Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPUTATIONAL LOAD DISTRIBUTION METHOD IN A PRIVATE CLOUD OF A MOBILE APPLICATION OF AN INTEGRATED ENTERPRISE INFORMATION SYSTEM

The use of cluster technology in a private cloud of a mobile application of an integrated enterprise information system requires the measures ensuring the reliable operation of its hardware component to be developed. The solution of this problem involves the use of effective mechanisms for maintaining high availability of a private cloud of a mobile application as well as the solutions to ensure the failover operation of server and client components. This paper describes the method of load distribution among computer network nodes in a private cloud of a mobile application integrated in a corporate information system; this proposed algorithm is based on the load prediction of the computer system, it performs the preliminary resources reservation required to perform a given task, initiated by user request. Load prediction is performed by the decomposition analysis of the history of running user tasks, which is a preliminary allocation of recurring task patterns with a certain set of parameters and with further prediction of occurrence of these problems in queue. The proposed method greatly enhances the productivity of a mobile application; it expands the set of the functions performed by the remote client by request and ensures the integration of several information systems on the basis of a single private cloud.
Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.
IN THE YEAR OF 1942 PRAMOEDYA ANANTA TOER’S NOVEL REPRESENTASI POSKOLONIAL MASA PENJAJAHAN JEPANG

-colonial forms which represented by the figures in Perburuan. This novel portrays about a character colonialism together with his two friends, Dipo and ed with post-colonial theory. Type of this research is -colonial approach. Researchers gathered the have been found. These three formulations include -doubt (ambivalence). The technique used in on the data, and data analysis. Data analysis technique ayal, and characteristic’s self-doubts -colonial representations contained in Perburuan. -colonial analysis, novel
An RF-based Hybrid Approach to Indoor Localization

Radio Frequency technologies are getting more and more attention as they are attractive solutions to many application domains. Accordingly, RF-based indoor localization has become a very hot research topic currently. As we know, there are several major challenges in RF-based indoor localization domain, such as radio signal multi-path problem, absorption effect of radio signal, etc. A few traditional indoor positioning approaches (for example, the classic LANDMRAC approach) have obtained achievements on this domain. In particular, they have enlightened later researchers very much, and provided many promising research directions. However, the traditional RF-based indoor positioning approaches suffer from several major drawbacks. In this thesis, we propose two novel RF-based indoor localization schemes to attempt to overcome the existing drawbacks. The proposed SVR-LANDMRC approach is our first interesting attempt, which can guarantee more stable location sensing results based on a same infrastructure to LANDMARC. Despite the stableness of the SVR-LANDMRC approach is good, its degree of prediction accuracy is similar to existing LANDMARC approach. The key idea of this thesis is the proposed SA-LANDMARC approach, which is an RF-based hybrid indoor localization mechanism. The SA-LANDMARC approach is based on active RFID equipments and Wireless Sensor Networks equipments jointly. This scheme uses wireless sensor nodes to implement effective target-clustering process to appoint a subset of all reference tags as candidates. Then the final location is estimated by radio signal relationship between the target tag and candidate reference tags. With a similar hardware cost to LANDMARC, the SA-LANDMARC approach can provide a remarkable high degree of localization accuracy. Our experimental results show that, under our system setup, the accuracy of SA-LANDMARC approach can reach to about 0.7m.
Eliciting Innovation Adoption Behaviour through Self-report and Peer-prediction Methods

First, passive innovation resistance (PIR) is the tendency to resist new innovations before evaluating its potential. In this thesis PIR is measured with a 18 item scale of self-report survey questions conceptualized by previous researchers. Second, innate consumer innovativeness (ICI) reflects a person’s predisposition and cognitive style towards innovation. ICI is measured by 6 survey questions asking people to predict the behaviour of their peers. Peer-prediction has been shown to reveal personal traits and preferences. Subsequently, the answers of each survey separately were used to classify people between early adopters and late adopters. Finally, I tested whether these classifications helped to explain respondent’s willingness to pay (WTP) for an actual innovation. The WTP was elicited by the use of the Becker-DeGroot-Marschak method. Being classified as an early adopter based on the measures of PIR marginally significantly influenced to WTP to increase. In contrast, the classification of respondents between early and late adopters using the peer-prediction measures was not significantly associated with the WTP.
A Wavelet Analysis of Price Integration in Major Agricultural Markets

Abstract: We use linear time series and wavelets approach to study the relationships between U.S. and international prices for corn, soybeans, and cotton. We then compare results obtained with each approach and verify that structural breaks discovered with wavelet analysis match those produced with subsequent partial-period cointegration analysis. We find little evidence that short-term fluctuations between domestic and international prices are stable, while long-term relationships for many price pairs experience distinct structural breaks. We further find that even though China is among the largest importers of U.S. agricultural products, its commodity prices share little or no relationship with those prevailing in U.S. markets.
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage

Models of user behavior are critical in many decision making problems and can be viewed as decision rules that transform state information (in set S) available to the user to actions (in set A). Formally, a user model is a function f : S 7→ A. Gaussian processes (GPs) employed to learn functions on the action/target space (henceforth target GPs or TGPs for short) can thus be used to place a prior on user models and identify a posterior distribution over them supported by data in conjunction with approximate Bayesian inference techniques (Blei et al., 2017; Beaumont, 2019). TGPs for user modeling would assume that user actions at a given set of finite states follow a multivariate Gaussian. To capture non-Gaussian action distributions, one could apply GPs to learn functions in a transformed space that is not the target. Examples include warped and chained GPs proposed in Snelson et al. (2004) and Saul et al. (2016), respectively. Extending this literature, we study the application of GPs in a transformed space defined by decision rules. Such rules are known in several applications and depend on functions themselves. Specifically, a user model based on a decision rule takes the form g : ΠkPk × S → A, where the arguments are obtained using functions hk : S 7→ Pk, k = {1, . ,K} that map from S to transformed spaces Pk, possibly different from the target space A. Each such function has immediate interpretability to a practitioner, and we model them using GPs. We refer to such a user model {g, h1, . , hk} as a decision-rule GP (DRGP). To make the notion of DRGPs concrete in this short article, we focus on the problem faced by a firm providing services to store ethanol – a real application that motivated this work. Suppose capacity (in gallons) is sold via annual contracts to N users. The contract of user n specifies the maximum amount of ethanol that can be stored, denoted by Cn. User behavior corresponds to the injection of ethanol and the withdrawal of previously injected ethanol, which can be modeled as a time series. The inventory In,t in storage associated with user n at time t is the net of past injections and withdrawals. A TGP approach would employ a GP to determine the next-period storage inventory level function In,t+1 directly. In contrast, we propose a DRGP that leverages a well-known decision rule based on injection and withdrawal threshold functions (Charnes et al., 1966; Secomandi, 2010). These threshold functions are learned as GPs instead of the (relatively less interpretable) inventory function. We focus on the following research questions in the context of the ethanol storage application: (Q1) Can existing exact and approximate Bayesian inference techniques be used for inference with DRGP? and (Q2) How does DRGP perform relative to TGP? We answer these questions by executing numerical experiments based on real data of aggregated ethanol storage injection and withdrawals. For Q1, we show that sparse vari-
University of Southern Denmark Cortical Marrow Ratio in Plain X-rays of Femoral Neck Fractures

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A Compact Micro-Bolometer Array for Mid-Infrared Laser Beam Alignment, Diagnostics and Spot-Size Measurement

Knowledge of a lasers beam’s profile throughout a laser system and experiment can help immensely in diagnosing laser problems and assisting in beam alignment and focusing at a sample. Obtaining such profiles is a trivial task in the ultraviolet-visible wavelength range but more challenging with near-infrared to infrared beams. Scientific grade bolometer arrays, suitable for such a task, do exist but are extremely costly, relatively large and have a large pixel size, of the order of 80 μm, which is adequate for profiling larger beams but poses an issue when trying to profile sub 100 μm beams for example at a focal point. This communication identifies a micro-bolometer array for nearto mid-infrared laser beam profiling, which is extremely low cost. In addition, the device is very compact, enabling use in confined spaces, and has a small, 12 μm, pixel size permitting the profiling of focused laser beams. The best scientific grade device identified has a pixel size of 17 μm. This device is a powerful tool for infrared laser spectroscopists, reducing the time required to measure the spot size of beams and to achieve spatial overlap of multiple infrared beams as used in twodimensional infrared spectroscopy, saving many hours of setup time. The use of the bolometer array as a spectrographic detector and probe of long-term beam drifts is also demonstrated.
When support is important: A study of leaders with a fixed digital mindset and their employees' active usage or avoidance towards new technology – the mediating effect of perceived developmental supervisor support

iv 1.0 Introduction . 1 1.1 Research Question and Conceptual Model . 2 Figure 1. Conceptual Model . 4 1.2 Intended Contribution of the Research. 4 2.0 Theoretical Background and Hypotheses . 5 2.1 Mindsets and Implicit Person Theories . 5 2.1.1 Fixed Mindset . 6 2.1.2 Growth Mindset . 6 2.2 Digital Mindset . 7 2.2.1 Digital Mindset and Change Readiness . 7 2.3 Employees’ Active Usage or Avoidance of New Technology (A Job Crafting Perspective) . 8 2.4 Hypotheses Relating Leader’s FDM to Employee’s Active Usage or Avoidance of New Technology . 10 2.5 Hypotheses Related to the Mediating Role of PDSS . 12 2.6 Digital Mindset Intervention: Helping Leaders Go from Fixed to Growth Digital Mindset 15 3.0 Research Method . 17 3.1 Sample . 17 3.2 Procedure . 17 3.3 Research Design . 18 3.4 Measures . 19 3.4. 1 Independent Variable: Leader’s FDM . 20 3.4. 2 Dependent Variable: Employees’ Active Usage or Avoidance to New Technology . 20 3.4. 3 Mediating Variable: PDSS . 21 3.5 Intervention . 21 4.0 Analysis . 22 5.0 Results . 24 5.1 Principal Component Analysis . 24 1010468 0895187 GRA 19703
INDIVIDUALS WITH HIGH AUTISTIC TRAITS FOCUS EXPLICITLY ON BODY PARTS WHEN TRANSFORMING VISUAL PERSPECTIVES

Individuals with autism spectrum disorders are known to have difficulties with visual perspective taking. This study used a left–right discrimination task to examine whether autistic traits in typically developing individuals influence visual perspective taking. In each trial, an avatar displaying one of three postures (front, back, and front with arms crossed) was displayed on a PC monitor. For each trial, the direction (left or right) and reference (subjective, objective, or others’ hand) were instructed and participants had to identify the correct side on the display. In trials with an objective reference, individuals with lower levels of autistic traits could easily project themselves onto the back view of the avatar. Individuals with higher levels of several autistic traits (e.g., attention to local detail, imagination difficulty) did not use this advantage, tending to focus body parts of the avatar as cues to discriminate directions.
Expectation vs. Reality: Attitudes Towards a Socially Assistive Robot in Cardiac Rehabilitation

Currently, Social Assistive Robotics (SAR) is widely explored in different areas and scenarios. In cardiac rehabilitation, SAR has been recently implemented as a tool to improve the quality of the procedures and support patients to boost their performance. As cardiac rehabilitation comprises numerous sessions, such systems must guarantee to be effective in the long term. Therefore, to achieve this goal, it is important to understand how users, namely patients and clinicians who mostly know the needs and the therapy environment, perceive this technology. In this context, this paper presents the assessment of the attitudes towards a social robot in order to evaluate the expectation of potential new users, and perception of users who interacted with the social robot during a period of 18 weeks performing cardiac rehabilitation. A total of 43 participants (28 patients and 15 clinicians) were included in the study, and acceptance and perception factors were evaluated through a modified UTAUT questionnaire model and open discussion sessions. Results show that 75% of patients have positive thoughts regarding the usefulness, utility, safety, and trust perceived of a social robot, and 80% of clinicians consider that the robot is a useful tool for cardiac rehabilitation. Similarly, a more positive perception was noticed after the users interacted with the robot. Furthermore, this perception study allows the enhancement of the social model of interaction in the future, aiming to provide a more natural interaction trough personalized features, increasing social abilities and engagement of the users during the therapy.
ePubWU Institutional Repository Sylvia Frühwirth-Schnatter and Gertraud Malsiner-Walli From here to infinity: sparse finite versus Dirichlet process mixtures in model- based clustering

In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303–324, 2016) are sparse finite mixtures, where the prior distribution on the weight distribution of a mixture with K components is chosen in such a way that a priori the number of clusters in the data is random and is allowed to be smaller than K with high probability. The number of clusters is then inferred a posteriori from the data. The present paper makes the following contributions in the context of sparse finite mixture modelling. First, it is illustrated that the concept of sparse finite mixture is very generic and easily extended to cluster various types of non-Gaussian data, in particular discrete data and continuous multivariate data arising from nonGaussian clusters. Second, sparse finite mixtures are compared to Dirichlet process mixtures with respect to their ability to identify the number of clusters. For both model classes, a random hyper prior is considered for the parameters determining the weight distribution. By suitable matching of these priors, it is shown that the choice of this hyper prior is far more influential on the cluster solution than whether a sparse finite mixture or a Dirichlet process mixture is taken into consideration.
Technological Challenges of Phosphorus Removal in High-Phosphorus Ores: Sustainability Implications and Possibilities for Greener Ore Processing

With the present rates of iron ore consumption, currently unusable, high-phosphorus iron ore deposits are likely to be the iron ores of the future as higher-grade iron ore reserves are depleted. Consequently, the design and timely development of environmentally-benign processes for the simultaneous beneficiation of high-phosphorus iron ores and phosphorus recovery, currently a technological challenge, might soon become a sustainability challenge. To stimulate interest in this area, phosphorus adsorption and association in iron oxides/hydroxyoxides, and current efforts at its removal, have been reviewed. The important properties of the most relevant crystalline phosphate phases in iron ores are highlighted, and insights provided on plausible routes for the development of sustainable phosphorus recovery solutions from high-phosphorus iron ores. Leveraging literature information from geochemical investigations into phosphorus distribution, speciation, and mobility in various natural systems, key knowledge gaps that are vital for the development of sustainable phosphorus removal/recovery strategies and important factors (white spaces) not yet adequately taken into consideration in current phosphorus removal/recovery solutions are highlighted, and the need for their integration in the development of future phosphorus removal/recovery solutions, as well as their plausible impacts on phosphorus removal/recovery, are put into perspective.
Estimating the protection provided by islands against anti-ship missiles

The purpose of this thesis is to develop a method for estimating the protection provided by islands against anti-ship missiles. These missiles pose a threat to the safety of the military equipment and personnel on board navies’ ships operating in coastal areas. Especially during a resupply mission, ships are susceptible to missile strikes as they are almost immobilized. Thus, finding a reliable way of estimating the protection islands provide for ships can decrease casualties during war time. The method merges the information of the possible locations of the ships to elevation data and calculates the protections for these locations and the routes between them. At the start of this thesis, the objective and the limitations of the method are defined. After this, parameters are selected and the method is implemented. The method is then tested and its results are validated using locations and elevation data from within the Finnish Archipelago Sea. Sensitivity analyses are performed to examine how the parameter values affect the processing time and the computational results. The method appears to be an accurate and a computationally efficient way for finding the safest location for a ship to be in a coastal area. The method can also estimate the protections between the locations, making it a viable way of determining a safe route to the resupply location. The method is implemented so that it is easy to modify the parameters for various situations where different ships and missiles are used. Thus, the method can be used for not only the area presented in this thesis, but also for other coastal areas.
COMPARISON OF FIVE CLASSIFIERS FOR CLASSIFICATION OF SYLLABLES SOUND USING TIME-FREQUENCY FEATURES

In a speech recognition and classification system, the step of determining the suitable and reliable classifier is essential in order to obtain optimal classification result. This paper presents Indonesian syllables sound classification by a C4.5 decision tree, a Naive Bayes classifier, a Sequential Minimal Optimization (SMO) algorithm, a Random Forest decision tree, and a Multi-Layer Perceptron (MLP) for classifying twelve classes of syllables. This research applies five different features set, those are combination features of Discrete Wavelet Transform (DWT) with statistical denoted as WS, the Renyi Entropy (RE) features, the combination of Autoregressive Power Spectral Density (AR-PSD) and Statistical denoted as PSDS, the combination of PSDS and the selected features of RE by using Correlation-Based Feature Selection (CFS) denoted as RPSDS, and the combination of DWT, RE, and AR-PSD denoted as WRPSDS. The results show that the classifier of MLP has the highest performance when it is combined with WRPSDS.
Advances in Hemodynamic Analysis in Cardiovascular Diseases Investigation of Energetic Characteristics of Adult and Pediatric Sputnik Left Ventricular Assist Devices during Mock Circulation Support

The need to simulate the operating conditions of the human body is a key factor in every study and engineering process of a bioengineering device developed for implantation. In the present paper, we describe in detail the interaction between the left ventricle (LV) and our Sputnik left ventricular assist devices (LVADs). This research aims to evaluate the influence of different rotary blood pumps (RBPs) on the LV depending on the degree of heart failure (HF), in order to investigate energetic characteristics of the LV-LVAD interaction and to estimate main parameters of left ventricular unloading. We investigate energetic characteristics of adult Sputnik 1 and Sputnik 2 LVADs connected to a hybrid adult mock circulation (HAMC) and also for the Sputnik pediatric rotary blood pump (PRBP) connected to a pediatric mock circulation (PMC). A major improvement of the LV unloading is observed during all simulations for each particular heart failure state when connected to the LVAD, with sequential pump speed increased within 5000–10000 rpm for adult LVADs and 6000–13000 rpm for PRBP with 200 rpm step. Additionally, it was found that depending on the degree of heart failure, LVADs influence the LV in different ways and a significant support level cannot be achieved without the aortic valve closure. Furthermore, this study expands the information on LV-LVAD interaction, which leads to the optimization of the RBP speed rate control in clinics for adult and pediatric patients suffering from heart failure. Finally, we show that the implementation of control algorithms using the modulation of the RBP speed in order to open the aortic valve and unload the LV more efficiently is necessary and will be content of further research.