Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intent-driven DaaS Management Framework to Enhance User Quality of Experience

Desktop as a Service (DaaS) has become widely used by enterprises. In 2020, the use of DaaS increased dramatically due to the demand to work remotely from home during the COVID-19 pandemic. The DaaS market is expected to continue growing rapidly [1]. The quality of experience (QoE) of a DaaS service has been one of the main factors to enhance DaaS user satisfaction. To ensure user QoE, the amount of cloud computation resources for a DaaS service must be appropriately designed. We propose an Intent-driven DaaS Management (IDM) framework to autonomously determine the cloud-resource-amount configurations for a given DaaS QoE requirement. IDM enables autonomous resource design by abstracting the knowledge about the dependency between DaaS workload, resource configuration, and performance from previous DaaS performance log data. To ensure the IDM framework's applicability to actual DaaS services, we analyzed five main challenges in applying the IDM framework to actual DaaS services: identifying the resource-design objective, quantifying DaaS QoE, addressing low log data availability, designing performance-inference models, and addressing low resource variations in the log data. We addressed these challenges through detailed designing of IDM modules. The effectiveness of the IDM framework was assessed from the aspects of DaaS performance-inference precision, DaaS resource design, and time and human-resource cost reduction.
Do we need sophisticated system design for edge-assisted augmented reality?

We revisit the performance of a canonical system design for edge-assisted AR that simply combines off-the-shelf H.264 video encoding with a standard object tracking technique. Our experimental analysis shows that the simple canonical design for edge-assisted object detection can achieve within 3.07%/1.51% of the accuracy of ideal offloading (which assumes infinite network bandwidth and the total network transmission time of a single RTT) under LTE/5G mmWave networks. Our findings suggest that recent trend towards sophisticated system architecture design for edge-assisted AR appears unnecessary. We provide insights for why video compression plus on-device object tracking is so effective in edge-assisted object detection, draw implications to edge-assisted AR research, and pose open problems that warrant further investigation into this surprise finding.
Tourist Motivation and Acceptance of Virtual Tour in Indonesia

Virtual tour enables people to experience a mediated travel during a pandemic. The study aims to clarify the motivational functions affecting acceptance of virtual tour as a substitute for physical tour during a pandemic. 102 People who experienced virtual tours arranged by tour operators responded to a questionnaire regarding their motivations and virtual tour experiences. We use a general linear model to explore the influences of tourists’ motivation functions on the unacceptance and acceptance of virtual tour. Compared to people with a high social adjustment motive, people with a high ego-defensive function motive are more easily accepting virtual tour. The study also indicates no significant effect between virtual tour acceptance and sociodemographic.
Optimization based non-linear observers revisited

Optimization based non-linear observers are those where the integrated output prediction error is used to define the dynamic of the observer's state. Their main advantage is genericity and the natural assumptions that are needed for the related convergence results to hold. Here, we present some new theoretical results concerning these observers. Despite the title we use, our observer is not based on the search for a global minimum of a cost function. It is based on a descent-like approach without the use of the Hessian matrix. A nice feature is that the resulting observer takes a classical form of in the sense that it is defined by a set of ordinary differential equations. Besides the theoretical results, some implementation issues are discussed. In particular, we propose to use the post stabilization approach, well known when dealing with differential systems with invariant submanifolds. This enables to increase the sampling period while keeping very good precision. Two illustrative examples are presente.
Photo Realistic Generation from Arabic Text Description Based on Generative Adversarial Networks

Generating accurate high-resolution images from text representations is a difficult problem in computer vision that has a wide range of functional applications. Text-to-image conversion is not dissimilar to the difficulties inherent in language processing. For example, each text meaning can be encoded in two distinct human languages, while photographs and text are two distinct encoding languages for similar data. However, these are two distinct issues, since text-to-image or image-to-text conversions are extremely multimodal in nature. The proposed model for creating 256 × 256 realistic images from Arabic text descriptions is discussed in this article. The relationship between an Arabic word in a sentence and its component in a picture as introduced in this paper using the DAMSM model. This model teaches two neural networks how to map the Arabic picture and word sub-regions of a full sentence to a shared semantic model. It performs well as an Arabic-text encoder and a picture encoder. We start with the Modified-Arabic dataset and train the model from scratch. The proposed model establishes a new standard for the conversion of Arabic text to realistic pictures. A mutation happens when Arabic is used as the primary language for converting Arabic texts to real images. The inception score of the newly introduced model reported by 3.42 ± . 05 on the CUB database.
Edge offloading for microservice architectures

Edge offloading is widely used to support the execution of near real-time mobile applications. However, offloading on edge infrastructures can suffer from failures due to the absence of supporting systems and environmental factors. We propose a fault-tolerant offloading method modeled as a Markov Decision Process (MDP) based on predictions performed through Support Vector Regression (SVR). SVR is used to estimate offloading service availability, which is used by MDP for offloading decisions. Our approach is implemented in a real-world test-bed and compared with the default Kubernetes scheduler augmented with hybrid fault-tolerance.
Pose and Shape Reconstruction of a Noncooperative Spacecraft Using Camera and Range Measurements

Recent interest in on-orbit proximity operations has pushed towards the development of autonomous GNC strategies. In this sense, optical navigation enables a wide variety of possibilities as it can provide information not only about the kinematic state but also about the shape of the observed object. Various mission architectures have been either tested in space or studied on Earth. The present study deals with on-orbit relative pose and shape estimation with the use of a monocular camera and a distance sensor. The goal is to develop a filter which estimates an observed satellite’s relative position, velocity, attitude, and angular velocity, along with its shape, with the measurements obtained by a camera and a distance sensor mounted on board a chaser which is on a relative trajectory around the target. The filter’s efficiency is proved with a simulation on a virtual target object. The results of the simulation, even though relevant to a simplified scenario, show that the estimation process is successful and can be considered a promising strategy for a correct and safe docking maneuver.
LSTM Wastewater Quality Prediction Based on Attention Mechanism

In order to solve the problem that some key water quality pa-rameters in the wastewater treatment process cannot be obtained in real time, a long and short-term memory network water qual-ity prediction model (SSAA-LSTM) based on SSA and attention mechanism is proposed. The model takes historical data as in-put, constructs models to learn the internal dynamic change law of features, introduces the attention mechanism, assigns different weights to the hidden state of the long and short-term memory network (LSTM) by mapping weighting and learning parame-ter matrix, and uses the sparrow search optimization algorithm (SSA) to achieve the optimal selection of hyperparameters for this model. The high-latitude feature vectors are prone to the dimensional disaster problem, so the SSAA-LSTM model with principal component analysis(PCA-LSTM) is further proposed to reduce the dimensionality of the original data. Finally, the SSAA-LSTM model without principal component analysis (NPCA-LSTM) and the PCA-LSTM model are applied in wastewater quality prediction, and the results show that the PCA-LSTM model has the higher predictive ability.
Statistica Sinica Preprint No : SS-2015-0435 R 3 Title Subsampling

In the statistical inference for long range dependent time series the shape of the limit distribution typically depends on unknown parameters. Therefore, we propose to use subsampling. We show the validity of subsampling for general statistics and long range dependent subordinated Gaussian processes which satisfy mild regularity conditions. We apply our method to a self-normalized change-point test statistic so that we can test for structural breaks in long range dependent time series without having to estimate any nuisance parameter. The finite sample properties are investigated in a simulation study. We analyze three data sets and compare our results to the conclusions of other authors.
Supervised Self Organizing Maps for Exploratory Data Analysis of Running Waters Based on Physicochemical Parameters: a Case Study in Chiang Mai, Thailand

This report demonstrated the use of a supervised self organizing map (SOM) for exploratory analysis of running waters based on their chemical criteria. Water samples from 10 different sites, representing 4 different water types – streams, a river, an irrigation canal and a sewage canal – were collected from some areas in Chiang Mai, Thailand, during 8 month period from May to December and analyzed for 16 physicochemical parameters. The samples were categorised into 8 classes (the 8 months from May to December) and 10 classes (the 10 sampling sites). This information was incorporated into the modeling using a supervised SOM methodology. The results were visualized using supervised colour shading and a unified distance matrix (U-matrix). The supervised SOM improved the correlation among the samples within group. It was possible to reveal the water sample clusters, either when organized according to the sampling times or sites. Moreover, all of the variation could be used for the analysis, eliminating the need to choose the specific dimensions or the number of principal components (PCs).
Syddansk Universitet Efficacy , adverse events , and inter-drug comparison of mepolizumab and reslizumab anti-IL-5 treatments of severe asthma-a systematic review and meta-analysis

Background: New, complex, and expensive therapies targeting Interleukin-5 (IL-5) to treat severe eosinophilic asthma are emerging. Objective: To assess efficacy, adverse events, and inter-drug comparison of mepolizumab and reslizumab for treating severe eosinophilic asthma. Design: A systematic review and meta-analysis on randomized, placebo-controlled, clinical trials elucidating two critical (exacerbation rate and oral corticosteroid (OCS) use) and six important clinical outcomes on the efficacy and safety of mepolizumab and reslizumab. Results: Five studies (N = 2197) contributed with data for exacerbation rate, showing a reduction of 53% (95% CI 46; 59) in favour of anti-IL-5, corresponding to –0.94 annual exacerbations (95% CI –1.08;–0.82), thus exceeding the predefined minimal clinical important difference (MCID) of 25% reduction of the estimated ≥2 annual exacerbations. Quality of evidence was considered moderate, with low heterogeneity in study findings (I2 = 0%). One study (N = 135) contributed with data on percentage of patients experiencing ≥50% reduction inoral corticosteroid treatment, showing an effect of 20% (95% CI 2.3;47) in favour of anti-IL-5 treatment (mepolizumab), thus exceeding the predefined MCID of 10%. Quality of evidence was considered low. Compared to placebo, anti-IL-5 showed significant improvements in lung function, asthma control, and asthma-related quality of life, but below the MCIDs. No differences were observed for serious adverse events and number of patients, who dropped out. No studies evaluating sickleave or head-to-head comparisons were identified. By indirect comparison, we found no significant difference between mepolizumab and reslizumab in any ofthe predefined clinical outcomes. OCS treatment reduction could not be compared due to lack of reslizumab studies investigating this outcome. Conclusions: Mepolizumab and reslizumab provide significant and clinically relevant improvements in exacerbation rate and OCS reduction. Indirect, inter-study comparisons revealed no differences between the anti-IL-5 drugs in efficacy or safety measures. ARTICLE HISTORY Received 16 May 2018 Revised 28 August 2018 Accepted 6 October 2018
Polarization Reconfigurable and Beam-Switchable Array Antenna Using Switchable Feed Network

A novel array antenna with reconfigurable polarizations and radiation pattern agility is presented in this paper. The digital-controllable array antenna has the functional capabilities of reconfigurable polarization and switching beam, which consists of cavity-backed antenna element based on substrate integrated waveguide (SIW) technique, parasitic patch array, power dividers and phase shifters. Polarization modes and radiation patterns can be dynamically controlled by means of Microcontroller Unit (MCU) with the different feeding ports and phase difference. To validate this proposal, the performance of the fabricated device was measured at the operating frequency of 5.4 GHz. Measured results indicate that the 10 dB impedance bandwidths and 3 dB axial ratio (AR) bandwidths cover from 5.25 to 5.6 GHz, and the measured gains range from 7.9 to 10.1 dBic. The experimentation and simulation verify that the proposed antenna can achieve two polarization states and five radiation beams of each polarization state, which indicate a good performance of the polarization reconfigurable and beam switching antenna array.
A Greedy Algorithm for Generating Malware Signatures Based on Association

Malware signature should be compact to reduce the possibility of being bypassed. However, previous studies combine unnecessary features because they cannot distinguish associated features. This study proposes an algorithm for generating effective and compact signatures, taking into account the association of features. The proposed algorithm is based on a greedy approach that chooses a feature with the lowest association with the currently combined features. As a result of conducting a family detection experiment on 4,443 samples consisting of 869 families, it was confirmed that the signature size was about 90 times smaller and the performance was 2.8% higher than in the existing method.
The success of fast reaction: A discrete reaction-diffusion model

We discuss the dynamics of a system of 2n ordinary differential equations that can be looked at as the discrete version of a system of two reaction-diffusion equations, which differ only in their sensitivity to the reaction term. Such reaction-diffusion systems occur in evolutionary models from biology. It is known that only the fastest reacting species survives in generic situations. We prove similar results for the related discrete system and give an interpretation of the results in terms of mathematical finance.
Solving Sdp's in Non-Commutative Algebras Part I: The Dual-Scaling Algorithm

Semidefinite programming (SDP) may be viewed as an extension of linear programming (LP), and most interior point methods (IPM s) for LP can be extended to solve SDP problems.However, it is far more difficult to exploit data structures (especially sparsity) in the SDP case.In this paper we will look at the data structure where the SDP data matrices lie in a low dimensional matrix algebra.This data structure occurs in several applications, including the lower bounding of the stability number in certain graphs and the crossing number in complete bipartite graphs.We will show that one can reduce the linear algebra involved in an iteration of an IPM to involve matrices of the size of the dimension of the matrix algebra only.In other words, the original sizes of the data matrices do not appear in the computational complexity bound.In particular, we will work out the details for the dual scaling algorithm, since a dual method is most suitable for the types of applications we have in mind.
Development of Corporate Social Responsibility Metrics Based on the Theory of Stakeholders

The article provides an overview of approaches to fixing the achievements of companies in the field of corporate social responsibility, based on international standards. A defining performance indicators are proposed for companies in the field of social responsibility in the interests of key stakeholders. Author formulates the principles of formation of social metrics as a tool for coordination purposes and advances the interests of its major stakeholder groups. The practice of Ukrainian companies in corporate social responsibility are shown. Advantages and disadvantages of statements of Ukrainian companies in the context of the requests of main stakeholders. Perspective directions of development of management accounting and general management in the corporate social responsibility of companies in Ukraine is proposed.
A High-Frequency Motion Error Compensation Algorithm based on Multiple Errors Separation in BiSAR onboard Mini-UAVs

In this paper, a high-frequency motion error compensation algorithm for mini-Unmanned Aerial Vehicle (UAV) based Bistatic Synthetic Aperture Radar (BiSAR) system is proposed. Compared with traditional SAR system platform, the mini-UAV is more vulnerable to interference from the external environment. Thus, the motion error of mini-UAV based BiSAR systems always contains both low-frequency and high-frequency components. Meanwhile, the independent transmitter and receiver sources would introduce more than one high-frequency motion error component, and the variance of the high-frequency motion error also needs to be considered. This paper first establishes a high-frequency motion error model and the corresponding Doppler phase signal for the mini-UAV based BiSAR system. Then, multiple forms and multiple components errors are separated and estimated on the azimuth phase. Next, the complex spatial variance is accurately modeled and compensated by estimating the platforms’ motion errors. It is suggested from the simulation and experimental results that the proposed algorithm can compensate for the motion error with complex forms under the mini-UAV based BiSAR system condition.
Improving Problem-based Learning in Creative Communities Through Effective Group Evaluation

In this case study, we researched one cohort from the Center for Animation, a higher education teaching environment that has successfully fostered group creativity and learning outcomes through problem-based learning. Through live and videotaped observations of the interactions of this community over 18 months, in addition to focused interviews with nine key community leaders, we considered the evaluative culture and actions of this community, and how these evaluative practices improved their creative problem solving. We describe their evaluation practices in the context of principles derived from the Joint Committees’ Evaluation Standards (Yarbrough, Shulha, Hopson, & Caruthers, 2011), which are well-respected standards used by professional evaluators. Specifically, we found that problem-based learning strategies were successful in part because the community members (1) established a context and culture of high expectations, collaboration, and evaluation; (2) united the students, teachers, and industry leaders as shared stakeholders in the success of the project; (3) identified early the key criteria for evaluating progress; and (4) asked questions to evaluate progress towards meeting the criteria, using many approaches to gathering information. We discuss the implications for applying these principles to other problem-based learning environments, particularly in higher education, as well as future research.
Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Visual Learning for a Mid Level Pattern Discrimination Task Permalink

Our goal was to examine the plasticity of the human visual system at mid to high levels of visual processing. It is well understood that early stages of visual processing contain cells tuned for spatial frequency and orientation. However images of real-world objects contain a wide range of spatial frequencies and orientations. We were interested in how different spatial frequencies and orientations are combined. We used a pattern discrimination task observers were asked to discriminate small changes in a “wickerlike” stimulus consisting of six superimposed sinusoidal gratings. Observers were asked to discriminate a 15% spatial frequency shift in two of these sinusoidal components, which were masked by four noise components. We found large amounts of perceptual learning for this task – over eight sessions of training observers’ average percent correct increased by 31%, corresponding to their thresholds dropping to a third of their initial values. Further experiments suggest that learning was based on changes within a mid level stage of processing intermediate between lowlevel analyzers tuned for orientation and spatial frequency and high-level pattern matching or object tuned cells. This mid level stage seems to be “very roughly Fourier” and combines information from individual gratings using probability summation. This stage of processing is also remarkably plastic compared to earlier stages of processing.
A review of atomic layer deposition modelling and simulation methodologies: Density functional theory and molecular dynamics

Abstract The use of computational modelling and simulation methodologies has grown in recent years as researchers try to understand the atomic layer deposition (ALD) process and create new microstructures and nanostructures. This review article explains and simplifies two simulation methodologies, molecular dynamics and the density functional theory (DFT), in solving atomic layer deposition problems computationally. We believe that these simulation methodologies are powerful tools that can be utilised in atomic layer deposition. DFT is used to solve problems in surface science and catalysis (predicting surface energy, adsorption energy, charge transfer, etc.), semiconductors (band structure, defect bands, band gap, etc.), superconductors (electron–phonon coupling, critical transition temperature), and molecular electronics (conductance, current–voltage characteristics). Molecular dynamics (MD) is used to predict the kinetic and thermodynamic properties of a material. Of interest in this article is a review where different material problems emanating from atomic layer deposition from these fields have been addressed by DFT and MD. Selected publications are discussed where DFT and MD have been successfully applied in atomic layer deposition (and related processes in some instances). The applications of DFT stretch from binding energy calculations of molecules and the solid band structure in chemistry and physics, respectively, computing the electron density up to determining the properties of a many-electron system. Also highlighted in this review study are the challenges that DFT and MD simulations must overcome.