Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Priority Based Job Scheduling Algorithm in Cloud Computing Using Iterative Method

Cloud Computing is a platform for computing resources (Hardware and Software) that are delivered as a service over an internet network to the customers. Its intention is to share large scale equipments and resources for computation, storage, information and knowledge for scientific researches. There are many jobs that are required to be executed by the available resources to achieve best performance, minimal total time for completion, shortest response time, utilization of resource usage and etc. Because of these different objectives and high performance of computing environment, we need to design, develop, and propose a scheduling algorithm that outperforms appropriate allocation map of jobs due to different factors. Job scheduling is one of the major issue in cloud computing environment. In job scheduling priority is the biggest issue because some jobs need to be scheduled first then all other remaining jobs which can wait for a long time. In this paper, we have proposed an improvement in priority based job scheduling algorithm in cloud computing which is based on multiple criteria and multiple attribute decision making model.
A New Paradigm for Component-based Development

Hancock and Setzer describe how Haskell’s monolithic IO monad can be decomposed into worlds when working in a dependently typed language, like Martin-Lof’s type theory. This paper introduces the notion of world map and shows that worlds and world maps form a category with arbitrary products. The construction of the category is carried out entirely in type theory and directly implementable in dependently typed programming languages. If we let the notion of world replace the standard notion of interface, and the notion of world map replace the standard notion of component, we get a rigorous paradigm for component- based development. This new paradigm is investigated and several applications are given. For example, the problem of session state is given a very clean solution.
Achieving full spatial and multi-path diversity with cyclically delay shift receiver for SC-FDE system

In this paper, a low computational complexity and flexible Cyclic Delay receive scheme is proposed for the receiver in the uplink, in which more than two transmit antennas is hard to locate in one mobile device. By summing up the shifted copy of the received signal at different receive antennas, the multi-path channels of the Single Input and Multiple Output (SIMO) system are cascaded into an equivalent single input and single output (SISO) Inter-Symbol-Interference (ISI) channel. Then, the full multi-path diversity gain is obtained through minimum mean square error (MMSE) equalization in frequency domain. Furthermore, the closed-form outage probability expression is derived with proposed Grassmann Manifold based Multi-Dimensional Hypergeometric Analysis (GMMDHA) method, and the diversity order is achieved by the trend of the effective diversity order when SNR approaches infinity. Finally, simulation results are given to confirm the performance of the scheme, the accuracy of the outage probability and the system diversity order.
Identifying Corresponding Entities Based on Attribute Entropy in Heterogeneous Databases

With progress of information technology, it is necessary to access multiple heterogeneous databases in order to get the desired information from different sources. One main problem encountered constantly among heterogeneous databases is entity heterogeneity, which arises when the same real-word entity type is represented using different identifiers in different application domains. So identifying the heterogeneous entities and resolving conflicts are critical when realizing interoperability in multiple heterogeneous databases. In this paper, we examine the entity heterogeneity problem and propose an approach based on attribute entropy to identifying corresponding entities. The experimental results on real-world data show our proposed approach is very effective.
Metabolic engineering of acetoin and meso‐2, 3‐butanediol biosynthesis in E. coli

The functional reconstruction of acetoin and meso‐2,3‐butanediol (meso‐2,3‐BD) biosynthetic pathways in Escherichia coli have been explored systematically. Pathway construction involved the in vsivo screening of prospective pathway isozymes of yeast and bacterial origin. After substantial engineering of the host background to increase pyruvate availability, E. coli YYC202(DE3) ldhA( ilvC( expressing ilvBN from E. coli and aldB from L. lactis (encoding acetolactate synthase and acetolactate decarboxylase activities, respectively) was able to produce up to 870 mg/L acetoin, with no coproduction of diacetyl observed. These strains were also found to produce small quantities of meso‐2,3‐BD, suggesting the existence of endogenous 2,3‐BD dehydrogenase activity. Finally, the coexpression of bdh1 from S. cerevisiae, encoding 2,3‐BD dehydrogenase, in this strain resulted in the production of up to 1120 mg/L meso‐2,3‐BD, with glucose a yield of 0.29 g/g. While disruption of the native lactate biosynthesis pathway increased pyruvate precursor availability to this strain, increased availability of NADH for acetoin reduction to meso‐2,3‐BD was found to be the most important consequence of ldhA deletion.
The Link between Dependency and Cochange: Empirical Evidence

We investigate the relationship between class dependency and change propagation (cochange) in software written in Java. On the one hand, we find a strong correlation between dependency and cochange. Furthermore, we provide empirical evidence for the propagation of change along paths of dependency. These findings support the often alleged role of dependencies as propagators of change. On the other hand, we find that approximately half of all dependencies are never involved in cochanges and that the vast majority of cochanges pertain to only a small percentage of dependencies. This means that inferring the cochange characteristics of a software architecture solely from its dependency structure results in a severely distorted approximation of cochange characteristics. Any metric which uses dependencies alone to pass judgment on the evolvability of a piece of Java software is thus unreliable. As a consequence, we suggest to always take both the change characteristics and the dependency structure into account when evaluating software architecture.
The influence of changes in the composition of substrate on the mechanical properties of coatings

The variation of physic-mechanical properties of the functional surfaces with wear resistant coating under clear terms modification of the substrate alloy samples VK8 are analyzed. Substrate was doped with different elements (Ti, Cr, Zr and N) by ion implantation. Wear resistant coating TiN and TiAlN where formed by vacuum arc method.
Fgfr1 Is Required for Cortical Regeneration and Repair after Perinatal Hypoxia

Chronic postnatal hypoxia causes an apparent loss of cortical neurons that is reversed during recovery (Fagel et al., 2006). The cellular and molecular mechanisms underlying this plasticity are not understood. Here, we show that chronic hypoxia from postnatal days 3 (P3) to 10 causes a 30% decrease in cortical neurons and a 24% decrease in cortical volume. T-brain-1 (Tbr1)+ and SMI-32+ excitatory neuron numbers were completely recovered 1 month after the insult, but the mice showed a residual deficit in Parvalbumin+ and Calretinin+ GABAergic interneurons. In contrast, hypoxic mice carrying a disrupted fibroblast growth factor receptor-1 (Fgfr1) gene in GFAP+ cells [Fgfr1 conditional knock-out (cKO)], demonstrated a persistent loss of excitatory cortical neurons and a worsening of the interneuron defect. Labeling proliferating progenitors at P17 revealed increased generation of cortical NeuN+ and Tbr1+ excitatory neurons in wild-type mice subjected to hypoxic insult, whereas Fgfr1 cKO failed to mount a cortical neurogenetic response. Hypoxic wild-type mice also demonstrated a twofold increase in cell proliferation in the subventricular zone (SVZ) at P17 and a threefold increase in neurogenesis in the olfactory bulb (OB) at P48, compared with normoxic mice. In contrast, Fgfr1 cKO mice had decreased SVZ cell proliferation and curtailed reactive neurogenesis in the OB. Thus, the activation of FGFR-1 in GFAP+ cells is required for neuronal recovery after neonatal hypoxic injury, which is attributable in part to enhanced cortical and OB neurogenesis. In contrast, there is incomplete recovery of inhibitory neurons after injury, which may account for persistent behavioral deficits.
Evidence for two distinct classes of streptococcal M protein and their relationship to rheumatic fever

The antigenic relatedness of surface-exposed portions of M protein molecules derived from group A streptococcal isolates representing more than 50 distinct serotypes was examined. The data indicate that the majority of serotypes fall into two major classes. Class I M protein molecules share a surface-exposed, antigenic domain comprising the C repeat region defined for M6 protein. The C repeat region of M6 protein is located adjacent to the COOH-terminal side of the pepsin-susceptible site. In contrast, Class I M proteins display considerably less antigenic relatedness to the B repeat region of M6 protein, which lies immediately NH2-terminal to the pepsin site. Surface-exposed portions of Class II M proteins lack antigenic epitopes that define the Class I molecules. Studies in the 1970s demonstrated that M protein serotypes can be divided into two groups based on both immunoreactivity directed to an unknown surface antigen (termed M-associated protein) and production of serum opacity factor. These two groups closely parallel our current definition of Class I and Class II serotypes. Both classes retain the antiphagocytic property characteristic of M protein, and Class II M proteins share some immunodeterminants with Class I M proteins, although the shared determinants do not appear to be exposed on the streptococcal surface. Nearly all streptococcal serotypes associated with outbreaks of acute rheumatic fever express M protein of a Class I serotype. Thus, the surface-exposed, conserved C repeat domain of Class I serotypes may be a virulence determinant for rheumatic fever.
A randomised, double-blind, phase III study comparing SB2, an infliximab biosimilar, to the infliximab reference product Remicade in patients with moderate to severe rheumatoid arthritis despite methotrexate therapy

Objectives To compare the efficacy, safety, immunogenicity and pharmacokinetics (PK) of SB2 to the infliximab reference product (INF) in patients with moderate to severe rheumatoid arthritis (RA) despite methotrexate therapy. Methods This is a phase III, randomised, double-blind, multinational, multicentre parallel group study. Patients with moderate to severe RA despite methotrexate therapy were randomised in a 1:1 ratio to receive either SB2 or INF of 3 mg/kg. The primary end point was the American College of Rheumatology 20% (ACR20) response at week 30. Inclusion of the 95% CI of the ACR20 response difference within a ±15% margin was required for equivalence. Results 584 subjects were randomised into SB2 (N=291; 290 analysed) or INF (N=293). The ACR20 response at week 30 in the per-protocol set was 64.1% in SB2 versus 66.0% in INF. The adjusted rate difference was −1.88% (95% CI −10.26% to 6.51%), which was within the predefined equivalence margin. Other efficacy outcomes such as ACR50/70, disease activity score measured by 28 joints and European League against Rheumatism response were similar between SB2 and INF. The incidence of treatment-emergent adverse events was comparable (57.6% in SB2 vs 58.0% in INF) as well as the incidence of antidrug antibodies (ADA) to infliximab up to week 30 (55.1% in SB2 vs 49.7% in INF). The PK profile was similar between SB2 and INF. Efficacy, safety and PK by ADA subgroup were comparable between SB2 and INF. Conclusions SB2 was equivalent to INF in terms of ACR20 response at week 30. SB2 was well tolerated with a comparable safety profile, immunogenicity and PK to INF. Trial registration number NCT01936181.
Curriculum-Based Measurement of Reading: An Evaluation of Frequentist and Bayesian Methods to Model Progress Monitoring Data

Curriculum-Based Measurement of Oral Reading (CBM-R) is often used to monitor student progress and guide educational decisions. Ordinary least squares regression (OLSR) is the most widely used method to estimate the slope, or rate of improvement (ROI), even though published research demonstrates OLSR’s lack of validity and reliability, and imprecision of ROI estimates, especially after brief duration of monitoring (6-10 weeks). This study illustrates and examines the use of Bayesian methods to estimate ROI. Conditions included four progress monitoring durations (6, 8, 10, and 30 weeks), two schedules of data collection (weekly, biweekly), and two ROI growth distributions that broadly corresponded with ROIs for general and special education populations. A Bayesian approach with alternate prior distributions for the ROIs is presented and explored. Results demonstrate that Bayesian estimates of ROI were more precise than OLSR with comparable reliabilities, and Bayesian estimates were consistently within the plausible range of ROIs in contrast to OLSR, which often provided unrealistic estimates. Results also showcase the influence the priors had estimated ROIs and the potential dangers of prior distribution misspecification.
Prioritization of test case scenarios derived from activity diagram using genetic algorithm

Software testing involves identifying the test cases which discovers the errors in the program. However, the exhaustive testing is rarely impossible and very time consuming. In this paper, the software testing efficiency is optimized by identifying the critical path clusters. The test case scenarios are derived from the activity diagram and the testing efficiency is optimized by applying the genetic algorithm on the test data. The information flow metric is adopted in this work for calculating the information flow complexity associated with each node of activity diagram.
Trust Formation in University-Industry Collaborations in the U.S. Biotechnology Industry: IP Policies, Shared Governance, and Champions* ,†

Haggling over rights to potential inventions can be a major roadblock to successful university–industry (UI) collaborations. Yet such collaborations are critical for innovation in science-based industries. This study examines the roles of universities’ intellectual property (IP) policies and of shared governance for trust formation between academe and industry. The study also examines how UI champions moderate this process and how trust between university and industry partners affects UI collaboration outcomes. The analysis of survey data of 105 recent UI collaborations in the U.S. biotechnology industry indicates that the flexibility and transparency of university IP policies and shared governance by UI partners are both positively related to trust formation. The activities of UI champions amplify the positive effects of shared governance and at the same time reduce the importance of university IP policies for trust formation between UI partners. The amount of trust between partners is positively related to knowledge transfer and innovation performance. The findings suggest that despite widely reported industry concerns over the control of IP, UI research partners can develop a trustful environment and thereby plant the seeds for a successful collaboration. In order to enhance trust, companies should not only consider university IP policies, but also need to actively engage in shared governance with university partners. UI collaboration champions can help shift the attention of company managers from formal rules set by university IP policies toward shared project planning, coordination, and implementation with university partners.
An approach to supporting young people with autism spectrum disorder and high anxiety to re-engage with formal education – the impact on young people and their families

Abstract School refusal is an important factor impacting upon poor outcomes for adolescents and youth. Individuals with autism spectrum disorder (ASD) experience characteristic difficulties regarding social interaction and communication, rigidity of thinking and sensory sensitivities. These difficulties, coupled with the heightened anxiety that many on the spectrum experience, place them at particular risk of school refusal. This study investigates activity undertaken in one UK local authority, where provision was developed to help such students to re-engage with formal education. Data were collected at three points through the first year of the provision’s existence. Findings show all students were successfully supported to attend the provision and re-engage with formal education. Factors supportive of re-engagement are presented and considered in the light of an ecological model of support for school refusers and what is considered as ‘good practice’ in autism education. It is suggested that the factors identified are indicative of good practice across both areas of activity.
LEARNING ATTRIBUTIONAL TWO-TIERED DESCRIPTIONS OF FLEXIBLE CONCEPTS

Most real-life concepts are flexible • that is they lack precise defInitions and are context dependent. Representing and learning flexible concepts presents a fundamental challenge for ArtifIcial Intelligence. This paper describes a method for learning such concepts, which is based on a two­ tiered concept representation. In such a representation the flI'St tier, called the Base Concept Representation (BCR), describes the most relevant properties of a concept in an explicit, comprehensible, and efficient form. The second tier, called the Inferential Concept Interpreration (ICI), contains procedures for matching instances with concept descriptions. and inference rules defining allowable transformations of the concept under different contexts and exceptional cases. In the method. the BCR is obtained by first creating a complete and consistent concept description, and then optimizing it according to a general description quality criterion. The complete and consistent description is obtained by applying the AQ inductive learning methodology. The optimization process is done by a double level best fJrSt search. The lei is defmed in pan by a method of flexible matching and in pan by a set of inference rules. The method has been implemented in the AQTI-15 learning system, and experimental results show that such a two­ tiered concept representation not only produces simpler concept descriptions, but may also increase their predictive power.
Efficiency of the deposition mode ice nucleation on mineral dust particles

The deposition mode ice nucleation efficiency of various dust aerosols was investigated at cirrus cloud temperatures between 196 and 223 K using the aerosol and cloud chamber facility AIDA (Aerosol Interaction and Dynamics in the Atmosphere). Arizona test dust (ATD) as a reference material and two dust samples from the Takla Makan desert in Asia (AD1) and the Sahara (SD2) were used for the experiments at simulated cloud conditions. The dust particle sizes were almost lognormally distributed with mode diameters between 0.3 and 0.5 μm and geometric standard deviations between 1.6 and 1.9. Deposition ice nucleation was most efficient on ATD particles with ice-active particle fractions of about 0.6 and 0.8 at an ice saturation ratio S i S i S i . This indicates that deposition ice nucleation on mineral particles may not be treated in the same stochastic sense as homogeneous freezing. The suggested formulation of ice activation spectra may be used to calculate the formation rate of ice crystals in models, if the number concentration of dust particles is known. More experimental work is needed to quantify the variability of the ice activation spectra as function of the temperature and dust particle properties.
System and software architecture for autonomous surveillance robots in urban environments

In this paper, we propose a system of the security robot and its software architecture. The software architecture consists of two parts, which are an autonomous navigation part and a semantic perceptron part. Autonomous navigation software can drive a robot autonomously. An semantic perception software can perform the security missions. The security patrol scenario compromises three steps. In the first step, a robot collects information about the environments. Then the perceived structures are compared with previous information. If there is something to be changed or to be strange, a robot alarm to a supervisor. The proposed system and software architecture will make a robot to perform security missions.
Cross-Language Acquisition of Semantic Models for Verbal Predicates

This paper presents a semantic-driven methodology for the automatic acquisition of verbal models. Our approach relies strongly on the semantic generalizations allowed by already existing resources (e.g. Domain labels, Named Entity categories, concepts in the SUMO ontology, etc). Several experiments have been carried out using comparable corpora in four languages (Italian, Spanish, Basque and English) and two domains (FINANCE and SPORT) showing that the semantic patterns acquired can be general enough to be ported from one language to the other language.
Using co-creation as a pedagogic method for the professional development of students undertaking a BA (Hons) in Education Studies

Abstract Using an interpretive epistemology to explore the perceived benefits of using co-creation as a pedagogic tool with undergraduate students, this research focuses on a collaborative project between some first year undergraduate Education Studies students, a university Senior Lecturer and an Education Coordinator from a regional social enterprise organisation. The paper explores how the use of co-creation and creativity enhances the professional development and employability of graduates. Drawing on data collected from the students, this paper presents a range of perspectives and identifies how ‘meaningful’ problem-solving, co-creation and collaboration lead to transformative learning and enhanced student engagement. By doing this, we explore how complex challenges create agency for learning and develop critical thinking.
Application of Independent Component Analysis to EEG and fMRI data in separating distinct neural processes

Objectives: Independent Component Analysis (ICA) is a powerful data-driven technique, which separates EEG signals into functionally and physiologically distinct source activities. The aim of this study was to identify the neural sources, which contribute to scalp ERPs including N450. Methods: Dense-array EEG data were obtained from 20 participants performing numerical Stroop task. By applying ICA, artifacts were identified and removed. The remaining neural sources underwent clustering and further clusters’ ERP analysis. Results: While the traditional channels’ ERP analysis confirmed the occurrence of conflict-related N450 potential, the ICA results revealed two sources contributing to its variance: the mid-parietal cluster with source estimated in posterior cingulate cortex (PCC) and fronto-central cluster with source in anterior cingulate cortex (ACC). The former showed increased (prolonged) activity before the response for cognitively demanding trials, whereas the latter showed negative deflection after the response. PCC activity was decreased (shortened) before erroneous responses, while ACC showed strong error-related negativity. Conclusions: PCC is responsible for stimulus evaluation, while ACC is responsible for evaluating the action–outcome. Moreover, errors are committed due to insufficient stimuli processing within PCC. Significance: ICA proved to be reliable and effective method for ERP analysis, which shed new light into the brain potentials evoked by the numerical Stroop task. 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.