Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic elasticity in OpenStack

Cloud computing infrastructures are the most recent approach to the development and conception of computational systems. Cloud infrastructures are complex environments with various subsystems, each one with their own challenges. Cloud systems should be able to provide the following fundamental property: elasticity. Elasticity is the ability to automatically add and remove instances according to the needs of the system. This is a requirement for pay-per-use billing models. Various open source software solutions allow companies and institutions to build their own Cloud infrastructure. However, in most of these, the elasticity feature is quite immature. Monitoring and timely adapting the active resources of a Cloud computing infrastructure is key to provide the elasticity required by diverse, multi-tenant and pay-per-use business models. In this paper, we propose Elastack, an automated monitoring and adaptive system, generic enough to be applied to existing IaaS frameworks, and intended to enable the elasticity they currently lack. Our approach offers any Cloud infrastructure the mechanisms to implement automated monitoring and adaptation as well as the flexibility to go beyond these. We evaluate Elastack by integrating it with the OpenStack showing how easy it is to add these important features with a minimum, almost imperceptible, amount of modifications to the default installation.
On ${l}_{q}$  Optimization and Sparse Inverse Covariance Selection

Graphical models are well established in providing meaningful conditional probability descriptions of complex multivariable interactions. In the Gaussian case, the conditional independencies between different variables correspond to zero entries in the precision (inverse covariance) matrix. Hence, there has been much recent interest in sparse precision matrix estimation in areas such as statistics, machine learning, computer vision, pattern recognition, and signal processing. A popular estimation method involves optimizing a penalized log-likelihood problem. The penalty is responsible for inducing sparsity and a common choice is the convex l1 norm. Even though the l0 penalty is the natural choice guaranteeing maximum sparsity, it has been avoided due to lack of convexity. As a result, in this paper we bridge the gap between these two penalties and propose the non-concave lq penalized log-likelihood problem for sparse precision matrix estimation where 0 ≤ q <; 1. A novel algorithm is developed for the optimization and we provide some of its theoretic properties that are useful in sparse linear regression. We illustrate on synthetic and real data, showing reconstruction quality comparisons of sparsity inducing penalties:l0, lq with 0 <; q <; 1, l1, and SCAD.
A method of entity relationship modeling of Internet of Things based on supernetwork

Currently, the methods of entity relationship modeling are mainly based on existing historical data or Complex Networks, because one-dimensional elements characterizing clusters relationship can't be a true reflection of the entity relationship and potential structure of IOT(Internet of Things). This paper proposes a method of entity relationship modeling of IOT based on Supernetwork, by considering both dominant relationship and implicit relationship of interest similarity, we build a multidimensional model of entity relationship of IOT, and use the MIT Media Lab's real datasets to verify the feasibility and effectiveness of the model. It lays a solid foundation for further study of the dynamic growth of the entity relationship network.
The Research of Relationship between Social Capital and Corporate Knowledge's Transfer and Innovation

Innovation, as an important firm strategy, is the key activity that contributes to building core competitive advantage. During the process, the social capital decides the level of company's gaining innovative knowledge and innovating. This paper, based on social capital theory and knowledge management theory, explores the relationship between knowledge's transfer and innovation, and identify the three social capitals which impacting on knowledge's transfer and innovation.
Genome Sequence of a Plant Growth-Promoting Rhizobacterium, Pseudomonas sp. Strain 31-12

We present here a draft genome sequence of Pseudomonas sp. strain 31-12, a plant growth-promoting rhizobacterium of several crop plants that was isolated from the rhizosphere of corn in southern Ontario, Canada. ABSTRACT We present here a draft genome sequence of Pseudomonas sp. strain 31-12, a plant growth-promoting rhizobacterium of several crop plants that was isolated from the rhizosphere of corn in southern Ontario, Canada.
Feature Selection for fMRI Classification

The functional Magnetic Resonance Imaging (fMRI) has provided us with an approach of revealing the activity of brain. Due to the large amount of data in fMRI studies, feature selection techniques are used to select particular features for classifier. In this project, Spectral Clustering is implemented to construct features to achieve best reconstruction of the data and be most efficient for making predictions.
Industrial case study of the MICA support system for warehouse workers

Returns caused by the delivery of incorrect items constitute a major problem for non-automated warehouses. Returns not only create extra costs, they also aggravate customers. Most errors in logistics occur during the picking process. MICA --- a mobile assistance system for warehouse workers --- unobtrusively navigates the worker on his way through the warehouse and effectively prevents picking errors using RFID technology. Therefore, MICA reduces error rates, job training periods, and the time required for picking and packing, leading to lower costs for warehouse operators and increased customer satisfaction. In this paper you will read about a case study where the MICA prototype was field-tested in the warehouse of a medium-sized enterprise.
A high PSRR capacitor-less on — Chip low dropout voltage regulator

A design of a high PSRR capacitor-less low dropout voltage regulator (LDO) is presented. This circuit is stable for full load current range from 0 to 100mA. A mid frequency zero has been introduced to stabilize the loop. The PSRR achieved was −71.258dB at 130kHz, and more than −40dB upto 650.750kHz. The LDO is capable of generating fixed 1V from a supply of 3.0V which on discharging goes to 1.5V. The LDO has been implemented in 0. 18µm generic CMOS technology. Simulation result showed that the line regulation achieved was 370µV/V and load regulation was just 0.01173%/mA.
Seaports Activity (A)synchronicity, Trade Intensity and Business Cycle Convergence: A Panel Data Analysis

In the present paper we suggest and test a general model that aims to explain the determinants and the extent of parallel moving (i.e. the synchronization) of trading ports activities. The model adopts a macro-approach and its main constituents are a) the business cycle (GDP) convergence, b) the intensity of trade between the countries of origin and destination, and c) the variables associated to international shipping developments. The present study makes possible the identification of distinct influences on the link between a given country’s economic and port activities influences which can traced back mainly to macro policies. The elaboration of such determinants and their relationships contribute to a better understanding of the dynamics of port throughput and facilitates infrastructure planning and strategic policy decision. The present paper’s conclusions feature a framework for modeling the relationship between ports and shipping activities. Panel data from European main ports for the period 1986-2010 were used for the empirical investigation and a dynamic panel GMM estimation technique was employed. Model simulations are conducted and provide evidence that an increase or decrease of port business cycle synchronization is a) attributed to the technical and structural characteristics of ports and, b) greatly influenced by the general macroeconomic environment.
Exploiting curvature to compute the medial axis with Constrained Centroidal Voronoi Diagram on discrete data

In this paper, we present a novel method for medial axis approximation based on Constrained Centroidal Voronoi Diagram of discrete data (image, volume). The proposed approach is based on the shape boundary subsampling controled by a clustering approach which generates a Voronoi Diagram well suited for Medial Axis extraction. The resulting Voronoi Diagram is further filtered in order to capture the correct topology of the medial axis. The main contribution of this paper is the integration of both a curvature maps and a distance map for controlling the local variability of Voronoi cells densities. Examples of complex shape processing prove the effectiveness of the proposed approach.
Investigation on applying passive magnetic bearings to impeller left ventricular assist devices(LVAD)

In order to avoid mechanical wear in artificial heart pumps for long-term application, the author developed 2 prototypes of permanent maglev left ventricular assist devices(LVAD): axially driven centrifugal pump, and radially driven centrifugal pump, in which a passive magnetic(PM) bearing in common use(Bearing A) and a novel PM bearing developed by the author(Bearing B) were used respectively. The permanent maglev impeller centrifugal pumps were tested in laboratory with normal saline. For the first pump, its motor magnets could be seen through a Plexiglas housing of the device. At the standstill, the rotor had only one contact point with the stator. As the rotor increased its rotating speed to over 4000rpm, the rotor moved axially off from the contact point about 0.5mm and disaffiliated from the stator, and became fully levitated. For the second pump, its rotor eccentric distance was measured by 4 Hall-sensors symmetrically fixed on the periphery of the stator. The experiment indicated that the eccentricity and vibration amplitudes would be under 0.07mm and 0.06mm respectively, smaller than the gap between the rotor and the stator (0.15mm), if the rotating speed increased to over 3250rpm. That means the rotor had no contact with the stator, namely, was suspended in the stator. The results demonstrate that passive magnetic bearings could achieve stable magnetic levitation in both of the centrifugal heart pumps, when rotating speed reaches high enough and thus obtains a so-called gyro-effect, namely, a rotating body with high enough speed could maintain its rotation stably.
Bridging the Gap between Theory and Practice: Integrating Multicultural Theory into an Undergraduate Foundations of Education Class.

This paper describes an action plan for integrating multicultural theory into a teacher education social foundations course and presents results from an evaluation of this effort. The action plan for the course was to integrate the theory and practice of multicultural education across five master questions posed by T. Berg (1999) and in the context of five dimensions of multicultural education defined by J. Banks (1995). "Schools in Communities" was planned as an undergraduate course to introduce students to the foundations of education by studying social, philosophical, and historical aspects of U.S. culture. Through the use of the five master questions and the five dimensions of multicultural education, students were to focus on the forces and influences that guide the U.S. education system and the individual classroom. Among the features of the course were the use of texts representing the diversity of students, including examples from children's literature, and a variety of enrichment experiences, ranging from speakers through videos and interactive technology. Students also conducted an ethnographic study of a school. The responses of 27 students (7 African American, 18 Caucasian, 1 Arab) showed gains in their appreciation of the complexity of education and the relationship between schools and society. Twelve of the students commented that they really enjoyed the class, and 14 said that the class had opened their eyes to many educational issues. An appendix contains the course syllabus and some supplemental information. (SLD) Reproductions supplied by EDRS are the best that can be made from the original document.
Qualitative Methodologies as a Vehicle for Understanding the Emotional Process in Soccer Players

A descriptive-exploratory investigation with pre-experimental design tried to answer the question how sports performance in competition is influenced by emotions? Two assessing instruments were built – Questionário Emoção e Desempenho Desportivo (QEDD, study 1) and Sistema de Observação do Desempenho Desportivo - (SODD -1, study 3). Interviews were used to identify the emotions’ triggers and which sports performance factors were influenced by those emotions (study 2). Study 4 intended to answer the research question, with two football players. For both players different kinds of emotions were generated by the same trigger and by different triggers. For player 1, positive and negative emotions always had a functional effect in sports performance; for player 2, positive emotions had only a functional effect on sports performance, negative emotions had both effects in sports performance (functional and dysfunctional), and some of the studied emotions had no influence in sports performance.
Adjustment Costs from Environmental Change Induced by Incomplete Information and Learning

The paper begins with the problem of a firm subject to random productivity shocks drawn from a particular distribution. We are concerned with the case whereby the distribution of the shocks changes without the knowledge of the firm. Over time the firm learns about the nature and extent of the change in the distribution of the shock and adjusts, incurring adjustment costs in the process. The long run loss in profits (Ž) due to the shift in the distribution we term the adaptation costs. The transitory profit loss, incurred while the firm is learning about the distribution shift, is termed the adjustment cost. The theory is developed and then applied to the problem of measuring adaptation and adjustment costs in the face of unanticipated and imperfectly observed climate change in agriculture. The empirical part of the paper involves estimating a supply function for corn that depends on actual weather realizations and expected weather, using county level data for the US. We then simulate the effect of an unobserved climate shock, where learning about the climate shock is by observing the weather and updating prior knowledge using Bayes Rule.
Duration of rise in free fatty acids determines salicylate's effect on hepatic insulin sensitivity

We have shown in rats that sodium salicylate (SS), which inhibits IkBa kinase B (IKKB), prevents hepatic and peripheral insulin resistance caused by short-term (7 h) i.v. administration of Intralipid and heparin (IH). We wished to further determine whether this beneficial effect of SS persisted after prolonged (48 h) IH infusion, which better mimics the chronic free fatty acid (FFA) elevation of obesity. Hence, we performed hyperinsulinemic euglycemic clamps with tritiated glucose methodology to determine hepatic and peripheral insulin sensitivity in rats infused with saline, IH, IH and SS, or SS alone. SS prevented peripheral insulin resistance (P<0.05) caused by prolonged plasma FFA elevation; however, it did not prevent hepatic insulin resistance. In skeletal muscle, protein levels of phospho-IkBa were augmented by prolonged IH administration and this was prevented by SS, suggesting that IH activates while SS prevents the activation of IKKB. Markers of IKKB activation, namely protein levels of phospho-IkBa and IkBa, indicated that IKKB is not activated in the liver after prolonged FFA elevation. Phosphorylation of serine 307 at insulin receptor substrate (IRS)-1, which is a marker of proximal insulin resistance, was not altered by IH administration in the liver, suggesting that this is not a site of hepatic insulin resistance in the prolonged lipid infusion model. Our results suggest that the role of IKKB in fat-induced insulin resistance is time and tissue dependent and that hepatic insulin resistance induced by prolonged lipid elevation is not due to an IRS-1 serine 307 kinase.
A Fast Moving Object Detection Technique In Video Surveillance System

Nowadays automated surveillance system has become a trend in field of security. Video processing algorithms are utilized to implement these systems. For any video processing system, first task is to detect moving object or subtract a background. In Computer Vision, many techniques are available for detection of the moving object, but Mixture of Gaussian (MoG) models [1] is best suited for system having static and complex background with clutters. MoG technique is more accurate but has a larger time complexity which is unrealistic for real time processing. In this paper, we present a fast technique to extract moving object from background using MoG model and Haar wavelet. In this technique, before applying the MoG we down sample each video frame to acceptable resolution using Haar wavelet decomposition. Selection of wavelet decomposition level depends on original video resolution. The technique has been implemented and tested on videos of PETS [2] and CAVIAR [11] databases. For PETS sample, Original video sample frames having resolution 768 X 576 are down sampled to resolution 192 X 144 using level three Haar wavelet decomposition. Then MoG model is applied to subtract the background. Our result shows this technique is able to detect all moving objects from video in presence of complex background and clutters. We observed that this technique works almost three times faster than using only MoG model without sacrificing the quality of results. In the first part of paper, introduction to visual surveillance system and its architecture have been discussed. In second part, related work in literature is surveyed. Haar wavelet decomposition and MoG models are explained in third part. In forth part, results of presented technique are thoroughly discussed and at last concluding and future work remarks are given. Keywords— Video surveillance, Background subtraction, Mixture of Gaussian model, Wavelet decomposition
PRICE REGULATION AND OPTIMAL SERVICE STANDARDS

Economic regulation of transport involves a problem common to many regulated industries, the specification of an appropriate quality level or service standard. In many industries the problem is ignored, or treated only implicitly or heuristically. In regulation of transport, however, it can play a crucial role in the determination of the price level and in the resulting equilibrium of the industry. Typically, even if the regulatory authorities do not explicitly deal with service quality, one can per ceive within the context of the competitive structure of the transport industry an endogenous relationship between price level and quality of service. Usually this derives from the capacity of the system, its headways and the stochastic para meters of the demand it serves. To explore this problem, we will consider a hypo thetical market for taxicabs. The taxicab industry, in its usual urban setting (where a taxi is engaged by hailing one that is vacant), provides a service in which an important element of service quality, waiting time to engage a taxi, is not amenable to differentiation. That is, an impatient customer cannot effectively transmit to the supplier his willingness to pay for a reduced wait, but must accept the stochastic wait given by the equilibrium of the market. The crux of the regulatory problem is that, a priori, there is no "normal" utilisation rate for taxis, and no concomitant "normal" level of service quality, as measured by the expected waiting time.2 Rather, one can show that the market can reach an equilibrium in response to any price within a wide range of prices, each equilibrium being characterised by a different level of common service quality. The regulators' dilemma, then, is the selection of a price, and implicitly a single service standard, for a population of customers with diverse preferences for service quality. The model described below is not meant to portray accurately any specific taxicab market, but rather to serve as a basis for discussion of the central problem of this paper, the selection of an optimal level of price and service quality.3 It is clear that other transport industries have a similar problem; the capacities and headways of a
Nanostructured Organic and Hybrid Solar Cells

This Progress Report highlights recent developments in nanostructured organic and hybrid solar cells. The authors discuss novel approaches to control the film morphology in fully organic solar cells and the design of nanostructured hybrid solar cells. The motivation and recent results concerning fabrication and effects on device physics are emphasized. The aim of this review is not to give a summary of all recent results in organic and hybrid solar cells, but rather to focus on the fabrication, device physics, and light trapping properties of nanostructured organic and hybrid devices.
Improvement of Transient Stability using Fuzzy Logic Controlled SMES in Matlab

In this paper, the transient stability of an electric power system is improved by fuzzy logic controlled superconducting magnetic energy storage (SMES). The effectiveness of the proposed fuzzy controlled SMES is compared with a conventional proportional integral (PI) controlled SMES. In addition to it a comparison between the fuzzy controlled SMES and fuzzy controlled braking resistor (BR) is also carried out. The simulation results show that under 3 phase fault, the fuzzy controlled SMES performance is better than PI controlled SMES. Furthermore, the performance of SMES is better than that of BR. The proposed method provides a very simple and effective means of improvement of transient stability.
Modeling and analysis of routing in IoT networks

Internet of Things (IoTs) is gaining increasing significance due to real-time communication and decision making capabilties of sensors integrated into everyday objects. IoTs are power and bandwidth-constrained with applications in smarthome, healthcare, transportation and industrial domains. Routing bears significant importance in IoTs where sensors acting as hosts deliver data to the gateways which in turn impacts power consumption. Thus there exists a need for modeling and analysis of routing in IoT networks towards predicting power consumption. In this work, we develop an analytical model of a naive flooding based routing protocol using Markov chains. In particular, we derive steady state transition probabilities of transmit and receive states using protocol execution traces and further utilize them towards predicting power consumption. Our approach to modeling is generic in that it can be applied to routing protocols across domains. Evaluation of the model shows that the predicted values for power consumption lie closer to the actual observations obtained using ns-2 simulation thus resulting in minimal mean square errors.
Semantic ranking based on Computer Science Ontology weight

Document Ranking retrieval systems are the top documents ordering and particularly appropriate for user's query. Most existing assigned based on the information retrieval term frequency (tf) that appears in the document. Although the number of times that the term occurrence is more relevant, but not meant for rank documents according to their proximity to user's query. So this paper, we presented a new document semantic ranking process for the semantic ranking that proposes a new weight of query term in the document based on Computer Science Ontology weight. The experimental results show that the new document similarity score between a user's query and the paper suggests that the new measures were effectively ranked.