Miracle-dz/UniFAR-FTUs huggingface dataset:
Unlimited Power The New Science Of Personal Achievement

Thank you very much for reading unlimited power the new science of personal achievement. As you may know, people have search numerous times for their favorite novels like this unlimited power the new science of personal achievement, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their laptop.
Control Cloud Data Access With Attribute Based Encryption In Cloud Computing

Cloud computing is a computing concepts, which enables when required and low maintenance usage of resources, but the data is shares to some cloud servers and various privacy related concerns emerge from it. Various schemes like based on the attribute-based encryption have been developed to secure the cloud storage. Most work looking at the data privacy and the access control, while less attention is given to the privilege control and the privacy. In this paper, we present a privilege control scheme Anonymity Control to address and the user identity privacy in existing access control. Anonymity Control decentralizes the central authority to limit the identity leakage and thus achieves partial anonymity. It also generates the file access control to the privilege control, by which privileges of all operations on the cloud data can be managed in a proper manner. We present the Anonymity Control-F, which prevents the identity and achieve the anonymity. Our security analysis shows that both Anonymity Control and Anonymity Control-F are secure under the Diffie–Hellman assumption and our performance evaluation exhibits the feasibility of our schemes.
Scalable Graph Algorithms in a High-Level Language Using Primitives Inspired by Linear Algebra

Author(s): Lugowski, Adam | Advisor(s): Gilbert, John R | Abstract: This dissertation advances the state of the art for scalable high-performance graph analytics and data mining using the language of linear algebra. Many graph computations suffer poor scalability due to their irregular nature and low operational intensity. A small but powerful set of linear algebra primitives that specifically target graph and data mining applications can expose sufficient coarse-grained parallelism to scale to thousands of processors.In this dissertation we advance existing distributed memory approaches in two important ways. First, we observe that data scientists and domain experts know their analysis and mining problems well, but suffer from little HPC experience. We describe a system that presents the user with a clean API in a high-level language that scales from a laptop to a supercomputer with thousands of cores. We utilize a Domain-Specific Embedded Language with Selective Just-In-Time Specialization to ensure a negligible performance impact over the original distributed memory low-level code. The high-level language enables ease of use, rapid prototyping, and additional features such as on-the-fly filtering, runtime-defined objects, and exposure to a large set of third-party visualization packages.The second important advance is a new sparse matrix data structure and set of algorithms. We note that shared memory machines are dominant both in stand-alone form and as nodes in distributed memory clusters. This thesis offers the design of a new sparse-matrix data structure and set of parallel algorithms, a reusable implementation in shared memory, and a performance evaluation that shows significant speed and memory usage improvements over competing packages. Our method also offers features such as in-memory compression, a low-cost transpose, and chained primitives that do not materialize the entire intermediate result at any one time. We focus on a scalable, generalized, sparse matrix-matrix multiplication algorithm. This primitive is used extensively in many graph algorithms such as betweenness centrality, graph clustering, graph contraction, and subgraph extraction.
A lightweight Framework for Improving the Mobile Applications Performance

In Recent years, the smartphones have exploded in popularity and set to become the fastest spreading technology. These devices have wide range of capabilities like more processing, more storage, GPS, Wi-Fi, camera, and others. As a result, the developer trends to build a complex application such as augmented reality, face detection, image processing and speech recognition. Although the capabilities that smartphones support, these applications require more processing and consume more battery. Therefore, the researchers trend to solve this problem by using rich resources such as cloud computing to support the mobile devices for execution these application. Where the mobile device offload these applications or part of it to be executed remotely on the cloud. This paper will introduce a lightweight framework to improve the performance of mobile applications and save the battery consumption. This framework have a core module which so-called dynamic offloader. This module determines dynamically at run time the decision for the offloading process based on the current environment. The experimental results are applied on Gaussian blur filter application and this results proved that this lightweight framework improves the performance and saves energy of mobile applications. Keywords—Smartphones; Android; Computation Offloading; Mobile Cloud Computing; Battery Consumption;Lightwight Framework;
Parameters of the Carslaw-Jaeger equation describing the temperature distribution in the ground

Abstract The ground temperature changes with depth and time. Time variability is considered as a harmonic function. The equation describing changes of the ground temperature contains four parameters: the average annual temperature of the surface of the ground, the annual amplitude of the temperature of the ground surface as well as the phase angle of the temperature and thermal diffusivity of the ground. Based on the results of the measurements presented in the literature, the parameters of the equation using the combined method on the basis of linear regression, described in the literature, were determined. This method, however, leads to an ambiguous value of the thermal diffusivity. It was found that the nonlinear regression method gives much better results, leading to obtaining precise and unambiguous values of all parameters of the equation.
A result-driven minimum blocking method for PageRank parallel computing

Matrix blocking is a common method for improving computational efficiency of PageRank, but the blocking rules are hard to be determined, and the following calculation is complicated. In tackling these problems, we propose a minimum blocking method driven by result needs to accomplish a parallel implementation of PageRank algorithm. The minimum blocking just stores the element which is necessary for the result matrix. In return, the following calculation becomes simple and the consumption of the I/O transmission is cut down. We do experiments on several matrixes of different data size and different sparsity degree. The results show that the proposed method has better computational efficiency than traditional blocking methods.
Optimizing ventricular work: a matter of constraints

The analysis of aortic blood pressure and flow represent an important tool to predict cardiovascular risk. A Windkessel model relating pressure and flow, together with an optimal performance criterion of left-ventricular work, is used to generate the aortic flow pattern for a given central pressure curve. For the corresponding optimization problem, different physiologically relevant constraints are specified, but due to the limited number of degrees of freedom not all of them can be included at once. The optimization problem is solved with every possible combination of constraints and the resulting flow and pressure curves are analyzed. These waveforms show that the choice of constraints strongly affects the accuracy of the generated curves. Constraining aortic flow during diastole appears to be the best choice, but a physiologically shaped flow and pressure pattern cannot be achieved simultaneously with the applied objective function and constraints.
Think-aloud protocol analysis as a measure of students' science learning through design assessment

Research has indicated that authentic engineering design tasks hold the attention and interest of students and can lead to deeper levels of science engagement (Fortus, Dershimer, Krajcik, Marx, & Mamlok-Naaman, 2004; Roth, 1996; 1997; 1998). However, there is a need for student assessments that can measure how students recall and possibly apply scientific concepts in the context engineering design-based tasks. Furthermore, there is a need for assessments that can identify students’ transfer of learning from classroom to real-world science and engineering. One promising assessment methodology is the use of the concurrent think-aloud (CTA) protocol analysis (Atman & Bursic, 1998; Ericsson & Simon, 1993) to assess students’ transfer of learning from classroom engineering design-based experiences. Although this assessment process is limited to small purposeful sample sizes, the technique allows researchers to observe in real time triads of students engaged in an authentic design task and identify which science concepts naturally emerge in the design team’s dialogue. Using a coding process (Halfin, 1973), researchers in this study have successfully piloted a coding scheme for different cognitive strategies exhibited by elementary school students and furthermore, have systematically identified gaps and areas of overemphasis of the design process. Results were organized using frequency and percentages for time on code. Preliminary analysis of data collected from pilot studies indicated limited use of science terms within design thinking dialogue; however the science terms that did emerge were used accurately and appropriately. Implications of the study reveal that CTAs provide significant insight into students’ design and inquiry thinking and if triangulated with other student measures, CTAs can enhance case study research. Introduction The current trend in K-12 science education is to infuse engineering practices as effective approaches for enhancing science education. More recently, engineering standards have become an integral part of the newly proposed Conceptual Frameworks for New Science Education Standards (NRC, 2012). This is compounded by newly adopted state-level standards that require students to engage in the engineering design process and/or explore the nature of technology and engineering practices. Underpinning these initiatives is the call for students to ask questions, define problems, develop and use models, plan and carry out design tasks, and use evidence
Deep learning methods to guide CT image reconstruction and reduce metal artifacts

The rapidly-rising field of machine learning, including deep learning, has inspired applications across many disciplines. In medical imaging, deep learning has been primarily used for image processing and analysis. In this paper, we integrate a convolutional neural network (CNN) into the computed tomography (CT) image reconstruction process. Our first task is to monitor the quality of CT images during iterative reconstruction and decide when to stop the process according to an intelligent numerical observer instead of using a traditional stopping rule, such as a fixed error threshold or a maximum number of iterations. After training on ground truth images, the CNN was successful in guiding an iterative reconstruction process to yield high-quality images. Our second task is to improve a sinogram to correct for artifacts caused by metal objects. A large number of interpolation and normalization-based schemes were introduced for metal artifact reduction (MAR) over the past four decades. The NMAR algorithm is considered a state-of-the-art method, although residual errors often remain in the reconstructed images, especially in cases of multiple metal objects. Here we merge NMAR with deep learning in the projection domain to achieve additional correction in critical image regions. Our results indicate that deep learning can be a viable tool to address CT reconstruction challenges.
A Hybrid Gesture Recognition Method for American Sign Language

Gesture based communication is a method of correspondence between the ordinary and hard of hearing people in which the vision based procedure is utilized. This paper proposes a novel methodology of hand gesture recognition system for American Sign Language (ASL), which will perceive communication via gestures signals in an ongoing situation. A hybrid based descriptor, which joins the benefits of LBP (Local binary pattern), SP (super pixels) and SURF (Speeded Up Robust Features) strategies, is utilized as a consolidated list of capabilities to accomplish a improved identification rate beside among a little moment in time computational difficulty. In additional increase the detection speed and create the appreciation framework strong to view-point varieties, the idea of derived features from the accessible list of capabilities is presented. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are utilized for hybrid arrangement of single marked letter. Comparative investigation of these strategies with other well known methods demonstrates that the constant proficiency and robustness are better. The performances parameters will be used in this method are accuracy, sensitivity, precision, FNR and FDR.
Biodiversity data provision and decision-making - addressing the challenges

Essential Biodiversity Variables (EBVs) are measurements required for study, reporting, and management of biodiversity change. They are being developed to support consistency, from the collection to the reporting of biodiversity data at the national, regional and global scales. However, "EBV stakeholders" need to strike a balance between 'doing innovative research' and 'having positive impact' on biodiversity management decisions. This paper reports on a workshop entitled Identifying joint pathways to address the challenges of ‡ § | ¶
Validating Value Network Business Models

Diﬀerent meta-models allow to model the business of organizations from diﬀerent perspectives. Business models, value network models and enterprise architecture models potentiate a shared understanding of information and together, they permit an alignment from business idealization to business implementation, considering that an organization is a part of a value network. However, model representation does not allow automated model analysis, which enables automated detection of inconsistencies between models and additional support for decision making. Our goal is to overcome this issue with model-based representations by integrating three meta-models, Business Model Canvas, e3value and Archimate with ontology engineering techniques, to potentiate automated analysis of models represented with description logics. A developed application uses ontology reasoning and querying to perform an automated detection of components that cancel conformity between models for a value network. The proposal is demonstrated through a scenario with Business Model Canvas, e3value and Archimate models for a value network, by executing the consistency validation application and further model analysis.
An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q -Learning and an N - Gram based system. It was found that N -Grams and Q - Learning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.
Harmonic fields based surface and volume mapping

Harmonic fields are widely used in computational science and engineering for their computational efficiency and promising properties. Radó theorem strongly supports the application of harmonic fields in parameterization by proving that harmonic maps from 2D Riemannian manifold to convex planar domain are diffeomorphism. But the limitations of harmonic maps are also obvious: first, there are a lot of real applications requiring the mapping constraints to be set inside the domain. However, in such cases, harmonic maps could not be guaranteed to be one-to-one. Second, Radó theorem holds true only in the 2D case. Volumetric harmonic maps could not be guaranteed to be a diffeomorphism. In this thesis, we systematically study the harmonic fields and their applications in surface and volume parameterization by overcoming the two aforementioned drawbacks technically. In the first part of the thesis, we present an editable polycube map framework in Chapter 4, that, given an arbitrary high-resolution polygonal mesh and a simple polycube representation plus optional sketched features indicating relevant correspondences between the two, provides a uniform, regular and artist-controllable quads-only mesh with a parameterized subdivision displacement scheme. The proposed method is based on a divide and conquer strategy. The mesh surface is divided into patches according to the feature constraints. A diffeomorphism is built between each pair of topological disk patches. After that, a global smoothing step is adopted to improve the continuity along the segmentation boundaries. In Chapter 5, we also develop another method to overcome the drawback of harmonic maps in parameterizing 3D facial expressions. With the salient features (such as the eyes, mouth and nose) in the captured expression,
REVISITING THE RELATIONSHIP BETWEEN LAND PRICE AND PARCEL SIZE

Hedonic land price models often use parcel size as an explanatory variable. Empirical analyses, however, are rather ambiguous regarding the direction and the size of the effect of this variable on farmland values. The objective of this paper is to investigate this size-price relation in detail and to derive recommendations for an appropriate specification of hedonic land price models. Our analysis consists of three steps. First, we conduct a meta-analysis based on a comprehensive literature review. Second, we analyze a dataset of more than 80,000 land transactions in Saxony-Anhalt, Germany, using the non-parametric locally weighted scatterplot smoothing (LOWESS) estimator. This unconditional smoothing algorithm identifies negative size-price relations for very small and large plots, whereas it finds a positive relation for medium plot. We use this finding in our third step, a hedonic land price model, in which the size-price relation is modelled conditional on land and buyer characteristics. From these steps, we conclude that the complex relationship between land price and plot size cannot be captured by a simple functional form since it is affected by several economic factors, such as economies of size, transaction cost, and financial constraints.
Low-Cost and Effective Student-Orientation Mastery Learning Simulator for Programming Course

The training method, one teacher teaches one student to solve his programming mistakes by using one computer, costs a little time. In this study, the low-cost and effective modeling, which the teacher integrates the common mistakes by students into the programming simulator, is presented. In programming course, the basic and common mistakes made by the students are first listed and then the teacher takes these mistakes as the course objective of mastery learning. For each course objective, the corresponding mistakes are randomly generated by the programming simulator. Hence, the students can generate the wrong source codes and then finish the exercises to fix all mistakes by themselves. The wrong source codes are quite different when the students perform the programming simulator. Therefore, the students need to realize the programming principle instead of memorize the answers. Experimental results show that the students indeed master to fix these mistakes which are corresponding to the objectives.
Vulnerability assessment on WAMS communication system based on attack tree model

With the promotion and application of wide area measurement systems (WAMS) in power system, the security of its communication system is attracting a growing body of research. To analyze the vulnerability of WAMS communication infrastructures when facing network intrusions, an assessing method of vulnerability based on attack tree model is proposed. First, the attack tree model of WAMS communication system is established, which directly reflects the intrusion scenarios when the system is maliciously attacked. Second, a more reasonable assessing method for the vulnerability of passwords and ports is proposed, as well as the vulnerability of the system with all scenarios taken into consideration. Finally, the paper puts forth the concept of vulnerability sensitivity, which can quantitatively evaluate the effect of changes in the leaf vulnerability on the system, so as to find out the critical port.
Segments and Elements Influenced on ERP System Implementation

The purpose of this paper is to discuss the segments and elements that influence on Enterprise Resource Planning (ERP) system implementation. The authors categorize the elements into four segmentations: functional capability, managerial capability, implementation capability and technology capability. The authors use the iterative triangulation methodology to establish this study. The proposed concept is then applied into a number of Malaysian SMEs to analyze their capability in ERP system adoption. By proposing these segments and elements influenced on ERP system implementation, the researcher expects that this concept will be valuable for giving the supplement in academic researches and also assist the practitioners in industry to implement ERP system successfully.
Survey on Distributed Hash Tables

In this paper we review distributed hash tables (DHTs) that work as overlay networks on top of the IP network. We compare these DHTs under a number of key aspects that concern performance, scalability and self-configurability. Additionally, since these networks are not suitable for wireless ad hoc networks, we shortly review techniques that can be used to take distributed hash tables to wireless environments. Document Identifier TR-05 Document Status Final Created 13 October 2005 Revised 24 November 2006 Distribution Public c © 2006 the University of Lisbon Permission to copy without fee all or part of this material is granted provided that this copyright notice and the title of the document appear. To otherwise copy or republish requires explicit permission in writing from the University of Lisbon. 1This paper is based on a chapter of the PhD Thesis of Filipe Araujo [1].