Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficiency Frontier based Model for Cloud Computing Provider Selection and Ranking

Cloud computing become a successful service model that allows hosting and distribution of computational resources all around the world, via Internet and on demand. This success leveraged and popularized its adoption into all major IT companies. Based on this success, a large number of new companies were competitively created as providers of cloud computing services. This fact can difficult the customer ability to choose among those several cloud providers the most appropriate one to attend their requirements and computing needs. Therefore, this work aims to propose a model capable of selecting and ranking cloud providers according the analysis on the most efficient ones using a popular Multicriteria Decision Analysis (MCDA) method called Data Envelopment Analysis (DEA), that calculates efficiency using Linear Programming (LP) techniques. To accomplish that, the efficiency modeling is based on the analysis of each Performance Indicator (PI) values desired by the customer and the available ones in the cloud provider database. An example of the method’s usage is given to illustrate the model operation, selection results and final provider ranking for five hypothetical customer requests and for ten providers.
Predictive interference-based scheduling mechanism for direct communications in IEEE 802.16 networks

The point-to-multipoint (PMP) mode is considered a well-adopted transmission type that is supported by the IEEE 802.16 standard. The base station (BS) is served as a centralized coordinator to control and forward packets for mobile stations (MSs) within the network. With the consideration of direct communications, the required bandwidth and packet latency are reduced. However, the inappropriate arrangements of direct communications may introduce additional inter-cell interferences for other communications. In order to avoid this situation, a predictive interference-based scheduling (PIS) mechanism for each pair of MSs that are expected to conduct direct communication is proposed in this paper. Based on calculating the interference region and feasible region for the pair, the PIS mechanism properly arranges the MSs to conduct direct communication. The efficiency of the proposed PIS mechanism is evaluated and compared via simulations. Simulation studies show that the PIS approach efficiently enhances the performance of user throughput in comparison with the original adaptive point-to-point (APC) approach and the conventional IEEE 802.16 scheme.
PBL BERNUANSA ADIWIYATA DENGAN BLENDED LEARNING UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH DAN KARAKTER PEDULI LINGKUNGAN

Tujuan dari penelitian ini untuk memperoleh keefektifan dan implementasi dari model pembelajaran tersebut serta untuk memperoleh temuan pengetahuan mengenai kemampuan pemecahan masalah dan karakter peduli lingkungan berdasarkan perbedaan gender. Jenis penelitian ini adalah kuantitatif dan kualitatif. Pengolahan data secara kuantitatif menggunakan yaitu uji ketuntasan, uji banding dengan two way ANOVA dan uji pengaruh, sedangkan kualitatif dengan triangulasi data dari dokumen, wawancara dan pengamatan pada subjek penelitian pilihan yang terdiri dari 5 siswa laki-laki dan 5 siswa perempuan dari kelompok tinggi, sedang dan rendah. Hasil penelitian ini sebagai berikut: (1) Pembelajaran matematika dinyatakan efektif, yaitu: a) KPM (Kemampuan Pemecahan Masalah) mencapai ketuntasan; b) adanya pengaruh positif keterampilan pemecahan masalah dan karakter peduli lingkungan terhadap KPM; c) Rata-rata KPM dan karakter peduli lingkungan untuk kelas dengan pembelajaran PBL bernuansa adiwiyata menggunakan blended learning lebih baik dibanding kelas dengan pembelajaran PBL menggunakan blended learning dan kelas dengan pembelajaran PBL. (2) Kemampuan pemecahan masalah dan karakter peduli lingkungan siswa perempuan lebih tinggi dibanding siswa laki-laki. The purpose of this study was to obtain the effectiveness and implementation of the learning model as well as to acquire knowledge about problem solving ability and environment caring character based on gender differences. This research is both quantitative and qualitative. Quantitative data processing is held use completion test, comparative test with two-way ANOVA and influence test while qualitative data processing is held by triangulate data from document, interview and observation from selected research subject that consists of 5 male students and 5 female students from high, medium and low group. Results of this study as follows: (1) Learning mathematics is declared effective, namely: a) KPM achieve mastery; b) the positive influence of problem solving skills and enviroment caring character to KPM; c) Average KPM and environment caring character in class with adiwiyata nuanced PBL learning using blended learning is better than class with PBL using blended learning and class with PBL. (2) The ability of problem solving and environment character caring in female student is higher than male student.
Identification of different stages of diabetic retinopathy

This work presents different eye dieses like Diabetic Retinopathy, Catract and Glaucona which causes our eye damage. Among these three Diabetic Retinopathy is most perilous due to its different stages "(Mild NPDR - Non Proliferative Diabetic Retinopathy Moderate NPDR and PDR - Proliferative Diabetic Retinopathy)" and segregate it from a normal eye with the study of fundus images. In this work, a computer based approach have been proposed in which the features are extracted from these images using image pre-processing and furnish into the "SVM (Support Vector Machine) using Sequential minimal optimization algorithm".
Treebank Translation for Cross-Lingual Parser Induction

Cross-lingual learning has become a popular approach to facilitate the development of resources and tools for low density languages. Its underlying idea is to make use of existing tools and annotations in resource-rich languages to create similar tools and resources for resource-poor languages. Typically, this is achieved by either projecting annotations across parallel corpora, or by transferring models from one or more source languages to a target language. In this paper, we explore a third strategy by using machine translation to create synthetic training data from the original source-side annotations. Specifically, we apply this technique to dependency parsing, using a cross-lingually unified treebank for adequate evaluation. Our approach draws on annotation projection but avoids the use of noisy source-side annotation of an unrelated parallel corpus and instead relies on manual treebank annotation in combination with statistical machine translation, which makes it possible to train fully lexicalized parsers. We show that this approach significantly outperforms delexicalized transfer parsing. % despite the error-prone translation step.
Development of Low Cost Treatment Options for Arsenic Removal in Water Treatment Facilities

3 Introduction 3 Arsenic Maximum Contaminant Level 3 Arsenic Speciation 4 Concurrent Arsenic and Iron Removal 4 Prior Work in the Area of Oxidation/Coagulation/Filtration for Arsenic Removal.5 Fenton Process Chemistry 5 Project Approach 8 Materials and Methods 9 Chemicals 9 Analytical Methods 9 Arsenic 9 Iron 10 Hydrogen Peroxide 10 Other Instrumental Measurements 10 Experimental Procedures 10 Sample Collection 10 Flow Experiments 11 Laboratory flow experiments 11 On-site pilot-scale experiments 13 Calculational Methods 16 Kinetic Calculations 16 Equilibrium Calculations 17 Results and Discussion 18 Preliminary Laboratory Experiments 18 Determination of Rates of Iron Oxidation by Oxygen 18 Effect of Hydrogen Peroxide on Iron Oxidation Rate 20 Laboratory Flow Experiments 26 On-Site Pilot-Scale Flow Experiments 27 Treatment Cost Estimates 33 Conclusions 34 Future Work 35 Acknowledgements 35 Appendix A 36 Appendix B 45 Appendix C 50 References 53
Temporal Evolution of Vehicular Network Simulators: Challenges and Perspectives

New proposals of applications and protocols for vehicular networks appear every day. Its crucial to evaluate, test and validate these proposals on a large scale before deploying them in the real world. Simulation is by far the preferred method by the community when conducting the evaluation. In this paper we survey the main simulators for vehicular networks and show how they evolved over time. Thus, we provide information that leads to an understanding of how, and how long does it take for the scientific community to absorb a new simulator proposal. Additionally, valuable insights are presented to help researchers make better choices when selecting the appropriate simulator to evaluate new proposals.
Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures

Server consolidation based on virtualization is an important technique for improving power efficiency and resource utilization in cloud infrastructures. However, to ensure satisfactory performance on shared resources under changing application workloads, dynamic management of the resource pool via online adaptation is critical. The inherent tradeoffs between power and performance as well as between the cost of an adaptation and its benefits make such management challenging. In this paper, we present Mistral, a holistic controller framework that optimizes power consumption, performance benefits, and the transient costs incurred by various adaptations and the controller itself to maximize overall utility. Mistral can handle multiple distributed applications and large-scale infrastructures through a multi-level adaptation hierarchy and scalable optimization algorithm. We show that our approach outstrips other strategies that address the tradeoff between only two of the objectives (power, performance, and transient costs).
A Prognostic Model for Electrohydraulic Servovalves

Servovalves are critical components of the hydraulic servos and their correct operation is mandatory to ensure the proper functioning of the controlled servosystem. A continuous monitor is typically performed that can detect a servovalve loss of operation, but falls short of recognizing other malfunctionings. A research was performed aimed at developing a prognostic algorithm able to identity the precursors of a servovalve failure and its degradation pattern. A model based technique was used that fuses several information obtained by comparing actual with expected response of the servovalve to recognize a degradation. The research was focused to flight control systems, but it can be used in other application areas. To assess the robustness of the algorithm a simulation test environment was developed. Simulations were run in which a servovalve controlled actuator was subjected to sequences of commands and loads representative of those encountered during actual operation, and variations of the servovalve characteristics within their normal range were applied. At the same time, degradations of the servovalve were superimposed and the merit of the prognostics algorithm was verified. The results showed an adequate robustness and confidence was gained in the ability to detect servovalve degradations with low risk of false alarms or missed failures. *
A comparison of CFAR strategies for blob detection in textured images

Traditional CFAR (constant false alarm rate) approaches applied to the detection of objects in images have proved useful in locating small patches on non-stationary backgrounds. However, the topic of detecting arbitrarily large objects by means of these approaches has received less attention. In this paper we make a comparative analysis of the performance of several CFAR strategies applied to the detection and segmentation of blobs in textured images. The difference in the strategies lies in the way the references for the estimation of the parameters of the detector are considered. By treating four detection schemes through MonteCarlo simulation, we show that directional approaches to the target have better results than non-directional ones. The fourth approach, refered to as ‘gradient-guided’, is the most promising philosophy.
Multi-camera tracking mehtod based on particle filtering

A video tracking system raises a wide range of possibilities in today's society, particularly in security, monitoring, and robotics. The most important research in tracking systems is to discover and develop an available method and algorithm for tracking an object's motion. The objective of this paper is to propose a new method that combines a prediction method and particle filter to manage problems in a wide area of observation. The comparative study of the method is provided and its capabilities are evaluated.
Exercise training increases L-type calcium current density in coronary smooth muscle.

Exercise training produces numerous adaptations in the coronary circulation, including an increase in coronary tone, both in conduit and resistance arteries. On the basis of the importance of voltage-gated Ca2+ channels (VGCC) in regulation of vascular tone, we hypothesized that exercise training would increase VGCC current density in coronary smooth muscle. To test this hypothesis, VGCC current was compared in smooth muscle from conduit arteries (>1.0 mm), small arteries (200-250 μm), and large arterioles (75-150 μm) from endurance-trained (Ex) or sedentary miniature swine (Sed). After 16-20 wk of treadmill training, VGCC current was determined using whole cell voltage-clamp techniques. In both Ex and Sed, VGCC current density was inversely related to arterial diameter, i.e., large arterioles > small arteries > conduit arteries. Exercise training increased peak inward currents approximately twofold in smooth muscle from all arterial sizes compared with those from Sed (large arteriole, -12.52 ± 2.05 vs. -5.74 ± 0.99 pA/pF; small artery, -6.20 ± 0.97 vs. -3.18 ± 0.44 pA/pF; and conduit arteries, -4.22 ± 0.30 vs. -2.41 ± 0.55 pA/pF; 10 mM Ba2+ external). Dihydropyridine sensitivity, voltage dependence, and inactivation kinetics identified this Ca2+ current to be L-type current in all arterial sizes from both Sed and Ex. Furthermore, peak VGCC current density was correlated with treadmill endurance in all arterial sizes. We conclude that smooth muscle L-type Ca2+ current density is increased within the coronary arterial bed by endurance exercise training. This increased VGCC density may provide an important mechanistic link between functional and cellular adaptations in the coronary circulation to exercise training.
Model‐based reliability and diagnostics: A common framework for reliability and diagnostics

Generally, technical systems are not guaranteed to work correctly. They are more or less reliable. One main problem for technical systems is the computation of the reliability of a system as studied in reliability theory. A second main problem for technical systems is the problem of diagnostic, i.e., explanations why something does not work. In fact, these problems are, in some sense, dual to each other.
Automatic Generation of OWL Ontology from XML Data Source

The eXtensible Markup Language (XML) can be used as data exchange format in different domains. It allows different parties to exchange data by providing common understanding of the basic concepts in the domain. XML covers the syntactic level, but lacks support for reasoning. Ontology can provide a semantic representation of domain knowledge which supports efficient reasoning and expressive power. One of the most popular ontology languages is the Web Ontology Language (OWL). It can represent domain knowledge using classes, properties, axioms and instances for the use in a distributed environment such as the World Wide Web. This paper presents a new method for automatic generation of OWL ontology from XML data sources.
A 12-bit DAC for Space Applications

We present a current output digital-to-analog converter (DAC) targeted for spacecraft applications. The DAC uses a switched current design and thermometer decoding guaranteeing monotonic behavior. Radiation Hard by Design techniques are used to mitigate potential total dose and Single Event Latch-up issues. A prototype DAC has been fabricated on a commercial 0. 5μm process through the MOSIS service.
Zoeal stages of alpheus brevicristatus De Haan, 1849 (decapoda, caridea, alpheidae) with a key to the first zoeal larvae of three Korean alpheus species

The four zoeal stages of Alpheus brevicristatus De Haan, 1849 are described and illustrated in detail from larvae reared in the laboratory. The larvae of A. brevicristatus was compared with those of the other Alpheus and a key to the first zoeal larvae of three Korean Alpheus species is provided
ROSETTA: the compile-time recognition of object-oriented library abstractions and their use within user applications

Libraries arise naturally from the increasing complexity of developing scientific applications, the optimization of libraries is just one type of high-performance optimization. Many complex applications areas can today be addressed by domain-specific object-oriented frameworks. Such object-oriented frameworks provide an effective compliment to an object-oriented language and effectively permit the design of what amount to essentially domain-specific languages. The optimization of such a domain-specific library/language combination however is particularly complicated due to the inability of the compiler to optimize the use of the libraries abstractions. The recognition of the use of object-oriented abstractions within user applications is a particularly difficult but important step in the optimization of how objects are used within expressions and statements. Such recognition entails more than just complex pattern matching. The approach presented within this paper uses specially built grammars to parse the C++ representation. The C++ representation is itself obtained using a modified version of the SAGE II C/C++ source code restructuring tool which is inturn based upon the Edison Design Group (EDG) C++ front-end. ROSETTA is a tool which automatically builds grammars and parsers from class definitions, associated parsers parse abstract syntax trees (ASTs) of lower level grammars into ASTs of higher level grammars. Themore » lowest level grammar is that associated with the full C++ language itself, higher level grammars specialize the grammars specific to user defined objects. The grammars form a hierarchy and permit a high-degree of specialization in the recognition of complex use of user defined abstractions. « less
Validation and Evaluation of Automatically Acquired Multiword Expressions for Grammar Engineering

This paper focuses on the evaluation of methods for the automatic acquisition of Multiword Expressions (MWEs) for robust grammar engineering. First we investigate the hypothesis that MWEs can be detected by the distinct statistical properties of their component words, regardless of their type, comparing 3 statistical measures: mutual information (MI), � 2 and permutation entropy (PE). Our overall conclusion is that at least two measures, MI and PE, seem to differentiate MWEs from non-MWEs. We then investigate the influence of the size and quality of different corpora, using the BNC and the Web search engines Google and Yahoo. We conclude that, in terms of language usage, web generated corpora are fairly similar to more carefully built corpora, like the BNC, indicating that the lack of control and balance of these corpora are probably compensated by their size. Finally, we show a qualitative evaluation of the results of automatically adding extracted MWEs to existing linguistic resources. We argue that such a process improves qualitatively, if a more compositional approach to grammar/lexicon automated extension is adopted.
L3-SVMs: Landmarks-based Linear Local Support Vectors Machines

For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past decade. In this paper, we introduce a new local SVM method, called L$^3$-SVMs, which clusters the input space, carries out dimensionality reduction by projecting the data on landmarks, and jointly learns a linear combination of local models. Simple and effective, our algorithm is also theoretically well-founded. Using the framework of Uniform Stability, we show that our SVM formulation comes with generalization guarantees on the true risk. The experiments based on the simplest configuration of our model (i.e. landmarks randomly selected, linear projection, linear kernel) show that L$^3$-SVMs is very competitive w.r. t. the state of the art and opens the door to new exciting lines of research.
A bit commitment signcryption protocol for wireless transport layer security (WTLS)

Signcryption is comparatively new approach in public key cryptography. Signcryption has the virtue to provide the services of digital signature and public key encryption in a single step. So when we compare it in terms of cost and ease of use, signcryption is better than “signature then encryption” (or encryption then signature) approach. So signcryption protocols are obvious choice for WTLS where authentication along with savings of computational overheads is always desired. In this paper we present a signcryption protocol for WTLS using bit commitment scheme with its security analysis.
New Multiband Coupling Matrix Synthesis Technique and Its Microstrip Implementation

In this paper, we present a novel analytical multiband transversal coupling matrix synthesis technique. By properly combining several single-band filtering functions, the multiband filtering function with flexible transmission zeros and various bandwidth are available. The transversal coupling scheme is then transformed into a practically realizable coupling scheme. To verify the feasibility of the proposed method, two dual-band coupling schemes, both single-path and dual-path, are proposed as examples of the dual-band filter design. Finally, the dual-band filter, based on the coupling matrix corresponding to a specific coupling scheme, can be realized using a microstrip parallel-coupled structure.
Computational Snapshot Multispectral Cameras: Toward dynamic capture of the spectral world

Multispectral cameras collect image data with a greater number of spectral channels than traditional trichromatic sensors, thus providing spectral information at a higher level of detail. Such data are useful in various fields, such as remote sensing, materials science, biophotonics, and environmental monitoring. The massive scale of multispectral data-at high resolutions in the spectral, spatial, and temporal dimensions-has long presented a major challenge in spectrometer design. With recent developments in sampling theory, this problem has become more manageable through use of undersampling and constrained reconstruction techniques. This article presents an overview of these state-of-the-art multispectral acquisition systems, with a particular focus on snapshot multispectral capture, from a signal processing perspective. We propose that undersampling-based multispectral cameras can be understood and compared by examining the efficiency of their sampling schemes, which we formulate as the spectral sensing coherence information between their sensing matrices and spectrum-specific bases learned from a large-scale multispectral image database. We analyze existing snapshot multispectral cameras in this manner, and additionally discuss their optical performance in terms of light throughput and system complexity.
A Model for User Profiling in an ASP Environment

Nowadays an ever increasing number of companies rely on application service providers for hosting some or almost all software applications. In this domain, an effective way for customizing user interfaces would be highly desirable. We propose a profiling system interacting with the applications in order to capture users' behavior. The system is based on Bayesian networks and we show a strategy for combining data collected in each session and previous knowledge. Experiments on a prototype show that the proposed approach is promising