Miracle-dz/UniFAR-FTUs huggingface dataset:
A multi-objective approach to search-based test data generation

There has been a considerable body of work on search-based test data generation for branch coverage. However, hitherto, there has been no work on multi-objective branch coverage. In many scenarios a single-objective formulation is unrealistic; testers will want to find test sets that meet several objectives simultaneously in order to maximize the value obtained from the inherently expensive process of running the test cases and examining the output they produce. This paper introduces multi-objective branch coverage.The paper presents results from a case study of the twin objectives of branch coverage and dynamic memory consumption for both real and synthetic programs. Several multi-objective evolutionary algorithms are applied. The results show that multi-objective evolutionary algorithms are suitable for this problem, and illustrates the way in which a Pareto optimal search can yield insights into the trade-offs between the two simultaneous objectives.
Comments on "A Multikey Secure Multimedia Proxy Using Asymmetric Reversible Parametric Sequences: Theory, Design, and Implementation"

For original paper see ibid., vol. 7, no. 2, p. 330-8 (2005). To protect multimedia data in audio and video streaming applications from unauthorized access by intruders, Yeung et al. recently proposed a multikey multimedia proxy with enhanced security features based on asymmetric reversible parametric sequences (ARPS) and the RSA technique. The authors claimed that their scheme provided a number of security features, including data confidentiality against member collusion. However, the security of their scheme is not as robust as they claim. In this paper, it is shown that the YLY-scheme remains vulnerable to collusion attacks
Influence of burial history on microstructure and compaction behaviour of Kimmeridge clay

The paper presents the results of laboratory compaction tests on two samples of the lower Kimmeridge Clay Formation obtained from two different outcrop localities in the UK. The purpose of the testing programme is to study the compaction behaviour of an argillaceous sediment from the same parent material but with different diagenetic histories. Since most available data are for clays subjected to moderate effective stresses (less than 20 MPa), the compaction laboratory tests were carried out to as high as 120 MPa, corresponding to about 9 km burial. The microstructure of the tested materials was studied in a scanning electron microscope before and after compaction testing to evaluate the effects of diagenesis on the microstructure of argillaceous sediments. The two tested materials have, in the past, been subjected to different burial depths and degrees of chemical precipitationand cementation. Several pieces of evidence are shown of the effects of mechanical and chemical processes on the compaction behaviour of argillaceous materials. Mechanical compaction can only account for part of the porosity loss and increase in stiffness of argillaceous materials during burial. Mechanical compaction produces an anisotropic rearrangement of clay particles in uncemented sediments. Once chemical diagenesis has taken place, chemical cementation preserves the microstructure of the sediment and only minor changes in clay particle arrangements take place during further mechanical compaction, even when very high stresses are used. Also, it is seen that chemical processes reduce the permeability more strongly than mechanical compaction.
Detecting Discontinuities in Large Scale Systems

Cloud providers and data centers rely heavily on forecasts to accurately predict future workload. This information helps them in appropriate virtualization and cost-effective provisioning of the infrastructure. The accuracy of a forecast greatly depends upon the merit of performance data fed to the underlying algorithms. One of the fundamental problems faced by analysts in preparing data for use in forecasting is the timely identification of data discontinuities. A discontinuity is an abrupt change in a time-series pattern of a performance counter that persists but does not recur. Analysts need to identify discontinuities in performance data so that they can a) remove the discontinuities from the data before building a forecast model and b) retrain an existing forecast model on the performance data from the point in time where a discontinuity occurred. There exist several approaches and tools to help analysts identify anomalies in performance data. However, there exists no automated approach to assist data center operators in detecting discontinuities in the first place. In this paper, we present and evaluate our proposed approach to help data center analysts and cloud providers automatically detect discontinuities. A case study on the performance data obtained from a large cloud provider and performance tests conducted using an open source benchmark system show that our proposed approach provides on average precision of 84% and recall 88%. The approach doesn't require any domain knowledge to operate.
Multivariate mixture models to describe longitudinal patterns of frailty in American seniors

Large, longitudinal, multivariate population surveys are increasingly common. Many analytic methods inspect changing rates of individual outcomes but ignore heterogeneous subpopulations that may exist. In this document I propose two analytical methods which extend group-based trajectory models to multivariate outcomes. I use group-based longitudinal finite mixture models (i.e., developmental trajectory models) to identify and describe latent subpopulations for the multivariate outcomes of interest. I use the National Long Term Care Survey, which measures various disabilities in American elderly from 1982 to 2004, as an example. Both models of longitudinal pathways of disability clearly illustrate the various frailty patterns in latent subpopulations of American seniors. These models recognize that within group some disabilities may coincide while others may follow at a later time. Finally, I demonstrate latent frailty patterns with illustrative plots that show multivariate temporal patterns within latent class.
Contributions to record linkage for disclosure risk assessment

This book is about reidentification methods, and, more especifically, about record linkage. This study is motivated on the need to assess in an accurate way the risk of disclosure within the fields of privacy preserving data mining (PPDM) and statistical disclosure control (SDC). Data protection methods are constructed so that disclosure is avoided when data is published. Nevertheless, before data publication, risk should be assessed as accurately as possible. Record linkage is one of the approaches to compute this risk. It is, in fact, a computational approach for risk assessment based on some assumptions about the information an intruder has. An important characteristic of record linkage methods is that they are flexible enough to be used in a large number of different scenarios. For example, they can be either used for assessing risk of data protected adding some noise, or used for assessing risk when data has been generated synthetically. In this book, record linkage methods are discussed, and new approaches are described. Specific methods are proposed. They are methods that take advantage of the available information on the protection methods. The author later shows their effectiveness in comparison to standard ones. The book also shows how the analysis of the results of risk assessment via record linkage can be used to develop new methods for data protection. In short, new data protection methods are resiliant to some of the attacks of record linkage.
Effects of the El Niño–southern oscillation on temperature, precipitation, snow water equivalent and resulting streamflow in the Upper Rio Grande river basin

Snowmelt runoff dominates streamflow in the Upper Rio Grande (URG) basin of New Mexico and Colorado. Annual variations in streamflow timing and volume at most stations in the region are strongly influenced by the El Niño–southern oscillation (ENSO) through its modulation of the seasonal cycles of temperature and precipitation, and hence on snow accumulation and melting. After removing long‐term trends over the study period (water years 1952–99), the dependence of monthly temperature, precipitation, snow water equivalent (SWE) at snowcourse stations, and streamflow throughout the URG on ENSO was investigated using composite analyses of the detrended residuals and through dependence of the residuals on the Climate Prediction Center southern oscillation index during the preceding summer and fall. The climate of La Niña years was found to differ significantly from either El Niño or neutral years. Moreover, significant climatological ENSO‐related effects are confined to certain months, predominantly at the beginning and end of the winter season. In particular, March of La Niña years is significantly warmer and drier than during either El Niño or neutral years, and November of El Niño years is significantly colder and wetter. Differences in temperature and precipitation lead to significant differences in SWE and streamflow in the URG between the three ENSO phases. Copyright © 2004 John Wiley & Sons, Ltd.
Moho depth and three-dimensional P and S structure of the crust and uppermost mantle in the Eastern Mediterranean and Middle East derived from tomographic inversion of local ISC data

SUMMARY ∼82 000 P and S arrival times from ∼3000 sources recorded by ∼250 seismic stations from the revised ISC catalogue are employed to study a circular area of 6 ◦ radius centred on the Dead Sea. We use the linearized tomographic approach based on the rays constructed in a 1-D spherical velocity model and corrected for the Moho depth variation and relief. All the sources were relocated. As the result of simultaneous iterative inversion we get 3-D P and S velocity anomalies in the crust and uppermost mantle, Moho depth and corrected source parameters. The resulting images fit well with the existing tectonic elements in the study area. In the crust, a narrow P and S low-velocity anomaly marks the position of the Dead Sea Transform (DST) that is interpreted as sediments in the shallower layer and a zone of fractured and deformed rocks in the middle and lower crust. There is a narrow (50 km wide) band of thickening of the crust along the DST in the Arava valley between the Red Sea and Dead Sea and some 100 km north of the Dead Sea. This zone may be associated with the minimum of the lithospheric strength and, therefore, explain the location of the DST in the Arava Valley. The velocity anomalies under the crust and the map of the Moho depth clearly distinguish the oceanic (Levant basin) and continental types of crust (Asia Minor, Zagros, Cyprus and Eratosthenes Mount). Verification of the results takes an important part in this study. Inversions of different starting models and independent processing of data subsets show high robustness of the results. Synthetic tests clearly show the limits of the resolving power of the inversion with the existing data set.
Path Creation with Digital 3D Representations: Networks of Innovation in Architectural Design and Construction

We examine the wake of innovations in architecture and construction propelled by the adoption of digital three-dimensional (3D) representations. We examine IT induced innovations and their consequences as a path creation process within a network of professional communities involved in architect Frank Gehry's projects. We report the results of a retrospective case study of 3D representationenabled innovation during the design and construction of the Peter B. Lewis Building at Case Western Reserve University. Our analysis suggests that the consequences of a complex information technology innovation like the use of digital 3D representations cannot be adequately understood as a singular adoption event. Instead, a more holistic and integrated view of the innovation process is required. One that views innovation as a continuous path creation by multiple actors sharing practices and feedback across professional communities while they appropriate 3D representations. Information technology innovation involves multiple agents' mindful deviations from established paths of practices and resource use. We observe that the use of 3D representations breaks down the traditional loosely coupled system in construction that relied on two-dimensional (2D) representations to share information between different contractors. These representations essentially black-boxed and hid most information about how to build the building or how different parts of design interrelate with one another. To effectively adopt and appropriate the potential of 3D representations requires that traditionally isolated actors during design and construction need to be brought together in a tightly coupled system. This system is arranged around rich and complex boundary objects enabled by the digital 3D representations and their transformations.
Uprising microblogs: a bayesian network retrieval model for tweet search

We investigate in this paper the problem of accessing to real-time information and we propose a Bayesian network retrieval model for tweet search. The proposed model interprets tweet relevance as a conditional probability and estimates it using different sources of evidence. In particular, we introduce a social search model that considers, in addition to text similarity measures, the microblogger's influence, the time magnitude and the presence of hashtags. To evaluate our model, we conducted a series of experiments on the TREC Tweets2011 corpus. Experiments with "Arab Spring" topic set show that both of social and temporal features improve tweet search for different types of queries. Final results show also that our model outperforms other traditional information retrieval baselines.
Classification of multiple sclerosis lesion evolution patterns a study based on unsupervised clustering of asynchronous time-series

Based on weekly up to monthly follow-up of MS patients over one year with T2-weighted magnetic resonance images, a new clustering scheme is proposed to automatically identify lesions sharing similar temporal behaviors. The proposed method, based on spatiotemporal mean-shift and dynamic time warping, allows to detect intra and inter-patient similarities in lesion evolution patterns, which provides a quantitative approach towards the understanding of lesion evolution heterogeneity.
Test-driven specification: paradigm and automation

This paper introduces test-driven specification, whereby the specification process is aided by the use of test cases. We also introduce an automated tool, the test-driven specification assistant (TDSA), which supports this approach. Test cases reveal specification anomalies such as incorrectness, incompleteness and ambiguity. Specification-based test coverage criteria are applied to reveal deficiencies in the set of test cases. Decision tables are used as a lightweight specification language capable of modeling black-box and Mills' state box specifications.
Remote sensing for mapping of suspended sediments in Krishna Bay Estuary, Andhra Pradesh, India

Abstract The objective of this study was to use Landsat-MSS digital data converted for the mapping of suspended sediments (SS) in the Krishna Bay Estuary. The relation between Landsat-MSS radiance values for all four bands and measured values of suspended solid concentrations were quantified using simple linear and multiple regression equations. An optimum and best fitted equation was chosen based on the percentage error of estimation, Chi2 values and simplicity of the model. This calibrated regression model was then applied to map the SS(mgl−1) concentration for the entire study area. It is shown that Landsat-MSS data can be used successfully to quantify suspended sediment concentrations in this geographical area and possibly in other areas which have similar environmental and climatic conditions, if the regression equation is tested using an independent data set.
Hierarchical Multi-Bottleneck Classification Method And Its Application to DNA Microarray Expression Data

The recent development of DNA microarray technology is creating a wealth of gene expression data. Typically these datasets have high dimensionality and a lot of varieties. Analysis of DNA microarray expression data is a fast growing research area that interfaces various disciplines such as biology, biochemistry, computer science and statistics. It is concluded that clustering and classification techniques can be successfully employed to group genes based on the similarity of their expression patterns. In this paper, a hierarchical multi-bottleneck classification method is proposed, and it is applied to classify a publicly available gene microarray expression data of budding yeast Saccharomyces
Efficacy of Cervical Spine Mobilization Versus Peripheral Nerve Slider Techniques (Neurodynamics) in Cervicobrachial Pain Syndrome

Objective: The objective of this study was to compare the efficacy of cervical spine mobilization versus peripheral nerve slider techniques (neurodynamics) incervicobrachial pain syndrome. Study Design: The study design was arandomized interventional study. Place and Duration of Study: This study was conducted at the Armed Forces Institute of Rehabilitation Medicine (AFIRM), Rawalpindi from August 2014 to January 2015. Materials and Methods: Forty patients (n=40) were included by using purposive sampling technique. Patients of Age 30 to 60 years with Radiating neck pain, Limited ROM of neck and Pain persisting for more than 2 months, were included in study. Then randomly divided into two groups, each group contains 20 participants. One group was treated with neck mobilization and other was treated with neurodynamic treatment protocol. Pain and Active Range of Motion (AROM) was measured by Visual analog scale (VAS) and Inclinometer respectively. Neck Disability Index was also used. Patients were assessed before and after six week intervention. Data was analyzed on SPSS 20 and Independent t Test was used to compare the results of two groups. Results: Pain was measured on VAS, the mean of Mobilization and Neurodynamics were (2.0+1.892 vs. 4.8+2.397) respectively. There is significant (p
Issue of Event Sequence in time of Distributed Intrusion Detection System

Distributed intrusion detection system (DIDS) is one of important devices for information security. In this field, how to improve detection rate is one of key issues. In this paper, the importance of event sequence in time is presented. Then, we discuss three factors, i.e. timestamp precision, time synchronization and network delay, which effect detection rate on the view of event sequence in time. On the three aspects, timestamp precision is the key to keep internal event sequence, time synchronization is the base of correcting event sequence among computers, and that network delay makes time-series analysis not true. Accordingly, we address some methods, i.e. raising timestamp precision, active self- adapting time synchronization algorithm and state turnabout mechanism. Experiments indicate that anyone of three measures can elevate detection performance to a certain extent. If they all are adopted, better detection results are revealed.
The UNSW submission to INTERSPEECH 2014 compare cognitive load challenge

Speech based cognitive load estimation is a new field of research. Due to this relative ‘lack of maturity’, a single best approach to building cognitive load estimation systems has not been established yet. The primary aim of this submission is to report the performance of various basic utterance level classification frameworks developed using important elements of state-of-the-art speaker recognition systems. This may lead to a suitable basis for future cognitive load estimation systems. As a consequence of being a part of a challenge, it is expected that these frameworks will be compared to a much larger number of alternative approaches than what would otherwise be possible. In keeping with this focused aim, the GMM supervector approaches along with some variants are utilised. The systems outlined in this paper include a frame-level MFCC-GMM system along with utterance level GMMsupervector-SVM, GMM-ivector-SVM and GMM-JFA-SVM systems. The best combined system has an accuracy (UAR) of 66.6% as evaluated on the challenge development set and 63.7% as evaluated on the test set.
Estimating source parameters from deformation data, with an application to the March 1997 earthquake swarm off the Izu Peninsula, Japan

We have applied two Monte Carlo optimization techniques, simulated annealing and random cost, to the inversion of deformation data for fault and magma chamber geometry. These techniques involve an element of randomness that permits them to escape local minima and ultimately converge to the global minimum of misfit space. We have tested the Monte Carlo algorithms on two synthetic data sets. We have also compared them to one another in terms of their efficiency and reliability. We have applied the bootstrap method to estimate confidence intervals for the source parameters, including the correlations inherent in the data. Additionally, we present methods that use the information from the bootstrapping procedure to visualize the correlations between the different model parameters. We have applied these techniques to GPS, tilt, and leveling data from the March 1997 earthquake swarm off of the Izu Peninsula, Japan. Using the two Monte Carlo algorithms, we have inferred two sources, a dike and a fault, that fit the deformation data and the patterns of seismicity and that are consistent with the regional stress field.
MJ: a rational module system for Java and its applications

While Java provides many software engineering benefits, it lacks a coherent module system and instead provides only packages (which are primarily a name space mechanism) and classloaders (which are very low-level). As a result, large Java applications suffer from unexpected interactions between independent components, require complex CLASSPATH definitions, and are often extremely complex to install and maintain. We have implemented a module system for Java called MJ that is implemented with class loaders, but provides a much higher-level interface. High-level properties can be specified in a module definition and are enforced by the module system as new modules are loaded. To experimentally validate the ability of MJ to properly handle the complex module inter-relationships found in large Java server systems, we replaced the classloader mechanisms of Apache Tomcat 4.1. 18 [27] with 30 MJ modules. The modified Tomcat is functionally identical to the original, but requires no CLASSPATH definitions, and will operate correctly even if user code loads a different version of a module used by Tomcat, such as the Xerces XML parser [31]. Furthermore, by making a small change to the Java core libraries enabled by MJ, we obtained a 30% performance improvement in a servlet microbenchmark.
Finding leaders from opinion networks

This paper is motivated to utilize results from opinion mining to facilitate social network analysis. We introduce the concept of Opinion Networks and propose a PageRank-like algorithm, named OpinionRank, to rank the nodes in an opinion network. This proposed approach has been applied to real-world datasets and initial experiments indicate that the sentiment information is helpful for finding leaders of online communities and that the OpinionRank method outperforms benchmark methods that ignore sentiment information.