposes computer-generated images on a user’s view of the world, can address these gaps by standardizing encounters with polyps while completing simulated procedures and enabling polypectomy-specific teaching. Aims Evaluate the impact of a simulation-based augmented reality curriculum on polypectomy performance among novice endoscopists. Methods This study includes two cohorts of participants from 2019 to 2020. In 2019, participants were randomized into either: (1) a control curriculum, involving 6 hours of simulation-based training (SBT) supplemented by expert feedback, interlaced with 4 hours of small group teaching on the theory of colonoscopy; or (2) the augmented reality curriculum (ARC), in involving the same curriculum with integrated AR, wherein participants engaged with an AR-enhanced video demonstrating relevant therapeutic and pathologic details during polypectomy. The SBT for all participants involved a progressive curriculum starting on a bench-top model and then moving to the EndoVR® virtual reality simulator. The primary outcome was polypectomy-specific performance using the Direct Observation of Polypectomy Skills (DOPyS) tool during a simulated polypectomy after training, with a maximum score of 100. Results Demographic characteristics are summarized in Table. In 2019, 21 novice endoscopists were enrolled. Immediately after training, the mean DOPyS score among ARC group participants was 76.2 (SD=17.9) compared to 71.8 (SD=13.2) among control group participants (Figure). In this interim analysis, there was no significant difference between groups. Data analysis will be completed after 2020 participants complete the study. Conclusions Interim results show a trend towards improved polypectomy performance with no significant difference. The results of this study have the potential to impact polypectomy education among novices. Simulation-based AR interventions may allow learners to progress towards achieving competency in polypectomy in a risk-free environment prior to first patient contact. Funding Agencies None
Analyzing the Impact of Age and Gender on User Interaction in Gaming Environment

The age andJamil, Abid genderNadeem Faisal, Ch. M. are the significantHabib, Muhammad Asif demographicJabbar, Sohail characteristicsAhmad, Haseeb in the context of usability. The different age and gender groups may have different perceptions of the aesthetics of user interfaces. The primary objective of this study is to analyze the influence of these factors on user performance in a gaming environment. To investigate the performance of users on commonly used tasks (clicking, pointing, and typing), 360 participants participated in controlled laboratory experiments through a gaming prototype. The performance of the participants was analyzed through different statistical methods. It is observed that there were significant differences between males and females during interaction with the computer. Such differences were also found in the spectrum of age groups. This study may pave new research directions to explore the impact of these factors in multiple different contexts.
Decomposition and Visualization of High-Dimensional Data in a Two Dimensional Interface

Data visualization has a crucial role in understanding and processing voluminous data (i.e., Big Data) and subsequently has become more important with the coincidence of the exponential growth of data analysis need.The problem of high-dimensional data visualization in a data processing software interface cannot be entirely displayed, in consideration that once the data size exceeds two-dimension, it cannot be projected into a two-dimension interface. Furthermore, the rough analysis and evaluation of high-dimensional data become considerably ambiguous, thus, making a precise decision on that data cannot be achieved. In order to overcome this anomaly, resorting to data dimensionality reduction is a plausible solution.In this paper, the integration of Principal Component Analysis (PCA) combined with the Matrix by Block Decomposition (MBD) method(A.K.A block segmentation). According to the literature, the MBD method turned out quite efficient in data segmentation, wherein a huge data can be divided into regular blocks. By doing so, it becomes easier to access and visualize a given part of data. In order to further enhance the visualization understanding, K-means segmentation has been integrated in our proposed algorithm.In our study, we took into account other data dimensionality reduction techniques such as Linear Discriminant Analysis (LDA), Multi-Dimensional Scaling(MDS).
Towards Formal Verification of Neural Networks: A Temporal Logic Based Framework

Due to extensive applications of deep learning and neural networks, their security has attracted more and more attentions from academic and industrial circles. Under the guidance of the theory of formal verification, this paper summarizes three basic problems which indicate the common features of different neural networks, and proposes three typical properties covering the correctness of a model, the correctness of a sample and the robustness of a model for neural network systems. The method is driven by these properties, the model is constructed using the MSVL language and the properties are characterized by the logic PPTL. On this basis, the modeling and verification process is done in the MC compiler.
A Makespan Based Framework for Detection of SLA Violations in Cloud Computing Environment

Service level agreement (SLA) plays a vital role in delivery of cloud computing services. SLA is an agreement between the cloud service provider and the customer(s). It specifies the quality of services (QoS) to be delivered along with time deadlines and penalties to be imposed, in the event of a violation of agreement. There are research evidences that show that an agreement with larger number of check metrics may result better QoS. Servers downtime, minimum bandwidth, maximum computation time, etc., are some examples of these metrics. The work presented here introduces a new metric called makespan in SLA and proposes a framework for detection of violations in SLA. Makespan is the maximum completion time of all jobs submitted to the cloud. The proposed framework mathematically calculates the makespan at the time of SLA definition. The time period, for which SLA is violated, can be estimated from the comparison of simulation results. We also calculated makespan for different cloudlet scheduling algorithms. The study also performed comparative analysis of popular scheduling algorithms like first come first serve (FCFS), shortest job first (SJF), round-robin (RR), and particle swarm optimization (PSO). The experimental work was carried on CloudSim simulator, and algorithms were implemented in JAVA programming language. The results obtained are promising. Following are some of the key findings of the study which states that for small number of cloudlets, SLA violations in SJF are highest as compared with other algorithms. For large number of cloudlets, SLA violations in RR are highest. PSO has shown uniformly low SLA violations for both small number and large number of cloudlets. The study not only proposes a novel technique for detection of SLA violations but also highlights possible research extensions in this field.