Reviewerly/exHarmony huggingface dataset:
Application of Improved Ant Colony Algorithm in the Path Planning Problem of Mobile Robot

In this paper a global path planning method for mobile robots based on improved ant colony algorithm was proposed. Which overcome the problem that the traditional ant colony algorithm was prone to deadlock, may not get the global optimal solution and easily get into the local optimal problem. The transfer probability of ants in the traditional ant colony algorithm was adjusted to improve the occurrence of deadlock in the paper. And the roulette wheel algorithm in genetic algorithm was introduced to avoid the ant colony algorithm falling into the local optimal solution. At last the optimal parameter combination of the improved ant colony algorithm was obtained through simulation experiment. It could be seen from the simulation experimental data that the number of iterations to find the shortest path under the same conditions was reduced to 47.8%, which proved that the adoption of improved ant colony algorithm for path planning of mobile robot greatly improves the operating efficiency.
Exploring Eye Tracking Data on Source Code via Dual Space Analysis

Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers' eye tracking behaviors.
Type-1 and Type-2 fuzzy Sets to Control a Nonlinear Dynamic System

This paper is related to the simulation, in Matlab environment, of a robot manipulator controlled by both type-1 and interval type-2 fuzzy controllers, in which a modification in Karnik-Mendel algorithm has been proposed.To calculate the output of interval type-2 fuzzy system there is a main step called type-reduced; this operation is based on Karnik-Mendel algorithm, which uses arithmetic mean to calculate the control output.In this work, we propose to change the arithmetic mean by harmonic one.The performances of modified interval type-2 controller and type-1 fuzzy controller with and without noises are compared in terms of integral of squared error.The proposed modification in type reduction of Karnik-Mendel algorithm for interval type-2 fuzzy set shows best performance.Indeed, the amount of error in case of modified interval type-2 fuzzy controller is less two times than type-1 fuzzy controller.
Failure-Aware Application Placement Modeling and Optimization in High Turnover DevOps Environment

DevOps (software DEVelopment and information technology OPerationS) has established a culture and environment, in which building, testing, and releasing software happen more rapidly, frequently, and reliably through automated pipelines. To support this high turnover application cycle, the cluster orchestration frameworks such as Kubernetes or Docker Swarm have evolved to provide high flexibility and reliability. However, while cluster orchestrators run on the cloud infrastructure, any failure incurred from the infrastructure can directly impact the nodes of a cluster, so infrastructure failures can disrupt applications running on the cluster. In this paper, we propose proactive application placement algorithms with prediction of infrastructure failures. The proposed algorithms utilize failure-risk measurements, Failure-Index, determined from turnover rate of applications and prediction of infrastructure failures. We build stochastic models for application turnover and infrastructure failure processes, and provide various types of Failure-Index. Our placement algorithms are implemented in an orchestration framework, Kubernetes, and in the simulation model. Experimental results show that our methods reduce the amount of application disruption by 20% than the state-of-the-art algorithms in Kubernetes.
Interaksi Sosial dalam Proses Pembelajaran

This study aims to provide an overview of students' social interactions in the learning process for instructors and prospective teachers. In this article a number of things will be reviewed, including the meaning of social interaction and learning, forms of social interaction and learning, and interaction relationships with learning. After understanding the interaction in the learning process, instructors and prospective instructors are expected to understand that learning outcomes and learning processes must be balanced so that the educational process leads to the development of attitudes, intellectual intelligence, or the development of children's skills according to their needs.
Query Expansion and Query Fuzzy with Large-Scale Click-through Data for Microblog Retrieval

Searchers using microblog search often have difficulty in expressing their needs, they often struggle to modify queries by adding or removing terms, and this lead to unsatisfied experience.On the other hand, Microblogs content is limited in size, contain a few words and these will cause a real problem of term mismatch in microblog search.We propose a probabilistic model for query reformulation on click-through data, which can help user auto-complete their search need.We assume that the knowledge of good queries of former searchers can then be used for improving a poor query of following users, by expanding extra meaningful terms or fuzzing trivial query terms.The key idea is to discover terms that can grasp users' click need or carry less meaning from large-scale click-through data.Our results indicate the reformulated query can better describe users's real functional need.In the experiment, we try to reduce query drift and noise in click-through data by using crowd-sourced relevance feedback and smoothing methods.The experimental result is much better than that of searching only the original query, especial on gains of low-frequency and long-tailed query.
Memory limitations are hidden in grammar

The ability to produce and understand an unlimited number of different sentences is a hallmark of human language. Linguists have sought to define the essence of this generative capacity using formal grammars that describe the syntactic dependencies between constituents, independent of the computational limitations of the human brain. Here, we evaluate this independence assumption by sampling sentences uniformly from the space of possible syntactic structures. We find that the average dependency distance between syntactically related words, a proxy for memory limitations, is less than expected by chance in a collection of state-of-the-art classes of dependency grammars. Our findings indicate that memory limitations have permeated grammatical descriptions, suggesting that it may be impossible to build a parsimonious theory of human linguistic productivity independent of non-linguistic cognitive constraints.
Polynomial transformation model for frame-to-frame registration in an adaptive optics confocal scanning laser ophthalmoscope

The adaptive optics (AO) technique has been integrated in confocal scanning laser ophthalmoscopy (SLO) to obtain near diffraction-limited high-resolution retinal images. However, the quality of AOSLO images is decreased by various sources of noise and fixational eye movements. To improve image quality and remove distortions in AOSLO images, the multi-frame averaging method is usually utilized, which relies on an accurate image registration. The goal of image registrations is finding the optimal transformation to best align the input image sequences. However, current methods for AOSLO image registration have some obvious defects due to the limitation of transformation models. In this paper, we first established the retina motion model by using the Taylor series and polynomial expansion. Then we generated the polynomial transformation model and provided its close-form solution for consecutively frame-to-frame AOSLO retina image registration, allowing one to consider more general retinal motions such as scale changes, shearing and rotation motions, and so on. The experimental results demonstrated that higher-order polynomial transformation models are helpful to achieve more accurate registration, and the fourth-order polynomial transformation model is preferred to accomplish an efficient registration with a satisfying computational complexity. In addition, the AKAZE feature detection method was adopted and improved to achieve more accurate image registrations, and a new strategy was validated to exclude those unsuccessful registered regions to promote the robustness of image registration.
An Improved Gravitational Coefficient Function for Enhancing Gravitational Search Algorithm’s Performance

This article proposes a new gravitational coefficient function of the gravitational search algorithm (GSA).Since the function concerns to the performance of GSA, we investigate its characteristic which influences the algorithm on global search performance.The novel function is compared to a former function in literature on four benchmark functions which incorporated of both unimodal landscape functions and multimodal landscape functions.The experimental results show that the proposed gravitational coefficient function outperforms the conventional one.The proposed function also shows that it works well on multimodal landscape functions.By balancing between exploration phase and exploitation phase, the slow convergence rate is compensated by the better solutions.
Classification of web services using data mining algorithms and improved learning model

As per the global digital report, 52.9% of the world population is using the internet, and 42% of the world population is actively using e-commerce, banking, and other online applications. Web services are software components accessed using networked communications and provide services to end users. Software developers provide a high quality of web service. To meet the demands of user requirements, it is necessary for a developer to ensure quality architecture and quality of services. To meet the demands of user measure service quality by the ranking of web services, in this paper, we analyzed QWS dataset and found important parameters are best practices, successability, availability, response time, reliability and throughput, and compliance. We have used various data mining techniques and conducted experiments to classify QWS data set into four categorical values as class1, 2, 3, and 4. The results are compared with various techniques random forest, artificial neural network, J48 decision tree, extreme gradient boosting, K-nearest neighbor, and support vector machine. Multiple classifiers analyzed, and it was observed that the classifier technique eXtreme gradient boosting got the maximum accuracy of 98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service for mixed attributes.
Teaching Mild Mentally Retarded Children using Augmented Reality

Every child has the right to get an education, including mild mentally retarded children. As like normal children, mild mentally retarded children need education that can help their growth and development in accordance with their potential. However, learning media for mild mentally retarded children still have several obstacles, one of which is object recognition. Object recognition for mild mentally retarded children usually uses 2-dimensional objects that are difficult to recognize. In addition, when using 2-dimensional objects not all aspects of development can be developed properly. The purpose of this study is to provide an overview of the use of augmented reality in developing various aspects of development in mild mentally retarded children. The methodology in this study is qualitative descriptive. Data collection techniques used observation and interviews with mild mentally retarded children and one special teacher in West Java, Indonesia. Data were analyzed through several stages, namely data reduction, data presentation, data conclusions, and data validation using triangulation. The results of the study show that using the augmented reality technology, the mild mentally retarded children can be easier to recognize an object. In addition, the use of augmented reality can develop various aspects of development in mild mentally retarded children ranging from moral and religious values, cognitive, language, physical-motoric, social emotional, and artistic. Special schools or inclusive schools can utilize augmented reality as a learning medium in introducing objects to mild mentally retarded children. https://doi.org/10.26803/ijlter.18.7. 12
A Heuristic Initialized Memetic Algorithm for the Joint Allocation of Heterogeneous Stochastic Resources

In this paper, a mathematical model for the joint allocation of two heterogeneous stochastic resources (namely, sensors and actuators) is presented, addressing the interdependencies between sensors and actuators, the resource constraints, the capability constraints as well as the strategy constraints. A heuristic initialized memetic algorithm (MA) is proposed to solve the joint allocation problem about stochastic resources (JASR). The integer-based dual-permutation encoding method is adopted and several permutation-based operators are involved in the process of crossover, mutation and local search. Besides, a hybrid initialization method is employed to maintain a balance between exploration and exploitation. For the performance evaluation, we build a general Monte Carlo simulation based JASR framework. Furthermore, we employ an extension of the state-of-the-art algorithm Swt_opt, MRBCH and BMA as competitors. Computational results show that the proposed MA performs very well in solving JASR instances of different scales, and it can generate better assignment schemes in most cases than its competitors in limited time.
Multivariate Locally Stationary Wavelet Analysis with the <b>mvLSW</b> <i>R</i> Package

This paper describes the R package mvLSW. The package contains a suite of tools for the analysis of multivariate locally stationary wavelet (LSW) time series. Key elements include: (i) the simulation of multivariate LSW time series for a given multivariate evolutionary wavelet spectrum (EWS); (ii) estimation of the time-dependent multivariate EWS for a given time series; (iii) estimation of the time-dependent coherence and partial coherence between time series channels; and, (iv) estimation of approximate confidence intervals for multivariate EWS estimates. A demonstration of the package is presented via both a simulated example and a case study with EuStockMarkets from the datasets package.
Real-Time Person Re-identification at the Edge: A Mixed Precision Approach

A critical part of multi-person multi-camera tracking is person re-identification (re-ID) algorithm, which recognizes and retains identities of all detected unknown people throughout the video stream. Many re-ID algorithms today exemplify state of the art results, but not much work has been done to explore the deployment of such algorithms for computation and power constrained real-time scenarios. In this paper, we study the effect of using a light-weight model, MobileNet-v2 for re-ID and investigate the impact of single (FP32) precision versus half (FP16) precision for training on the server and inference on the edge nodes. We further compare the results with the baseline model which uses ResNet-50 on state of the art benchmarks including CUHK03, Market-1501, and Duke-MTMC. The MobileNet-V2 mixed precision training method can improve both inference throughput on the edge node, and training time on server 3.25 $$\times $$ reaching to 27.77 fps and 1.75 $$\times $$ , respectively and decreases power consumption on the edge node by 1.45 $$\times $$ , while it deteriorates accuracy only 5.6% in respect to ResNet-50 single precision on the average for three different datasets. The code and pre-trained networks are publicly available. ( https://github.com/TeCSAR-UNCC/person-reid )
An Industrial Case Study for Adopting Software Product Lines in Automotive Industry

Software Product Lines (SPLs) seek to achieve gains in productivity and time to market. Many companies in several domains are constantly adopting SPLs. Dealing with SPLs begin after companies find themselves with successful variants of a product in a particular domain. The adoption of an SPL-based approach in the automotive industry may provide a significant return on investment. To switch to an SPL-based approach, practitioners lack a reengineering approach that supports SPL migration and evolution in a systematic fashion.
Future Emoji Entry Prediction Using Neural Networks

In today's world, textual data has made momentous progress in social media.The rise of digital communication via text has paved the way to emoji, a pictographically represented way of expressing emotions.In digital communication, Emoji gives a visual appeal to the text, which improves communication and new vistas of exchange and creativity.While emoji entry prediction based on text is well optimized, based on the neural network model, predicting the future emojis from images is not so easy due to lack of knowledge on the same.While effective models already exist for generating text descriptions of images, less attention has been given to models of symbolic description.We have used two models for predicting emoji from images, convolutional neural network architecture for image classification and an emoji2vec embedding into word2vec model.We have also done a sentiment analysis of the text for predicting future emoji labels.Our model captures the relation between emojis in an optimized way.This model has optimized the search time for future emoji entry predictions from images.
Dimension reduction of high-dimensional dataset with missing values

Nowadays, datasets containing a very large number of variables or features are routinely generated in many fields. Dimension reduction techniques are usually performed prior to statistically analyzing these datasets in order to avoid the effects of the curse of dimensionality. Principal component analysis is one of the most important techniques for dimension reduction and data visualization. However, datasets with missing values arising in almost every field will produce biased estimates and are difficult to handle, especially in the high dimension, low sample size settings. By exploiting a Lasso estimator of the population covariance matrix, we propose to regularize the principal component analysis to reduce the dimensionality of dataset with missing data. The Lasso estimator of covariance matrix is computationally tractable by solving a convex optimization problem. To illustrate the effectiveness of our method on dimension reduction, the principal component directions are evaluated by the metrics of Frobenius norm and cosine distance. The performances are compared with other incomplete data handling methods such as mean substitution and multiple imputation. Simulation results also show that our method is superior to other incomplete data handling methods in the context of discriminant analysis of real world high-dimensional datasets.
L3VTP

short-paper Share on L3VTP: A Low-Latency Live Video Transmission Platform Authors: Gang Yi Tsinghua University Tsinghua UniversityView Profile , Dan Yang Beijing University of Posts and Telecommunications Beijing University of Posts and TelecommunicationsView Profile , Mowei Wang Tsinghua University Tsinghua UniversityView Profile , Weihua Li PowerInfo PowerInfoView Profile , Yi Li PowerInfo PowerInfoView Profile , Yong Cui Tsinghua University Tsinghua UniversityView Profile Authors Info & Claims SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and DemosAugust 2019 Pages 138–140https://doi.org/10.1145/3342280.3342336Published:19 August 2019Publication History 3citation380DownloadsMetricsTotal Citations3Total Downloads380Last 12 Months45Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Accurate Detection Method of Aviation Bearing Based on Local Characteristics

Aviation bearing assembled detection is the final barrier to quality and safety. Therefore, an accurate detection method of aviation bearing that is based on local characteristics is designed to solve the detection problem of mis-assembly and miss-assembly of balls in aviation bearing assembled. When considering the spatial limitation of aviation bearing assembled image acquisition, the dynamic distribution of balls and the interference of lubricating grease on the surface, a dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is designed to achieve the accurate segmentation of the local ball region of aviation bearing. Subsequently, an incomplete circle fitting algorithm is designed based on the segmented local ball image and Hough transform principle. These two algorithms make the measurement error of aviation bearing ball size less than 100 μm. Using bearings validates the algorithm. The results show that the accuracy of dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is over 99%. At the same time, on the basis of accurate segmentation in aviation bearing local ball, the designed Hough circle algorithm is used for circle detection. The experimental results show that the false detection rate of mis-assembly and miss-assembly of balls is less than 3%. Further, the goal of zero-missed detection of mis-assembly and miss-assembly of balls in aviation bearing is achieved. The accurate segmentation of aviation bearing local ball and the effective identification of mis-assembly and miss-assembly of balls are realized. This method can provide a theory for the improvement of mis-assembly and miss-assembly of balls detection in aviation bearing. Furthermore, it has high application value.
Video frames encryption based on DNA sequences and chaos

In this study, we propose an encryption scheme for significant video frames based on DNA sequences and chaotic system. Initially, we use video frame separation method to decompose the video into video frames which contain key frames and non-key frames. Furthermore, the key frame image with fire information is extracted and encrypted using DNA sequences and logistic mapping generated random vector. Experiments show that the encrypted image has well performance to prevent various security attacks.