Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic query expansion method based on pay-as-yougo fashion for graph model

The search requirements of users are usually vague and unclear, and the traditional keyword query method is difficult to obtain satisfactory query results. Therefore, the semantic query expansion technology uses semantic information to modify and extend the initial search requirements of users, to obtain more comprehensive and accurate query results. However, the construction cost of the extended query graph is high, and the existing research is difficult to adjust and optimize the user’s query demand in time, resulting in a poor query effect. To solve the above problems, this paper proposes a semantic expansion method based on pay-as-you-go fashion for graph model: Firstly, the initial query graph is constructed according to the user’s search requirements, and the semantic similarities between the search requirements and knowledge bases are calculated; Then, the extended sets of attribute-values and edges are generate by sorting the similarity value in descending order; Finally, all or part of the elements in the extended set are combined to generate the extended query graph. This method can dynamically adjust and optimize the initial query based on the relevant semantic information in time, and improve the query efficiency and effect.
Spectrum Reconstruction Algorithm based on Resource Partition Allocation in Elastic Optical Networks

In order to solve the problem of spectrum fragment in elastic optical networks, a spectrum reconstruction algorithm based on resource partition allocation is proposed in this paper. Services are classified according to the different frequency of the services that occupy different number of frequency slots in the networks. Region division of spectrum resources is made according to the type of services and services are allocated in the corresponding spectrum partitions. When defragmenting in the networks, we reconstruct the spectrum for different regions with comprehensively considering the factors in time domain and frequency domain, integrating current resources and preventing the generation of spectrum fragment in the future. Simulation results show that the algorithm can reduce the degree of network fragmentation and reduce traffic blocking.
Nickel(II) complex covalently anchored on core shell structured SiO2@Fe3O4 nanoparticles: a robust and magnetically retrievable catalyst for direct one-pot reductive amination of ketones

A robust and efficient core shell structured magnetically retrievable nickel nanocatalytic system was fabricated via the covalent immobilization of 2-acetyl furan on the surface of an amine functionalized silica coated magnetic nanosupport followed by its metallation with nickel acetate. The newly synthesized magnetic silica based organic–inorganic hybrid nanocatalyst (Ni–ACF@Am–SiO2@Fe3O4) was systematically affirmed using several physico-chemical characterization tools such as FT-IR, XRD, VSM, SEM, TEM, EDS, ED-XRF and AAS. Thereafter, the catalytic performance of this Ni–ACF@Am–SiO2@Fe3O4 nanocatalyst was investigated in the one-pot reductive amination of ketones using NaBH4 as the reductant under neat conditions. The developed core shell magnetic silica based nickel nanocatalyst successfully afforded a structurally diverse range of secondary amines with high turnover frequency (TOF) and excellent conversion percentage. Additionally, it was found that this catalyst could not only be retrieved from the reaction vessel within a fraction of seconds using an external magnet but also be recycled for multiple runs without any discernible loss in its activity that rendered this protocol superior to all the previously established methodologies for the one-pot synthesis of substituted amines. Besides, some of the other fascinating features of this methodology that made it a potential candidate for addressing various economic and environmental concerns were ambient reaction conditions, broad substrate scope, simple workup procedure, shorter reaction time and cost effectiveness.
Schriftenreihe Des Fachgebietes Neuroinformatik Der Technischen Universitt at Ilmenau Implementation and Comparison of Growing Neural Gas, Growing Cell Structures and Fuzzy Artmap Herausgeber

Acknowledgement The authors want to thank Soeren Wohlfahrt and Thomas Vesper for their contributions to this technical report. Soeren Wohlfahrt implemented Fuzzy ARTMAP and ran the benchmark tests on this network presented in this report. In addition, he contributed through several discussions to some of the critcs about Fuzzy ARTMAP. Thomas Vesper implemented Growing Neural Gas and Growing Cell Structures and ran preliminary tests on these networks. Finally, we want to thank Dimitrij Surmeli for his thoroughly proofreading and Professor H. -M. Gross for comments on an earlier version of this report.
Special Issue on Self-Managing Databases: Lessons Learned and Challenges Ahead

We describe our experience in the development of self-managing technologies in Sybase SQL Anywhere, a full-featured relational database system that is commonly embedded with applications developed by Independent Software Vendors (ISVs). We illustrate how SQL Anywhere’s embeddability features work in concert to provide a robust data management solution in zero-administration environments.
Cache-Oblivious Query Processing Position paper

We propose a radical approach to relational query processing that aims at automatically and consistently achieving a good performance on any memory hierarchy. We believe this automaticity and stableness of performance is at times more desirable than some peak performance achieved through careful tuning, especially because both database systems and hardware platforms are becoming increasingly complex and diverse. Our approach is based on the cache-oblivious model, in which data structures and algorithms are aware of the existence of a multi-level memory hierarchy but do not assume any knowledge about the parameter values of the hierarchy, such as the number of levels in the hierarchy, the capacity and the block size of each level. Since traditional database systems are intrinsically aware of these parameters, e.g., the memory page size, our cache-oblivious approach requires us to rethink the query processing architecture and implementation. In this position paper, we present the architectural design of our cache-oblivious query processor, EaseDB, discuss the technical challenges, and report our initial results.
Image Segmentation Using Texture

This paper details work on the preprocessing of phytoplankton images for classificaltion by neural networks. Firstly there is a review of the Gabor filter set generated for textural feature extraction. Secondly there is a review of discriminant analysis and the main classification techniques The third section of the repoprt details the analysis and the results of data extracted from eight test images of Dinophysis (two specimen from each of the four species). The analyses show that the background clusters well and that figure ground segmentation is possible. The analysis also shows that there is a high classification accuracy for segmenting the images upon the basis of both species and subtype (fin, theca etc.). The final section concludes that the texture segmentation using the Gabor filters is a valid preprocessing technique that will enable the group to expad the current successful neural network classifier.
This information is current as Human Macrophages following Chronic NOD 2 Stimulation of Proinflammatory Cytokine Downregulation SOCS 3 and MAPKs Contributes to Dependent Regulation of − TAM Receptor

Print ISSN: 0022-1767 Online ISSN: 1550-6606. Immunologists, Inc. All rights reserved. Copyright © 2015 by The American Association of 9650 Rockville Pike, Bethesda, MD 20814-3994. The American Association of Immunologists, Inc., is published twice each month by The Journal of Immunology at U iv of C alifnia-San D igo Seials/B om ed L ib 0699 on Jauary 2, 2015 hp://w w w jim m unl.org/ D ow nladed from
Immersive Audio Rendering Algorithms Project Role in Support of Imsc Strategic Plan Discussion of Methodology Used

Additive background noise and convolutive noise in the form of reverberation are two common types of noises in audio conferencing and hands-free telecommunication environments. In this project, we developed an efficient two-step adaptive noise reduction scheme to suppress these two types of noise. In particular, an Affine Projection Algorithm (APA) based Adaptive Noise Canceller is employed to reduce the additive noise in the first step, while a Constant Modulus Algorithm (CMA) based blind deconvolution technique is used to dereverberate the “wet” signal in the second step. To reduce the computational burden and accelerate the convergence speed, both causal and non-causal real-valued Delayless Subband Adaptive Filters (DSAF) were studied and integrated with the APA and CMA separately. The utilization of this real-valued Delayless SAF architecture can eliminate the substantial transmission delay existed in the conventional subband processing methods, while the real-valued subband signals produced by Single Sideband modulated filterbanks can make the implementation more efficient. Our results show that this method can achieve better performance with lower computational complexity and avoid transmission delay, thus it is very suitable for real time applications.
Selection of Samples for Calibration

In NIR spectroscopy, good multivariate calibration equations are crucial for accurate chemical determinations. To obtain such multivariate equations one needs adequate statistical models, efficient estimation procedures and good calibration data. Most of the previous contributions to this column have been focussed on calibration methods and models and very little has been said about selection of samples for calibration. Therefore we have decided that the present and some forthcoming articles in this column will be devoted to this problem. The present article will discuss briefly some general principles behind the selection of data for calibration. We will concentrate on calibration data for linear calibration models, Le models of the type
SURVIVAL ANALYSIS FOR UNOBSERVED EVENT

In clinical studies subjects are often followed for varying lengths of time for the occurrence of some outcome (event) of interest. Standard approaches for comparing the proportion of events observed in two or more treatment groups at the end of follow-up (chi-square testing) are then typically used to analyze the data. However, these approaches ignore the fact that the individual observation periods for subjects may vary. If this variation is considerable, survival analysis may be used to evaluate such data even if exact event times are never recorded. At the end of each subject’s observation period, one notes whether or not the subject has experienced the event of interest. Hence the binary data generated this way represent either left-censored (if the event has occurred) or rightcensored (if the event has not occurred) observations from the survival distribution governing the exact but unobserved event times. These data can be used to model the hazard function from flexible survival distributions such as the Weibull. Moreover, model fitting is easily achieved using standard software for generalized linear models. Convenient adjustment for covariates other than treatment is also possible. This approach will be discussed in some detail and an example given to illustrate the method.
A component-based approach for supporting functional and non-functional analysis in control loop design

One of the main issues in developing modern control systems is how to design control loops in such a way that functional requirements as well as real-time attributes can be analyzed during designtime. Nowadays, control loops are often constructed performing a modular approach by means of libraries of building blocks that can be considered as components of a control system. Although component models that support predictability of the system behavior there exist, they are found to be inappropriate for the control systems application domain. In fact, they assume to not deal with control loops (i.e., control flow feedbacks) which cause problems with predictability of the system behavior. This has lead to a real need of a component-based approach for designing control loops and supporting predictability of the behavior of the designed system. In this paper, we present a possible component-based approach for supporting functional and non-functional analysis of control loops during design-time. Moreover, we outline an overall view of the component-based development framework which implements our approach.
Analysis : On Feature Engineering , Classifier Performance and Realtime Tracking

Millions of people publish their opinions about a variety of topics on the microblogging platform Twitter every day. Analyzing this stream of opinions automatically can be useful in various ways. For example, as a customer one might like to get an idea of the general opinion about a product before buying it. Another example are politicians who may be interested in the sentiment towards their political party, especially shortly before an election. As a first step towards such an automated analysis, algorithms which are able to determine the sentiment of a tweet are needed. This thesis provides a high quality testset to evaluate such algorithms, analyzes and compares various methods to classify a tweet’s sentiment, and finally illustrates how a web based realtime sentiment tracking application, which tracks the sentiment towards given keywords, can be implemented.
Hypoglycemic Activity of Carica Papaya Leaf Aqueous Extract in Normal and Diabetic Mice

The hypoglycemic effect of aqueous extract of Carica papaya leaves was investigated on alloxan monohydrate induced diabetic mice. The blood glucose levels were measured at 0, 2, 4 and 6 h after the treatment. The aqueous extract of C. papaya (250 mg/kg) reduced the blood glucose of normal mice from 85.50±1.22 to 64.67±3.27 mg/dl, 3 h after oral administration of the extract (P<0.05). It also significantly lowered blood glucose level in alloxan induced diabetic mice from 331.67±4.90 to 130.33±6.53 mg/dl, 6h after oral administration of the extract (P<0.05).
Studyon InfluenceLawsofStrataBehaviors for ShallowCoal Seam Mining beneath Gully Terrain

School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China State Key Laboratory of Coal Resources in Western China, Xi’an University of Science and Technology, Xi’an 710054, China Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology (Beijing), Beijing 100083, China National Demonstration Center for Experimental Safe Coal Mining and Geological Guarantee Education, China University of Mining and Technology (Beijing), Beijing 100083, China General Technology Group Engineering Design Co., Ltd., Jinan 250031, China
DFT Study of Chemisorbed Atomic Oxygen Inducing Co Segregation in CoNi(111) Alloy

Chemisorbed atomic oxygen inducing Co segregation in CoNi (111) alloy is studied using periodic self-consistent density functional theory (DFT) calculations. In particular, the coverage dependence and possible adsorption-induced segregation phenomena are addressed by investigating segregation energies (the difference in calculated total free energy between surface sites and bulk-like sites) of isolated Co in CoNi (111) alloy. In agreement with previous experimental and theoretical investigations, segregation of Co is found to be oxygen-coverage dependent. While for ‘clean’ CoNi (111), Co prefers to be in the bulk. In the presence of more than 2/9 ML of oxygen, Co segregates to the surface. The analysis of oxygen adsorption trends and surface electronic structures explains the change in the local atomic arrangement which is expected to occur on the surface of alloys under reaction conditions. Our predictions for the high oxygen coverage cases are particularly relevant in underlining the importance of segregation phenomena to the hydrogen evolution performance of CoNi alloy hydrogen evolution electrode.
Use of liquid MDI products for polyurethane reactive hot-melt resins

Reactive hot-melt resins typically consist of pure MDI (methylenediphenyl diisocyanate) prepolymers of adipate-based polyester polyols. These materials are solids at room temperature and thus harder to process than liquids. This study evaluates the effectiveness of liquid versions of MDI for reactive hot melts. Pure (close to 100% p,p') MDI served as the control, and two liquid formulations-MDI containing 50% o,p' isomer, MDI containing carbodiimide adduct-were tested for rheological properties, peel strength, cure rate, melt point, open time, and long-term stability. The liquid formulations were comparable with the pure MDI control in all physical and thermal properties, with the following exceptions. The MDI with 50% o,p' had a lower melt viscosity and cure rate, but it also showed on increased in shelf and pot stability. The adduct-modified MDI showed a moderate increase in melt viscosity and 15-min peel strength, increased hardness, and significantly improved shelf stability. Application: Reactive hot-melt adhesives, based on polyurethane chemistry, are finding utility in high-performance hot-melt applications where superior green strength and ultimate thermosetting properties are required.
Career Success, Career Commitment and Organizational Support: Empirical Evidence from the Banking Sector

Career success plays a significant role in the progress of any employee's career. The current study attempted to investigate how organizational support and career commitment can influence career success. Through using the quantitative approach, data were collected from 415 employees in the Islamic banking sector of Bahrain. The structural equation modeling results using Smart PLS 2.0 reported a significant relationship between organizational support and career success. Accordingly, the study also reported a significant correlation between career commitment and career success. The findings highlight how career success is essential, and the role of organizational support and career commitment towards harnessing it. The paper presents implications for theory and practice, followed by the scope for future studies. Key-words: Career Success, Career Commitment, Organization Support.
Forschungsberichte der Fakultät IV – Elektrotechnik und Informatik Automatic Analysis of Malware Behavior using Machine Learning

Malicious software—so called malware—poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffective, such that millions of hosts in the Internet are infected with malware in form of computer viruses, Internet worms and Trojan horses. While obfuscation and polymorphism employed by malware largely impede detection at file level, the dynamic analysis of malware binaries during run-time provides an instrument for characterizing and defending against the threat of malicious software. In this article, we propose a framework for automatic analysis of malware behavior using machine learning. The framework allows for automatically identifying novel classes of malware with similar behavior (clustering) and assigning unknown malware to these discovered classes (classification). Based on both, clustering and classification, we propose an incremental approach for behavior-based analysis, capable to process the behavior of thousands of malware binaries on a daily basis. The incremental analysis significantly reduces the run-time overhead of current analysis methods, while providing an accurate discovery and discrimination of novel malware variants.
Elasticity and anisotropy of Fe3C at high pressures

Abstract Using static electronic structure calculations, we determine the equation of state, the full elastic constant tensor and the sound wave velocities of cementite (Fe3C) at pressures up to 410 GPa. Fe3C is ferromagnetic (fm) at ambient pressures. Upon compression, the magnetic moment of the Fe atoms are gradually lost and, at around -62 GPa, Fe3C becomes non-magnetic (nm). We find that the pressurevolume results for the Fe3C (fm) phase are well represented by a Vinet equation of state with K0fm = 183 GPa, K0 = 5.9, and V0fm = 151.6 Å3 and that of the Fe3C (nm) phase are well represented by a Vinet equation of state with K0nm = 297 GPa, K0′ = 4.9, and V0nm = 143.2 Å3. A third-order Birch-Murnaghan equation of state formulation for the Fe3C (nm) phase yields similar parameters with K0nm = 304 GPa, K0 = 4.5, and V0nm = 143.3 Å3. At pressures relevant to the Earth’s inner core, the full elastic constant tensor of Fe3C (nm) reveals significant P-wave anisotropy (-10%). A crystal preferred orientation with the [110] directions of Fe3C aligned along the pole axis would be required to explain the inner core anisotropy. Comparing, pure hcp Fe and iron carbides with varying stoichiometry, we find that the shear wave velocity decreases linearly with the increasing C content.
A SOFTWARE FRAMEWORK FOR OPEN STANDARD SELF-MANAGING SENSOR INFRASTRUCTURE FOR WEB SERVICES

To improve the usability and reliability of grid-based applications, instrumentation middleware services are now proposed and widely accepted as a means to monitor and manage grid users’ applications. A plethora of research works now exist focusing on the design and implementation of a range of software instrumentation techniques (Lee et al. 2003, Reilly and Taleb 2002) to enhance general systems’ management including; QoS, fault-tolerance, systems recovery and load-balancing. However, management and assurance concerns of related to sensors and actuation (effectors) for grid and web services environment received little to no attention. This paper presents a lightweight framework for the generation, deployment and discovery of different types of sensors and actuators together with two associated description languages namely; monitor session description language and sensor and actuation description langue. These are used respectively to describe the set of deployed sensors and actuators in a given self-managing grid infrastructure, and to define monitoring properties and policies of a given target service/application. In addition, the paper presents a developed sensor framework to provide the basic systems awareness fabric layer for managing decentralised web services. The paper concludes with a case study illustrating the use of the sensor framework and monitoring job request to manage and schedule the sensor’s operation.
UNIVERSIDAD DE MURCIA

Tyrosinase has two types of enzymatic activities: the hydroxylation of monophenols to o-diphenols (monophenolase activity) and oxidation of o-diphenols to o-quinones (diphenolase activity). The action on o-diphenols involves two substrates: oxygen and o-diphenol, while the mechanism proposed is a Uni Uni Bi Bi ping-pong. In this contribution, we demonstrate experimentally that there is a kinetically preferred pathway, which translates into the appearance of curves of initial velocity vs. initial diphenol concentration shows inhibition by an excess of substrate, while sigmoid curves are obtained when the initial velocity vs. initial oxygen concentration are graphed. However, the action mechanism of the enzyme on monophenols, which is more complex because it involves three substrates (monophenol, oxygen and o-diphenol), does behave differently from the hyperbolic behaviour as regards the initial velocity vs. initial monophenol concentration, results that can be explained if the limiting step in the action of tyrosinase is the hydroxylation of monophenol to o-diphenol.