Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling

Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection

Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.Comment: This paper will appear in CSCW 202

Storyfier: Exploring Vocabulary Learning Support with Text Generation Models

Vocabulary learning support tools have widely exploited existing materials, e.g., stories or video clips, as contexts to help users memorize each target word. However, these tools could not provide a coherent context for any target words of learners' interests, and they seldom help practice word usage. In this paper, we work with teachers and students to iteratively develop Storyfier, which leverages text generation models to enable learners to read a generated story that covers any target words, conduct a story cloze test, and use these words to write a new story with adaptive AI assistance. Our within-subjects study (N=28) shows that learners generally favor the generated stories for connecting target words and writing assistance for easing their learning workload. However, in the read-cloze-write learning sessions, participants using Storyfier perform worse in recalling and using target words than learning with a baseline tool without our AI features. We discuss insights into supporting learning tasks with generative models.Comment: To appear at the 2023 ACM Symposium on User Interface Software and Technology (UIST); 16 pages (7 figures, 23 tables

AmbiguityVis: Visualization of Ambiguity in Graph Layouts

Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graphlayout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteriasimultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attentionto these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visualambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguityin edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in communitystructures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others arethen displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualizationapproaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a userto explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectivenessof the technique is demonstrated through case studies and expert reviews

Frequency and distribution of AP-1 sites in the human genome

The AP-1-binding sequences are promoter/enhancer elements that play an essential role in the induction of many genes in mammalian cells; however, the number of genes containing AP-1 sites remains unknown. In order to better address the overall effect of AP-1 on expression of genes encoded by the entire genome, a genome-wide analysis of the frequency and distribution of AP-1 sites would be useful; yet to date, no such analysis of AP-1 sites or any other promoter/enhancer elements has been performed. We present here our study of the consensus AP-1 site and two single-bp variants showing that the frequency of AP-1 sites in promoter regions is significantly lower than their average rate of occurrence in the whole genomic sequence, as well as the frequency of a random heptanucleotide suggesting that nature has selected for a decrease in the frequency of AP-1 sites in the regulatory regions of genes. In addition, genes containing multiple AP-1 sites are more prevalent than those containing only one copy of an AP-1 site, which again may have evolved to allow for greater signal amplification or integration in the regulation of AP-1 target genes. However, the number of AP-1-regulated genes identified in various studies is far smaller than the number of genes containing potential AP-1 sites, indicating that not all AP-1 sites are activated in a given cell under a given condition, and is consistent with the prediction by others that cellular context determines which AP-1 sites are targeted by AP-1

Strong Neel ordering and luminescence correlation in a two-dimensional antiferromagnet

Magneto-optical effect has been widely used in light modulation, optical sensing and information storage. Recently discovered two-dimensional (2D) van der Waals layered magnets are considered as promising platforms for investigating novel magneto-optical phenomena and devices, due to the long-range magnetic ordering down to atomically-thin thickness, rich species and tunable properties. However, majority 2D antiferromagnets suffer from low luminescence efficiency which hinders their magneto-optical investigations and applications. Here, we uncover strong light-magnetic ordering interactions in 2D antiferromagnetic MnPS3 utilizing a newly-emerged near-infrared photoluminescence (PL) mode far below its intrinsic bandgap. This ingap PL mode shows strong correlation with the Neel ordering and persists down to monolayer thickness. Combining the DFT, STEM and XPS, we illustrate the origin of the PL mode and its correlation with Neel ordering, which can be attributed to the oxygen ion-mediated states. Moreover, the PL strength can be further tuned and enhanced using ultraviolet-ozone treatment. Our studies offer an effective approach to investigate light-magnetic ordering interactions in 2D antiferromagnetic semiconductors

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

Cloud-based web applications are proliferating fast. Owing to the elastic capacity and diverse pricing schemes, cloud Infrastructure-as-a-Service (IaaS) offers great opportunity for web application providers to optimize resource cost. However, such optimization activities are confronting the challenges posed by the uncertainty of future demand and the increasing reservation contracts. This work investigates the problem of how to minimize IaaS rental cost associated with hosting web applications, while meeting the demand in the future business cycle. First, an integer liner program model is developed to optimize reservation-contract procurement, in which reserved and on-demand resources are planned for multiple provisioning stages as well as a long-term plan, e.g., twelve stages in an annual plan. Then, a Long Short-Term Memory (LSTM) based algorithm is designed to predict the workload in the future business cycle. In addition, the approaches for determining virtual instance capacity and the baseline workload of planning time slot are also presented. Finally, the experimental prediction results show the LSTM-based algorithm gains an advantage over several popular models, such as the Holter–Winters, the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Support Vector Regression (SVR). The simulations of resource planning show that the provisioning scheme based on our reservation-optimization model obtains significant cost savings than other typical provisioning schemes, while satisfying the demands

Coupling behaviors of quarter-wavelength impedance transformers for wideband CPW bandpass filters

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