Reviewerly/exHarmony huggingface dataset:
A3

short-paper Share on A3: An Automatic Malfunction Detection and Fixation System in FatTree Data Center Networks Authors: Che Zhang Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, China Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, ChinaView Profile , Shiwei Zhang Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, China Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, ChinaView Profile , Bo Jin Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, China Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, ChinaView Profile , Weichao Li Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, China Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, ChinaView Profile , Zhen Wang Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, China Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, ChinaView Profile , Yi Wang Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, China Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China PCL Research Center of Networks and Communications, Pengcheng Laboratory, Shenzhen, ChinaView Profile Authors Info & Claims SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and DemosAugust 2019 Pages 24–26https://doi.org/10.1145/3342280.3342295Published:19 August 2019Publication History 1citation200DownloadsMetricsTotal Citations1Total Downloads200Last 12 Months14Last 6 weeks1 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
Super-Resolution Imaging Using Convolutional Neural Networks

Convolutional neural networks (CNN) have been applied to many classic problems in computer vision. This paper utilized CNNs to reconstruct super-resolution images from low-resolution images. To improve the performance of our model, four optimizations were added in the training process. After comparing the models with these four optimizations, Adam and RMSProp were found to achieve the optimal performance in peak signal to noise ratio (PSNR) and structural similarity index (SSIM). Considering both reconstruction accuracy and training speed, simulation results suggest that RMSProp optimization in the most scenarios.
Scene Recognition via Semi-Supervised Multi-Feature Regression

With the development of visual sensor equipment (e.g., personal smart phones, vehicle cameras, surveillance videos and camcorders), scene recognition technology has attracted much attention due to its latent applications in visual surveillance, intelligent traffic and aerial remote sensing. Although some progress has been made in the field of scene recognition in recent years, the complexity of scene images and the inadequate numbers of labeled data pose challenges in this area. Hence, to effectively fuse the multiple features of each image and employ the information of both labeled and unlabeled images for scene recognition, we proposed a semi-supervised multi-feature regression (SSMFR) model in this paper. The SSMFR model possesses three advantages. First, the model propagates the labels of labeled data to unlabeled data by utilizing graph-based semi-supervised learning techniques so that both the information regarding unlabeled data and labeled data can be exploited to gain better performance. Second, SSMFR employs multiple graphs to characterize the structures of multiple feature spaces and adaptively assigns the weight to different graphs. Therefore, SSMFR can efficiently preserve the manifold structure of samples in each feature space and adequately exploit the complementary information of multiple features. Moreover, SSMFR adopts a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula> -norm constraint to learn a sparse and robust classifier for scene recognition. To solve the SSMFR model, we proposed a simple and efficient iterative update optimization scheme. Finally, we also proved the convergence of SSMFR by theoretical analysis and experiments. Experiments were conducted on several benchmark scene datasets, and the experimental results demonstrated that the proposed SSMFR model can obtain better performance for scene recognition than some other state-of-the-art algorithms.
Quantum Wireless Network Private Query With Multiple Third Parties

With the development of quantum wireless networks, the practical application of quantum private query (QPQ) should conform to the features of quantum wireless networks to achieve any-to-any query, and QPQ requires a new query mechanism. We first propose a scheme for a network private query in a quantum wireless network with multiple third parties, in which any user can query any database with the assistance of the third parties, by measuring each of the distant nodes that initially share entanglement with each other. The required entanglement among these nodes is established by the third-party intermediate node equipped with GHZ generator using multi-qubit GHZ states. A bilayer network topology, as well as the corresponding private query protocol, ensures the rapid and secure private query. Under the protection of the multi-qubit GHZ state's quantum correlation, the privacy of Alice and Bob is protected from the third parties' fake entangled attack and external attack, and they cannot obtain the privacy of each other by taking quantum memory attack. The performance parameters of the proposed scheme are described based on the analysis of classical wireless communication cost and communication delay.
Soil mineralogy and geochemistry along a north-south transect in Alaska and the relation to source-rock terrane

statistics for chemical analyses of soil samples from the north-south transect of Alaska.2. Plots of mineral contents in soil samples from the upper and lower mineral soil horizons at sites along the north-south transect of Alaska.3. Box plots of elemental contents in soil samples at sites along the north-south transect of Alaska.4. Mineralogical and chemical data for all transect soil samples, standard reference materials, and laboratory splits. DatumVertical coordinate information is referenced to the North American Vertical Datum of 1983 (NAVD 83).Horizontal coordinate information is referenced to North American Datum of 1983 (NAD 83).Altitude, as used in this report, refers to distance above the vertical datum.
Las TIC y su influencia en la calidad del aprendizaje universitario

This paper analyzes information and communication technologies (ICTs) and their important role on the quality of university education at the Department of Economics of the Universidad Nacional Mayor de San Marcos (UNMSM). To this end, university education in Latin American and European countries is studied in order to identify strengths and use them as reference points to improve the quality of education at UNMSM, especially at said department. A questionnaire, directed at 120 respondents, including professors and students of the School of Economic Sciences, is used as a data collection instrument, which is then analyzed using the Likert scale. This analysis makes it possible to determine the degree of influence ICTs have on the quality of learning at said school, as well as the benefits of implementing the identified strengths of foreign university education.
A Holistic Model for Performance Prediction and Optimization on NUMA-based Virtualized Systems

The non-uniform memory access (NUMA) architecture has become the dominant server architecture due to its scalable bandwidth performance. However, the NUMA architecture also introduces the complicated performance influences to the applications, because of the differentiated remote devices access latency and shared resource access contention. Secondly, quick developments of high speed networking devices make I/O resource be another important performance affecting element for I/O-intensive cloud applications on NUMA server. Thirdly, it is more critical in virtualized environment since all resources are managed uniformly and transparently to the VM, and the application behaviors in the VM are shielded from the Virtual Machine Manager (VMM). In this paper, we first give an analytic evaluation for performance influence from the various resource affinity. Motivated by the observations, we then build an accurate performance prediction model, named Resource Affinity performance Influence Estimation (RAIE). RAIE provides a novel performance prediction model with the holistic resource affinity parameters that are measured with the platform independent quantification approaches that need be executed in one-off manner. Moreover, RAIE model takes into account the actual influence of resource affinity according to the VM behaviours that can be monitored online without VM modification. Comprehensive evaluations prove that the RAIE model for a VM's performance prediction can increase the average prediction accuracy by 3.27x on a 4node NUMA server with high speed Network Interface Cards (NIC). The RAIE guided scheduling case validates that it can achieve 2.1x performance improvement for actual VMM resource management servicing a VM running the dynamic applications.
Correcting Typographical Error and Understanding User Intention in Chatbot by Combining N-Gram and Machine Learning Using Schema Matching Technique

Purpose of this research is to make chatbot based system to help Small and Medium Enterprise business.Initially, we build this application only to help Small and Medium Enterprise owner to monitor their business and report.Yet, we realize that we can make our chatbot to be more effective and efficient using machine learning technique.N-gram and machine learning using schema matching are embedded to the chatbot to understand user intention and correct typographical error inside the sentences.Finally, the chatbot has been successfully achieved those objectives.It can be concluded that the chatbot can drive the users' feeling to be more convenient and help Small and Medium Enterprise owner to monitor their business.
Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana

Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.&#x0D;
ICT Usage in the Czech Business and the Strategy of Digital Literacy in the Czech Republic

The paper is focused on the current situation of ICT usage in the Czech business.Attention is paid to the role of ICT in business as well as the status of ICT skills of employees.Unfortunately, a gap still exists between the needs and the real situation.Therefore, adequate actions have to be prepared and implemented.The paper points to actions that will lead to improvement of the skills required for effective use of ICT in practice.Strategy of digital literacy is showed there.The discussion is oriented towards a joint search for the optimal paths leading to improvement of ICT skills and their subsequent effective utilization in practice.
Computed Torque Control of an Aerial Manipulation System with a Quadrotor and a 2-DOF Robotic Arm

This paper presents the control of an aerial manipulation system with a quadrotor and a 2-DOF robotic arm by using the computed torque control method.The kinematic and dynamic model of the system is obtained by modeling the quadrotor and the robotic arm as a unified system.Then, the equation of motion of the unified system is got in the form of a standard robot dynamics equation.For the trajectory control of the system, computed torque control is used.Gains of the controller are optimized by using nonlinear least squares method.The performance and stability of the control structure are tested with a simulation case study.
vSimilar: A high-adaptive VM scheduler based on the CPU pool mechanism

In a virtualized system, the virtual machine (VM) scheduler plays a key role to the performance promotion of the virtual machine monitor (VMM), a.k.a. hypervisor. The scheduler is responsible for assigning adequate system resources to each VM according to the demands of the VM tenants, which is quite challenging as VM tenants’ demands are quite dynamic and unpredictable. To this end, CPU pool mechanism has been widely adopted as an adaptive solution. However, the CPU pool mechanism still has defficiency in terms of VM classification model and time-slice allocation strategy, as the two strategies have to be effectively utilized for realizing a high-adaptive VM scheduler. In this paper, we thus explore opportunities to improve the CPU pool mechanism and develop a new VM scheduling solution, called vSimilar, which uses VM multi-classification model to more effectively adaptive to the VMs of running different types of tasks at different time. Moreover, by a dynamic time-slice function, vSimilar manages to provide a more efficient resource allocation. The experimental evaluation shows that vSimilar can significantly improve the performance of a VMM, such as Xen. The improvements include 1) a VM server hosted by Xen with vSimilar can reduce nearly 95% of a client’s Ping round-trip time (Ping RTT), 2) vSimilar can help increase about 40% the TCP throughput, and about 20% the UDP throughput, between a Xen-hosted VM server and a client, and 3) vSimilar also increases the page operation rate by nearly 50% for a Xen-hosted VM Web server.
Real Time Control Plane Verification

In cloud datacenters, network configuration changes frequently, thus fast control plane verification is demanded to validate the changes before deployed. However, existing control plane verification tools are all based on static analysis of network configuration, so that the configuration need to be reverified completely when edited. Inspired by the real time data plane verification, we propose an incremental control plane verification. Its implementation based on an existing work demonstrated supeior performance.
Generalization of real interval matrices to other fields

An interval matrix is a matrix whose entries are intervals in $\R$. This concept, which has been broadly studied, is generalized to other fields. Precisely, a rational interval matrix is defined to be a matrix whose entries are intervals in $\Q$. It is proved that a (real) interval $p \times q$ matrix with the endpoints of all its entries in $\Q$ contains a rank-one matrix if and only if it contains a rational rank-one matrix, and contains a matrix with rank smaller than $\min\{p,q\}$ if and only if it contains a rational matrix with rank smaller than $\min\{p,q\}$; from these results and from the analogous criterions for (real) inerval matrices, a criterion to see when a rational interval matrix contains a rank-one matrix and a criterion to see when it is full-rank, that is, all the matrices it contains are full-rank, are deduced immediately. Moreover, given a field $K$ and a matrix $\al$ whose entries are subsets of $K$, a criterion to find the maximal rank of a matrix contained in $\al$ is described.
Moves Recognition in Abstract of Research Paper Based on Deep Learning

The purpose of this work is to explore the applicability and effectiveness of deep learning methods for the task-moves recognition in abstract of research paper. We firstly build a large corpus for moves recognition. Then we choose the traditional machine learning method SVM as a benchmark, and develop four moves recognition methods based on DNN, LSTM, Attention-BiLSTM and BERT. Finally, we design two groups of experiments with sample size 10,000 and 50,000 and then compare experimental results. The results show that most of the deep learning methods outperform the traditional machine learning method SVM especially in large-scale sample experiments, in which the BERT with a re-pre-trained model achieves the best results in both groups of experiments. Deep learning methods are proved applicable and effective for moves recognition in research paper abstracts.
Reading Habits of Postgraduate Students of Khalsa College, Amritsar: A Survey

Reading is an important activity during college life. In the young age the mind is ready to acquire knowledge of different types. This study attempts to know the reading habits of postgraduate students of Khalsa College Amritsar. The data was collected from students by using random sampling technique through questionnaire method. This study found that a majority of the students enjoy reading and book is still a preferred reading material. The students read in English language to get information. The students feel that information communication technologies have impact on reading and they read from both print and screen. The study revealed that watching movies, listening to music, use of facebook and whatSapp are the major obstacles to reading habits.
Combining time, keywords and authors information to construct papers correlation graph (S)

Nowadays, recommender systems have become one of the main tools and methods for users to search for their interested papers from massive candidates.Typically, through analyzing the typed keywords by a user, a recommender system can easily retrieve the papers that cover the keywords, in an efficient and economic manner.However, one paper often only contains partial keywords that the user is interested in; therefore, the recommender system needs to analyze a pre-built paper citation graph and then return a set of papers that collectively satisfy the user's requested keywords.While the existing paper citation graph does not consider the possible self-citations and potential correlations among the papers that are not connected in the paper citation graph but with close publication time.Considering the above drawbacks, in this paper, we propose a link prediction approach that combines time, keywords and authors information for constructing a new relation graph.Finally, a case study is employed to explain our approach step by step and demonstrate the feasibility of our proposal.
Do general Genetic Algorithms provide benefits when solving real problems?

There is a vibrant community devoted to developing novel Genetic Algorithms each year. The effectiveness of these algorithms is normally evaluated based upon a selected set of artificial functions self-selected by the same community. These sets are generated to reduce the complexity seen in real applications so they can be run in a computationally short period of time, so that the final answer is known and so that certain characteristics can be isolated, giving more information about the strengths and weaknesses of a given methodology. In particular, the literature is dominated by a wide range of continuous and unconstrained problems, which are dominated by only one characteristic. This leads to a bias in the current Genetic Algorithms towards a set of specialist solvers for these problems, dominated by convergence enhancing mechanisms, as the success of the current state-of-the-art is directly linked to them. In this paper, the success of these specialist solvers is determined on two different engineering problems to verify the performance of a number of specialist and general genetic algorithms. The results show that the general-solvers exhibit better performance on the engineering problems with and without constraints. It is concluded that more emphasis should be given to general solvers development and the development of new benchmarking problems should be broader in scope and contain problems with many different characteristics.
Impact of Agile Practices Adoption on Organizational Learning: a Survey in Brazil

Agile software development is a particularly intense knowledge activity in which the success depends greatly on the experience of the professionals involved in the process.Knowledge Management Strategies play an important role in assisting knowledge acquisition and sharing among Agile teams.In this scenario, this paper answer the following research question: What is the impact caused by the use of Agile practices in the process of organizational knowledge acquisition at software development companies?The objective is to analyze strategies for Knowledge Management among teams and evaluate the impact caused by the adoption of Agile practices on the Organizational Learning process.For this, we proposed a model which it was possible evaluate this impact.Thus, a survey was conducted with 455 respondents in order to validate the proposed model.The data collected in this research was processed and analyzed using Structural Equation Modeling.The results corroborates the impact of software development practices on Knowledge Management Strategies and Organizational Learning.Additionally, this study provides mechanisms for software engineering professionals to implement strategies that contribute to the knowledge acquisition and sharing in their teams.