Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Testing of Web Services

Software testers are confronted with great challenges in testing Web Services (WS) especially when integrating to services owned by other vendors. They must deal with the diversity of implementation techniques used by the other services and to meet a wide range of test requirements. However, they are in lack of software artifacts, the means of control over test executions and observation on the internal behavior of the other services. An automated testing technique must be developed to be capable of testing on-the-fly nonintrusively and nondisruptively. Addressing these problems, this paper proposes a framework of collaborative testing in which test tasks are completed through the collaboration of various test services that are registered, discovered, and invoked at runtime using the ontology of software testing STOWS. The composition of test services is realized by using test brokers, which are also test services but specialized in the coordination of other test services. The ontology can be extended and updated through an ontology management service so that it can support a wide open range of test activities, methods, techniques, and types of software artifacts. The paper presents a prototype implementation of the framework in semantic WS and demonstrates the feasibility of the framework by running examples of building a testing tool as a test service, developing a service for test executions of a WS, and composing existing test services for more complicated testing tasks. Experimental evaluation of the framework has also demonstrated its scalability.
Geographical Practice and Postmodern Destination Image

The present-day evolution of tourist imagery has received considerable critical attention in the tourist literature of late. What I propose to add to the already existing discussion on the topic is what I jokingly term a ‘continental’ perspective. The reader will be hard pressed to find an extensive presentation of relevant anglophone work as I believe that any such presentation on my part (coming, as I do, from an external vantage point) could only be partial at best; neither do I pretend to present a definitive vision of all other perspectives on the subject. My considerations are framed, rather, around a particular set of critiques emerging from recent Italian geographic thought; critiques that might serve as a counter-point to the debates of Englishlanguage theorists based in postmodern understandings of the crisis of representation and the way(s) in which it has served to shape our conceptions/perceptions of the world, of distance and of Otherness.
Licensed under Creative Commons Attribution Cc by Fingerprint Image Enhancement Using Adaptive Pre-processing of Data and K-means Segmentation

An adaptive fingerprint image enhancement method is proposed in this paper. Which is the improvement of existing methods based on contextual filtering. Adaptive system implies the parameters of this method are automatically adjusted based on input fingerprint image. Method is comprised into five processing blocks where all the five blocks are updated with new and best technologies. Hence over all system is noval. Processing blocks are preprocessing, global analysis, local analysis, matched filtering and image segmentation. a non linear dynamic range adjustment method is performed in preprocessing and local analysis blocks. Different form of order statistical filtering are applied in global analysis and matched filtering. A new technique called K-means segmentation is used for image segmentation. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of updated processing blocks is presented in evaluation part of this paper.
A framework for human recognition using a multimodel Gait analysis approach

Gait analysis applications are not only limited to medical, rehabilitation and sports, but it can also play a decisive role in security and surveillance as a behavioral biometric factor. Gait recognition is non-invasive and doesn't need any cooperation from subject in case of video surveillance. This paper presents a framework for human recognition based on gait without using markers or sensors using multimodel approach. The experiment presented in this paper was initially performed on 10 subjects, whose data were recorded at Raman Lab, MNIT Jaipur. In order to cross validate our approach, we have also used a standard gait database CASIA B.
Analysis Of Disk Workloads In Network File Server Environments

Workloads of network file server disk IO subsystems have very different characteristics than observed in time sharing or local IO systems described in the literature. In this study we provide a detailed analysis of disk workload traces collected from network file servers. Our results characterize file server disk traffic and give insights into file server disk access patterns. Measurements and statistics presented in this paper will aid designers and managers in developing and tuning disk subsystems for network file servers. Moreover, our results can be used by analysts to parameterize synthetic workloads for server subsystem studies.
XRIndex: a brief screening tool for individual differences in security threat detection in x-ray images

X-ray imaging is a cost-effective technique at security checkpoints that typically require the presence of human operators. We have previously shown that self-reported attention to detail can predict threat detection performance with small-vehicle x-ray images (Rusconi et al., 2012). Here, we provide evidence for the generality of such a link by having a large sample of naïve participants screen more typical dual-energy x-ray images of hand luggage. The results show that the Attention to Detail score from the autism-spectrum quotient (AQ) questionnaire (Baron-Cohen et al., 2001) is a linear predictor of threat detection accuracy. We then develop and fine-tune a novel self-report scale for security screening: the XRIndex, which improves on the Attention to Detail scale for predictive power and opacity to interpretation. The XRIndex is not redundant with any of the Big Five personality traits. We validate the XRIndex against security x-ray images with an independent sample of untrained participants and suggest that the XRIndex may be a useful aid for the identification of suitable candidates for professional security training with a focus on x-ray threat detection. Further studies are needed to determine whether this can also apply to trained professionals.
Variation in signal–preference genetic correlations in Enchenopa treehoppers (Hemiptera: Membracidae)

Fisherian selection is a within-population process that promotes signal–preference coevolution and speciation due to signal–preference genetic correlations. The importance of the contribution of Fisherian selection to speciation depends in part on the answer to two outstanding questions: What explains differences in the strength of signal–preference genetic correlations? And, how does the magnitude of within-species signal–preference covariation compare to species differences in signals and preferences? To address these questions, we tested for signal–preference genetic correlations in two members of the Enchenopa binotata complex, a clade of plant-feeding insects wherein speciation involves the colonization of novel host plants and signal–preference divergence. We used a full-sibling, split-family rearing experiment to estimate genetic correlations and to analyze the underlying patterns of variation in signals and preferences. Genetic correlations were weak or zero, but exploration of the underlying patterns of variation in signals and preferences revealed some full-sib families that varied by as much as 50% of the distance between similar species in the E. binotata complex. This result was stronger in the species that showed greater amounts of genetic variation in signals and preferences. We argue that some forms of weak signal–preference genetic correlation may have important evolutionary consequences.
Ecocritical contestations with neoliberalism: Teaching to (un)learn “normalcy”

This article seeks to address often overlooked cultural assumptions embedded within neoliberalism; specifically, the researchers explore what ecofeminist Val Plumwood describes as centric thinking, leading to a logic of domination. The authors argue that social justice educators and activists who are committed to critiquing neoliberalism must take into consideration the ways in which a logic of domination undergirds the unjust and destructive social and economic ideologies and policies that constitute neoliberalism. The authors examine and share pedagogical moments from experiences in teacher education seeking to: (a) challenge and disrupt dualistic thinking; (b) interrupt perceptions of hegemonic normalcy—referring to a socio-cultural process by which actions, behaviors, and diverse ways of interpreting the world are perceived by dominant society as “fitting in” and being socially acceptable; and, (c) contest false notions of independence—the degree to which an individual is perceived as able to meet their social and economic responsibilities on their own—as measures of success in schools and society. The authors detail how they work with(in) teacher education programs to introduce how an ecocritical approach, drawing from ecofeminist frameworks, identifies and examines the impacts of neoliberal policies and practices dominated by “free” market ideology. The authors assert that educators, especially teacher educators, can challenge harmful discourses that support the problematic neoliberal understandings about independence that inform Western cultural norms and assumptions. Concluding, the authors share a conceptualization for (un)learning the exploitation inextricable from the policies and practices of neoliberalism.
Data Personalization : a Taxonomy of User Profiles Knowledge and a Profile Management Tool

The goal of data personalization is to facilitate the expression of the need of a particular user and to enable him to obtain relevant information when he accesses an information system. The relevance of the information is defined by a set of criteria and preferences specific to each user or community of users. The data describing the users' interests and preferences is often gathered in the form of profiles. The content of a profile varies according to the target application and to its context (databases, information retrieval, digital libraries, multimedia applications). This paper elaborates a taxonomy of the most important knowledge composing a profile and proposes a generic model that can be instantiated and adapted for each specific application. The model is supported by a profile management tool (PMT) which also allows matching of profiles, aggregation of profiles and knowledge derivation from profiles. The last section of the paper lists some research challenges related to data personalization as identified in the APMD project initiated within the French Research Program on Data Masses.
Event Abstraction: Some Deenitions and Theorems Event Abstraction: Some Deenitions and Theorems

Debugging distributed applications is very di cult, due to a number of problems. To manage the inherent complexity of distributed applications, for example, the use of abstractions is proposed. Event abstractions group sets of events into one higher{ level event. Only event sets with certain properties guarantee proper abstraction. This paper examines two speci c event set structures in more depth: complete precedence abstractions and contractions. Its main results are as follows. First, two theorems that facilitate the algorithmic detection of complete precedence abstractions based on their density are given. Second, it is shown that contractions are not dense, making their algorithmic detection very di cult. And third, an additional structural requirement for contractions is derived to ensure their complete timestamping.
A hardware multicast routing algorithm for two-dimensional meshes

Multicast communication is a significant operation an multicomputer systems and can be used to support several other collective communication operations. Hardware implementation is a viable solution to develop a low latency multicast algorithm. In this paper, we present a new multicast routing algorithm for two-dimensional meshes. The algorithm uses wormhole routing mechanism and can send messages to any number of destinations within two start-up communication phases; hence the name, two-phase multicast (TPM) algorithm. The algorithm uses the base routing scheme used for unicast communication, thus minimizing the additional hardware support. The algorithm allows some intermediate nodes that are not in the destination set to perform multicast functions. This feature allows flexibility in multicast path selection and therefore improves the performance. A simulation study has been conducted to investigate the performance of the purposed TPM algorithm. The simulation results show that the proposed TPM algorithm performs significantly better for both contention-free and mixed-traffic conditions, when compared with the previously proposed multicast algorithms.
Using fuzzy grey correlation TOPSIS evaluation for service quality of online bookstore

The advancement of the internet technology had brought revolutions to our society. Numerous changes are taking place in today's business operation, sources of information, and transaction behaviors. As the computer hardware and dot com technology growing even more mature, more and more people are doing their everyday shopping on the internet. One of the examples is the establishment of the online bookstore. However, consumers have been increasing their demand toward the business of online bookstores; how do we provide satisfactory quality services to today's customers has become one of the important subjects confronting business owners of the online bookstores. Service itself exhibits qualities of being hard to define and measure. Therefore when consumers attempt to measure the quality of service performed, their judgments usually tend to be both subjective and fuzzy; hence, this research adopted the fuzzy multiple criteria decision making method. First we use the analytic hierarchy process to obtain the subjective weights for the evaluation criteria; the objective weights were also obtained through the entropy method; then subsequently the combinational weights were calculated. In the end, the one with the best service quality was derived and the overall online bookstore service quality was sorted. This research based on samples taken from five domestic online bookstores for analysis with the hope of providing useful future references for further evaluation and improvements.
Sexiness on Social Media

Using an experimental methodology, the present study assessed college men’s perceptions of a female peer who presented herself on Facebook in either a sexualized or nonsexualized manner. One hundred and seventeen college men viewed a Facebook profile with either a sexualized profile photo or a nonsexualized profile photo of a young woman and then evaluated the profile owner. They also reported on their dating attitudes. Results indicated that the sexualized profile owner was considered less physically attractive, less socially appealing, and less competent to complete tasks. Interest in dating and casual sex with the profile owner as well as general dating attitudes were largely not impacted by the type of profile photo. Findings suggest that using a sexualized profile photo on Facebook comes with some relational costs for young women. Strategies for educating young people about new media use and sexualization are discussed.
The Theory of Implementation. What Did We Learn?

In this survey we review the most important aspects of Implementation theory. We present the notions of a mechanism, a social choice correspondence and that of implementation. We first review the main contributions to implementation under dominant strategies namely the impossibility theorems that show the impossibility to implement efficient allocations when the preference domain is sufficiently large. Then we review Nash implementation and we show that a Maskin monotonic social choice correspondence satisfying no veto power can be implemented. Finally we review the case of Bayesian equilibrium, a kind of intermediate case between Nash and Dominant strategy implementation.
Large-Scale Electronic Structure Calculations with Real-Space grid Density-Functional Theory code

First-principles quantum-mechanical electronic structure calculations based on the density-functional theory (DFT) is an important ingredient of material sciences. The system sizes handled in usual DFT calculations are limited within a thousand of atoms due to the computational complexity of fully quantummechanical approaches. Real-space DFT (RSDFT) code, which has been developed in our group, is a promising application to overcome the size difficulty in first-principles quantum mechanical calculations. RSDFT is developed based on the real-space grid formulation of DFT [1] instead of conventional reciprocal-space formulation, in which frequent use of fast Fourier transform (FFT) is inevitable, so that it’s suitable for massively-parallel computers such as K computer [2]. Recently, we have performed electronic structure calculations for silicon nanowires with 10,000 to 1000,000 atoms by using RSDFT on the K computer with very high performance, and we got Gordon-Bell prize in 2011 with this work. In this paper, I review the first-principles electronic structure calculations in DFT and our program code RSDFT. In addition, I give an introduction on the technological importance of our target system silicon nanowire (SiNW), and discuss the results of the calculations for that system.
Student engagement in neo-liberal times: what is missing?

ABSTRACT Quality teaching is increasingly prioritized in higher education. One reason is that government funding requires students to succeed in their studies and be ready for employment. In response, educators throughout the Western world have generated large quantities of evidence-based, practical, often uncritical research about what works to improve teaching, learning and student success. Much of this research carries the label student engagement. But student engagement is a complex construct used to identify what students do, think and feel when learning and how teachers can improve that doing, thinking and feeling in instructional settings. Despite its extensive coverage of learning and teaching, the paper asks whether something is missing from student engagement. It recognizes the complexity of the engagement construct and examines its key ideas but identifies three missing elements: critique; learning agency/democracy; as well as purposes, knowledge and values that transcend powerful political discourses in neo-liberal times.
Unsteady Flow Visualization by Animating Evenly‐Spaced Streamlines

In recent years the work on vector field visualization has been concentrated on LIC‐based methods. In this paper we propose an alternative solution for the visualization of unsteady flow fields. Our approach is based on the computation of temporal series of correlated images. While other methods are based on pathlines and try to correlate successive images at the pixel level, our approach consists in correlating instantaneous visualizations of the vector field at the streamline level. For each frame a feed forward algorithm computes a set of evenly‐spaced streamlines as a function of the streamlines generated for the previous frame. This is achieved by establishing a correspondence between streamlines at successive time steps. A cyclical texture is mapped onto every streamline and textures of corresponding streamlines at different time steps are correlated together so that, during the animation, they move along the streamlines, giving the illusion that the flow is moving in the direction defined by the streamline. Our method gives full control on the image density so that we are able to produce smooth animations of arbitrary density, covering the field of representations from sparse, that is classical streamline‐based images, to dense, that is texture‐like images.
Toward autonomic grids: analyzing the job flow with affinity streaming

The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a dataset, albeit with quadratic computational complexity. This paper, motivated by Autonomic Computing, extends AP to the data streaming framework. Firstly a hierarchical strategy is used to reduce the complexity to O(N1+ε); the distortion loss incurred is analyzed in relation with the dimension of the data items. Secondly, a coupling with a change detection test is used to cope with non-stationary data distribution, and rebuild the model as needed. The presented approach StrAP is applied to the stream of jobs submitted to the EGEE Grid, providing an understandable description of the job flow and enabling the system administrator to spot online some sources of failures.
Locating the Source of Diffusion in Large-Scale Networks

How can we localize the source of diffusion in a complex network? Because of the tremendous size of many real networks-such as the internet or the human social graph-it is usually unfeasible to observe the state of all nodes in a network. We show that it is fundamentally possible to estimate the location of the source from measurements collected by sparsely placed observers. We present a strategy that is optimal for arbitrary trees, achieving maximum probability of correct localization. We describe efficient implementations with complexity O(N(α)), where α=1 for arbitrary trees and α=3 for arbitrary graphs. In the context of several case studies, we determine how localization accuracy is affected by various system parameters, including the structure of the network, the density of observers, and the number of observed cascades.
Pipelining and overlapping for MPI collective operations

Collective operations are an important aspect of the currently most important message-passing programming model MPI (message passing interface). Many MPI applications make heavy use of collective operations. Collective operations involve the active participation of a known group of processes and are usually implemented on top of MPI point-to-point message passing. Many optimizations of the used communication algorithms have been developed, but the vast majority of those optimizations is still based on plain MPI point-to-point message passing. While this has the advantage of portability, it often does not allow for full exploitation of the underlying interconnection network. In this paper, we present a low-level, pipeline-based optimization of one-to-many and many-to-one collective operations for the SCI (scalable coherent interface) interconnection network. The optimizations increase the performance of some operations by a factor of four if compared with the generic, tree-based algorithms.
A Review of DNA Microarray Image Compression

We review the state of the art in DNA micro array image compression. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution. We then summarize the compression results reported for these specific-specific image compression schemes. In a set of experiments conducted for this paper, we obtain results for several popular image coding techniques, including the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS, and JPEG2000 using zero wavelet decomposition levels are the best performing standard compressors, but are all outperformed by the best micro array-specific technique, Battiato's CNN-based scheme.