Miracle-dz/UniFAR-FTUs huggingface dataset:
Stages of Motivation for Virtual Voluntary Teams Online Engagement

Members of Virtual Voluntary Teams engage in massive virtual collaborations (MVC), in which large numbers of mostly unpaid contributors collectively collaborate to create new content. Motivation for such contributions has been an active area of research. We argue that what was previously considered a single, static and individual phenomenon, namely motivation for contribution to MVC, is in fact at least three separate but interrelated phenomena, with varying organizational and dynamic aspects. Using the theory of helping behavior as a framework and integrating stage models, work motivation and social movement theory, we propose a conceptual framework that distinguishes three separate models of motivations of participants in Virtual Voluntary Teams. The models distinguish motivations for three stages of participation (initial, sustained and meta) and include as well propositions concerning the effects of contributions on individual states and emergent states of the project as a whole and vice versa. The models provide implications for both researchers and practitioners who manage MVC projects.
Modeling Relational Data by the Adjacency Model

The World-Wide-Web contains data that cannot be constrained by a schema. Another source for such data is heterogenous corporate systems which are integrated in order to get better service for the users. Such data is commonly called semistructured data. Semistructured data has been under intensive investigation during the last few years. The main focus of interest has been on the development of new data models and new query languages. The most widely used data model for representing semistructured data is a graph-like or tree-like structure. The problem is to develop a model which could be all-embracing. In order to develop such a model we have introduced a new model called the Adjacency Model (AM). Our model is a general model which can be used to represent semistructured data as well as relational data.
ZEUS: A Toolkit for Building Distributed Multiagent Systems

The multiagent systems approach of knowledge- level cooperation between autonomous agents promises significant benefits to distributed systems engineering, such as enhanced interoperability, scalability, and reconfigurability. However, thus far, because of the innate difficulty of constructing multiagent systems, this promise has been largely unrealized. Hence there is an emerging desire among agent developers to move away from developing point solutions to point problems in favor of developing methodologies and toolkits for building distributed multiagent systems. This philosophy led to the development of the ZEUS Agent Building Toolkit, which facilitates the rapid development of collaborative agent applications through the provision of a library of agent- level components and an environment to support the agent-building process. The ZEUS toolkit is a synthesis of established agent technologies with some novel solutions to provide an integrated collaborative agent-building environment.
Omnidirectional camera calibration based on an approximate factorization method

A standard perspective image can be represented with part center area of an omnidirectional image from Micusik [12], can we generalize a few camera calibration methods from the perspective image to the omnidirectional image using part information in the center area of the omnidirectional image? In this paper, a novel omnidirectional camera calibration based on the approximate factorization method from multiple images is presented. This method first performs the approximate projective factorization algorithm using part of image points, which are less distorted than other feature points, in the center area of the omnidirectional image. Then, assuming zero skew and the principal point at the center of omnidirectional image, the initial value of camera intrinsic parameters can be computed. Last, the distortion intrinsic parameters are refined on minimizing the objective function. We demonstrate this method in experiment with synthetic data and real fisheye images.
Characterization of Autoantigens Targeted by Anti-Citrullinated Protein Antibodies In Vivo: Prominent Role for Epitopes Derived from Histone 4 Proteins

Anti-citrullinated protein antibodies (ACPA) have become an integral part of the clinical definition of rheumatoid arthritis, and are hypothesized to be important in the immunopathogenesis of this autoimmune disease. Several citrullinated proteins have been demonstrated to serve as candidate autoantigens for the ACPA, based on in vitro immune reactions between citrullinated peptides/proteins and RA sera. Yet it remains unclear whether the autoantigens identified in vitro are indeed directly and specifically targeted by the ACPA in vivo. Moreover, it is unclear whether ACPA present in RA sera are directed towards the same spectrum of autoantigens as the ACPA present within the synovial compartment. In this study, we isolated ACPA immune complexes from RA synovial fluids (SF) and sera by using immobilized cyclic citrullinated peptides (CCP3) based immune affinity, and characterized the proteins that are directly and specifically associated with them by mass spectrometry. The results demonstrate that four histone proteins are prominent ACPA autoantigens, with the frequency of detection being histone H4 (89%), H2B (63%), H3 (63%), and H2A (58%) in ACPA positive RA SF. We further demonstrate that a histone 4 peptide containing citrulline at position Cit39 was recognized by 100% of ACPA positive RA SF. An adjacent citrulline residue at Cit40 was recognized by 34% of ACPA positive RA SF. An H4 peptide containing Cit39-40 was recognized in the serum of 94% ACPA positive RA, 77% ACPA positive first-degree relatives (FDR) of RA patients, and 2.5% of healthy controls. The Cit39-40 peptide substantially blocked the ACPA reactivity in both SF and serum. Although the spectrum of ACPA we identified was limited to those isolated using immobilized CCP3 peptides, the findings indicate that H4 is a widely recognized RA autoantigen in both the synovial and serum compartments. The identification of this immunodominant ACPA epitope may be valuable in designing approaches to immune tolerance induction in RA.
Bad Luck or Bad Management? Emerging Banking Market Experience

A large number of bank failures occurred in transition countries during the 1990s and at the beginning of the 2000s. These were related to increases in non-performing loans and deteriorated cost efficiency of banks. This paper addresses the question of the causality between non-performing loans and cost efficiency in order to examine whether either of these factors is the deep determinant of bank failures. We extend the Granger-causality model developed by [Berger, A. , DeYoung, R. , 1997. Problem loans and cost efficiency in commercial banks. J. Banking Finance 21, 849-870] by applying GMM dynamic panel estimators on a panel of Czech banks between 1994 and 2005. Our findings support the bad management hypothesis, according to which deteriorations in cost efficiency precede increases in non-performing loans. Banking supervisors should consequently focus on enhanced cost efficiency of banks in order to reduce the likelihood of bank failures in transition countries.
Empirical evidence concerning AES

AES, the Advanced Encryption Standard, is one of the most important algorithms in modern cryptography. Certain randomness properties of AES are of vital importance for its security. At the same time, these properties make AES an interesting candidate for a fast nonlinear random number generator for stochastic simulation. In this article, we address both of these two aspects of AES. We study the performance of AES in a series of statistical tests that are related to cryptographic notions like confusion and diffusion. At the same time, these tests provide empirical evidence for the suitability of AES in stochastic simulation. A substantial part of this article is devoted to the strategy behind our tests and to their relation to other important test statistics like Maurer's Universal Test.
The VMC survey - IX. Pilot study of the proper motion of stellar populations in the LMC from 2MASS and VISTA data

We use multi-epoch near-infrared observations from the VISTA survey of the Magellanic Cloud system (VMC) to measure the proper motion of stars of the LMC, in one tile of 1.5 deg^2 centred at (alpha, delta) = (05:59:23.136, -66:20:28.68) and including the South Ecliptic Pole, with respect to their 2MASS position over a time baseline of ~10 years and from VMC observations only, spanning a time range of ~1 year. Stars of different ages are selected from the (J-Ks) vs. Ks diagram and their average coordinate displacement is computed from the difference between Ks band observations for stars as faint as Ks=19 mag. Proper motions are derived by averaging up to seven 2MASS-VMC combinations in the first case and from the slope of the best fit line among the seven VMC epochs in the second case. Separate proper motion values are obtained for variable stars in the field. The proper motion of ~40,000 LMC stars in the tile, with respect to ~8000 background galaxies, obtained from VMC data alone, is mu_alpha cos(delta) = +2.20+/-0.06 (stat) +/-0.29 (sys) and mu_delta = +1.70+/-0.06 (stat) +/-0.30 (sys) mas/yr. This value agrees with recent ground-based determinations in a nearby field but is larger than studies with the HST; the cause of this discrepancy may be due to additional systematic errors in the data. The LMC proper motion is also clearly distinct from the proper motion derived for stars of the Milky Way. The relative proper motion between the foreground stars and the LMC stars is ~5 mas/yr. Furthermore, we measure a decrease of the proper motion with increasing logarithm of stellar age for LMC stars. This study, based on just one VMC tile, shows the potential of the 2MASS-VMC and VMC-VMC combinations for a comprehensive investigation of the proper motion across the Magellanic system. (Abridged)
Basal Metabolic Rate: History, Composition, Regulation, and Usefulness

The concept of basal metabolic rate (BMR) was developed to compare the metabolic rate of animals and initially was important in a clinical context as a means of determining thyroid status of humans. It was also important in defining the allometric relationship between body mass and metabolic rate of mammals. The BMR of mammals varies with body mass, with the same allometric exponent as field metabolic rate and with many physiological and biochemical rates. The membrane pacemaker theory proposes that the fatty acid composition of membrane bilayers is an important determinant of a species BMR. In both mammals and birds, membrane polyunsaturation decreases and monounsaturation increases with increasing body mass and a decrease in mass‐specific BMR. The secretion and production of thyroid hormones in mammals are related to body mass, with the allometric exponent similar to BMR; yet there is no body size–related variation in either total or free concentrations of thyroid hormones in plasma of mammals. It is suggested that in different‐sized mammals, the secretion/production of thyroid hormones is a result of BMR differences rather than their cause. BMR is a useful concept in some situations but not in others.
The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy

Crowdsourcing is an increasingly popular, collaborative approach for acquiring annotated corpora. Despite this, reuse of corpus conversion tools and user interfaces between projects is still problematic, since these are not generally made available. This demonstration will introduce the new, open-source GATE Crowdsourcing plugin, which offers infrastructural support for mapping documents to crowdsourcing units and back, as well as automatically generating reusable crowdsourcing interfaces for NLP classification and selection tasks. The entire workflow will be demonstrated on: annotating named entities; disambiguating words and named entities with respect to DBpedia URIs; annotation of opinion holders and targets; and sentiment.
Inductive-Based Wireless Power Recharging System for an Innovative Endoscopic Capsule

Wireless capsule endoscopic devices are adopted for painless diagnosis of cancer and other diseases affecting the gastrointestinal tract as an alternative to traditional endoscopy. Although much work has been done to improve capsule performance in terms of active navigation, a major drawback is the limited available energy on board the capsule, usually provided by a battery. Another key shortcoming of active capsules is their limitation in terms of active functionalities and related costs. An inductive-based wireless recharging system for the development of an innovative capsule for colonoscopy is proposed in this paper; the aim is to provide fast off-line battery recovery for improving capsule lifecycle and thus reducing the cost of a single endoscopic procedure. The wireless recharging system has been properly designed to fit the dimensions of a capsule for colonoscopy but it can be applied to any biomedical devices to increase the number of times it can be used after proper sterilization. The current system is able to provide about 1 W power and is able to recharge the battery capsule in 20 min which is a reasonable time considering capsule operation time (10–15 min).
Joint Evaluation of Morphological Segmentation and Syntactic Parsing

We present novel metrics for parse evaluation in joint segmentation and parsing scenarios where the gold sequence of terminals is not known in advance. The protocol uses distance-based metrics defined for the space of trees over lattices. Our metrics allow us to precisely quantify the performance gap between non-realistic parsing scenarios (assuming gold segmented and tagged input) and realistic ones (not assuming gold segmentation and tags). Our evaluation of segmentation and parsing for Modern Hebrew sheds new light on the performance of the best parsing systems to date in the different scenarios.
Improving the Information Retrieval System through Effective Evaluation of Web Page in Client Side Analysis

To improve the information retrieval system for user, programmers have to learn a user's preferences accurately. In order to optimize retrieval accuracy, modeling the users appropriately based on their preferences and personalizing search according to each individual user are important. Implicit feedback information improves the user modeling process. The advantage of implicit modeling is effectively improving the user model without extra effort of user. Several implicit feedback features are used to develop user modeling process. We present a new model to find a user's preferences from click through behavior and using the exposed preferences to adapt the search engine's ranking function for improving search service. In this proposed model, the combination of viewed and stored document summaries is used.
Formal Verification of Arithmetic Functions in SmartMIPS Assembly ∗

In embedded systems, the recent trend is to manufacture processors with application-specific extensions. This makes it often necessary to write assembly programs to take advantage of the added hardware facilities. In such situations, formal verification is technically difficult because the programs in question manipulate data in a bitwise fashion, using non-standard specialized instructions, and under strict constraints for memory usage. In this paper, we propose an encoding of Hoare logic in the Coq proof assistant for formal verification of assembly programs that manipulate machine integers and bounded memory. Using this encoding, we formally verify arithmetic functions used in cryptography and written in SmartMIPS, an extension of the MIPS instruction set for smartcards.
Analytical Study of Pre-congestion notification (PCN) techniques

Maintaining the quality of service (QOS) and controlling the network congestion are quite complicated tasks. They cause degrading the performance of the network, and disturbing the continuous communication process. To overcome these issues, one step towards this dilemma has been taken in form of Pre-congestion notification (PCN) technique. PCN uses a packet marking technique within a PCN domain over IP networks. It is notified by egress node that works as guard at entry point of network. Egress node gives feedback to communicating servers whether rate on the link is exceeded than configured admissible threshold or within the limit. Based on this feedback, admission decisions are taken to determine whether to allow/block new coming flows or terminate already accepted. The actual question is about selection of right algorithm for PCN domain. In this paper, we investigate the analytical behavior of some known PCN algorithms. We make slide modifications in originality of PCN algorithms without disquieting working process in order to employ those within similar types of scenarios. Our goal is to simulate them either in highly congested or less congested realistic scenarios. On the basis of simulation done in ns2, we are able to recommend each PCN algorithm for specific conditions. Finally, we develop a benchmark that helps researchers and scientific communities to pick the right algorithm. Furthermore, the benchmark is designed to achieve specific objectives according to the users' requirements without congesting the network.
Mean Time to Meaningless: MTTDL, Markov Models, and Storage System Reliability

Mean Time To Data Loss (MTTDL) has been the standard reliability metric in storage systems for more than 20 years. MTTDL represents a simple formula that can be used to compare the reliability of small disk arrays and to perform comparative trending analyses. The MTTDL metric is often misused, with egregious examples relying on the MTTDL to generate reliability estimates that span centuries or millennia. Moving forward, the storage community needs to replace MTTDL with a metric that can be used to accurately compare the reliability of systems in a way that reflects the impact of data loss in the real world.
Rapid Discrimination of Malignant Breast Lesions from Normal Tissues Utilizing Raman Spectroscopy System: A Systematic Review and Meta-Analysis of In Vitro Studies

Purpose The aim of this study is to evaluate the diagnostic accuracy of Raman spectroscopy system in the detection of malignant breast lesions through a systemic review and meta-analysis of published studies. Methods We conducted a comprehensive literature search of PubMed and Embase from 2000 to June 2015. Published studies that evaluated the diagnostic performance of Raman spectroscopy in distinguishing malignant breast lesions from benign lesions and normal tissues were included in our study. The pooled sensitivity, specificity, diagnostic odds ratio, and the area under the curve of summary receiver-operating characteristic curves was derived. A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies guidelines was used to assess the quality of included studies. Results The initial search produced a total of 157 articles after removing duplicates. Nine studies (8 in vitro and 1 in vivo) were eligible in this meta-analysis. We analyzed the eight in vitro studies with 1756 lesions, the pooled sensitivity and specificity of Raman spectroscopy system for the diagnosis of malignant breast lesions were 0.92 (95% CI 0.86–0.96) and 0.97 (97% CI 0.93–0.98), respectively. Diagnostic odds ratio was 266.70 (95% CI 89.38–795.79), and the area under the curve of summary receiver-operating characteristic curves was 0.98 (95% CI 0.97–0.99). Significant heterogeneity was found between studies. There was no evidence of considerable publication bias. Conclusions Raman spectroscopy system is an optical diagnostic technology with great value for detecting malignant breast lesions. At the same time, it has advantages of being non-invasive, real-time, and easy to use. Thus it deserves to be further explored for intra-operatory breast tumor margin detection.
Engineering identity and communication outcomes: comparing integrated engineering and traditional public-speaking courses

ABSTRACT We assessed the effectiveness of an integrated engineering public-speaking class relative to a traditionally taught public-speaking class. The integrated class was designed to meet the growing science, technology, engineering, and mathematics communication needs and the fundamental Accreditation Board for Engineering and Technology and the National Communication Association student outcomes related to public speaking. Working within the Communication in the Disciplines theoretical framework, this study employed a quasi-experiment with both a test (engineering specific communication class) and control (traditional communication class) group; finding a significant increase with respect to attitude toward communication for students before and after the engineering specific class compared with the traditional class. Along with attitude toward communication, efficacy toward communication and being enrolled in the engineering specific class related positively to a sense of engineering identity for students at the end of class. For students enrolled in the engineering specific class, their sense of engineering identity was mediated through an improved attitude toward communication.
Enhancement of support vector machine for remote protein homology detection and fold recognition

Remote protein homology detection and fold recognition refers to detection of structural homology in proteins where there are small or no similarity in the sequence. The issues arise on how to accurately classify remote protein homology and fold recognition in the context of Structural Classification of Proteins (SCOP) hierarchy database and incorporate biological knowledge at the same time. Homology-based methods have been developed to detect protein structural classes from protein primary sequence information which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. We present a comprehensive method based on two-layer multiclass classifiers. The first layer is used detect up to superfamily and family in SCOP hierarchy by using optimized binary SVM classification rules directly to ROC-Area. The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that used to leverage the unlabeled data. It will detect up to fold in SCOP hierarchy. We evaluated the results obtained using mean ROC and mean MRFP. Experimental results show that our approaches significantly improve the performance of protein remote protein homology detection for all three different datasets (SCOP 1.53, 1.67 and 1.73). We achieved 0.03% improvements in term of mean ROC in dataset SCOP 1.53, 1.17% in dataset SCOP 1.67 and 0.33% in dataset SCOP 1.73 when compared to the result produced by state-of-the-art methods.