Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Automated Unit Test Generation Really Help Software Testers? A Controlled Empirical Study

Work on automated test generation has produced several tools capable of generating test data which achieves high structural coverage over a program. In the absence of a specification, developers are expected to manually construct or verify the test oracle for each test input. Nevertheless, it is assumed that these generated tests ease the task of testing for the developer, as testing is reduced to checking the results of tests. While this assumption has persisted for decades, there has been no conclusive evidence to date confirming it. However, the limited adoption in industry indicates this assumption may not be correct, and calls into question the practical value of test generation tools. To investigate this issue, we performed two controlled experiments comparing a total of 97 subjects split between writing tests manually and writing tests with the aid of an automated unit test generation tool, EvoSuite. We found that, on one hand, tool support leads to clear improvements in commonly applied quality metrics such as code coverage (up to 300% increase). However, on the other hand, there was no measurable improvement in the number of bugs actually found by developers. Our results not only cast some doubt on how the research community evaluates test generation tools, but also point to improvements and future work necessary before automated test generation tools will be widely adopted by practitioners.
FINITE ELEMENT METHOD FOR AMERICAN OPTION PRICING: A PENALTY APPROACH

The model for pricing of American option gives rise to a parabolic variational inequality. We first use penalty function approach to reformulate it as an equality problem. Since the problem is defined on an unbounded domain, we truncate it to a bounded domain and discuss error due to truncation and penalization. Finite element method is then applied to the penalized problem on the truncated domain. By coupling the penalty parameter and the discretization parameters, error estimates are established when the initial data in H 0 . Finally, some numerical experiments are conducted to confirm the theoretical findings.
Flow Logic for Process Calculi

Flow Logic is an approach to statically determining the behavior of programs and processes. It borrows methods and techniques from Abstract Interpretation, Data Flow Analysis and Constraint Based Analysis while presenting the analysis in a style more reminiscent of Type Systems. Traditionally developed for programming languages, this article provides a tutorial development of the approach of Flow Logic for process calculi based on a decade of research. We first develop a simple analysis for the π-calculus; this consists of the specification, semantic soundness (in the form of subject reduction and adequacy results), and a Moore Family result showing that a least solution always exists, as well as providing insights on how to implement the analysis. We then show how to strengthen the analysis technology by introducing reachability components, interaction points, and localized environments, and finally, we extend it to a relational analysis. A Flow Logic is a program logic---in the same sense that a Hoare’s logic is. We conclude with an executive summary presenting the highlights of the approach from this perspective including a discussion of theoretical properties as well as implementation considerations. The electronic supplements present an application of the analysis techniques to a version of the π-calculus incorporating distribution and code mobility; also the proofs of the main results can be found in the electronic supplements.
ChronoTape: tangible timelines for family history

An explosion in the availability of online records has led to surging interest in genealogy. In this paper we explore the present state of genealogical practice, with a particular focus on how the process of research is recorded and later accessed by other researchers. We then present our response, ChronoTape, a novel tangible interface for supporting family history research. The ChronoTape is an example of a temporal tangible interface, an interface designed to enable the tangible representation and control of time. We use the ChronoTape to interrogate the value relationships between physical and digital materials, personal and professional practices, and the ways that records are produced, maintained and ultimately inherited. In contrast to designs that support existing genealogical practice, ChronoTape captures and embeds traces of the researcher within the document of their own research, in three ways: (i) it ensures physical traces of digital research; (ii) it generates personal material around the use of impersonal genealogical data; (iii) it allows for graceful degradation of both its physical and digital components in order to deliberately accommodate the passage of information into the future.
Structural equivalence in the transcribed spacers of pre-rRNA transcripts in Schizosaccharomyces pombe.

The structure of the internal transcribed spacer 2 (ITS2) in Schizosaccharomyces pombe was re-evaluated with respect to phylogenetically conserved features in yeasts, features in other transcribed spacer regions as well as the binding of transacting factors which potentially play a role in ribosomal maturation. Computer analyses and probes for nuclease protection indicate a very simple core structure consisting of a single extended hairpin which includes the interacting termini of the mature 5.8S and 25S rRNAs. Comparisons with ITS2 sequences in greatly diverging organisms indicate that the same feature also can be recognized. This is especially clear in organisms that contain very short sequences in which the putative structures are much less ambiguous. Diversity between organisms is the result of changes in hairpin length as well as the addition of branched helices. Protein binding and gel retardation studies with the S.pombe ITS2 further indicate that, as observed in the 3" external transcribed spacer (ETS) and ITS1 regions, the extended hairpin is not only the site of intermediate RNA cleavage during rRNA processing but also a site for specific interactions with one or more soluble factors. Taken together with other analyses on transcribed spacer regions, the present data suggest that the spacer regions all may act in a similar fashion, not only to organize the maturing terminal sequences, but also serve to organize specific soluble factors possibly acting with snoRNAs or in a manner which is analogous with that of the free snoRNPs.
An advanced forensic readiness model for the cloud environment

Cloud computing facilitates the consumption of virtually infinite resources with reduced cost and less maintenance overheads. But the implicit elasticity property of cloud can be misused by adversaries to perform some attacks-either using the cloud or to the cloud itself. An astute intruder will follow certain anti-forensic measures to delete the traces of an attack. In that situation, the evidence identification and collection from the cloud becomes difficult which in turn reduces the efficiency of the entire forensic investigation. To handle this, we propose a model called improved Cloud Forensic Readiness (iCFR). We discuss iCFR in detail along with its practical implications when applied to the cloud environment.
Cosmic-ray driven dynamo in the interstellar medium of irregular galaxies

Context. Irregular galaxies are usually smaller and less massive than their spiral, S0, and elliptical counterparts. Radio observations indicate that a magnetic field is present in irregular galaxies whose value is similar to that in spiral galaxies. However, the conditions in the interstellar medium of an irregular galaxy are unfavorable for amplification of the magnetic field because of the slow rotation and low shearing rate. Aims. We investigate the cosmic-ray driven dynamo in the interstellar medium of an irregular galaxy. We study its efficiency under the conditions of slow rotation and weak shear. The star formation is also taken into account in our model and is parametrized by the frequency of explosions and modulations of activity. Methods. The numerical model includes a magnetohydrodynamical dynamo driven by cosmic rays that is injected into the interstellar medium by randomly exploding supernovae. In the model, we also include essential elements such as vertical gravity of the disk, differential rotation approximated by the shearing box, and resistivity leading to magnetic reconnection. Results. We find that even slow galactic rotation with a low shearing rate amplifies the magnetic field, and that rapid rotation with a low value of the shear enhances the efficiency of the dynamo. Our simulations have shown that a high amount of magnetic energy leaves the simulation box becoming an efficient source of intergalactic magnetic fields.
N-Order Weight Propagation on Collections with Related Documents

N-Order Weight Propagation (NWP) is an enhanced version of the Relevance Feedback (RF) Weight Propagation (WP) technique which moves more relevant documents higher up the ranking. The technique propagates positive and negative weights from documents judged relevant and not relevant respectively, to other documents. Further improvement in precision in WP is mostly attributed to the NWP technique where the documents that received the highest weights, and called top-set, propagate themselves to documents close by in vector similarity based on threshold Epsilon ε. NWP and WP were tested on Web collections with documents that cover various subjects. However, other non-Web collections exist and contain documents related to the same subject. These collections are usually used by regular users, researchers or students searching for information in research centers or libraries related to a specific subject such as Aerodynamic, Medical, and Information Science.etc. . Many of the RF techniques perform well in Web collections and fail in these collections with related documents. In this paper NWP and WP techniques are tested using Text REtrieval Conference (TREC) small test collections of articles from aerodynamics journals (Cranfield), articles from medical journals (Medline), documents on information Science (CISI), abstracts from Library and Information Science journals (LISA) and document titles on electronic engineering (NPL). Various models are used in the experiments such as the vector model using the tf.idf weighting scheme, and the probabilistic model using the DB2 model in the context of the Divergence from Randomness (DFR) framework. In this paper the constant Epsilon ε will be determined for the collections for the various models.
Analyzing the Evolution of Web Services Using Fine-Grained Changes

In the service-oriented paradigm web service interfaces are considered contracts between web service subscribers and providers. However, these interfaces are continuously evolving over time to satisfy changes in the requirements and to fix bugs. Changes in a web service interface typically affect the systems of its subscribers. Therefore, it is essential for subscribers to recognize which types of changes occur in a web service interface in order to analyze the impact on his/her systems. In this paper we propose a tool called WSDLDiff to extract fine-grained changes from subsequent versions of a web service interface defined in WSDL. In contrast to existing approaches, WSDLDiff takes into account the syntax of WSDL and extracts the WSDL elements affected by changes and the types of changes. With WSDLDiff we performed a study aimed at analyzing the evolution of web services using the fine-grained changes extracted from the subsequent versions of four real world WSDL interfaces. The results of our study show that the analysis of the fine-grained changes helps web service subscribers to highlight the most frequent types of changes affecting a WSDL interface. This information can be relevant for web service subscribers who want to assess the risk associated to the usage of web services and to subscribe to the most stable ones.
Dehydrin-like Proteins in the Necrotrophic Fungus Alternaria brassicicola Have a Role in Plant Pathogenesis and Stress Response

In this study, the roles of fungal dehydrin-like proteins in pathogenicity and protection against environmental stresses were investigated in the necrotrophic seed-borne fungus Alternaria brassicicola. Three proteins (called AbDhn1, AbDhn2 and AbDhn3), harbouring the asparagine-proline-arginine (DPR) signature pattern and sharing the characteristic features of fungal dehydrin-like proteins, were identified in the A. brassicicola genome. The expression of these genes was induced in response to various stresses and found to be regulated by the AbHog1 mitogen-activated protein kinase (MAPK) pathway. A knock-out approach showed that dehydrin-like proteins have an impact mainly on oxidative stress tolerance and on conidial survival upon exposure to high and freezing temperatures. The subcellular localization revealed that AbDhn1 and AbDhn2 were associated with peroxisomes, which is consistent with a possible perturbation of protective mechanisms to counteract oxidative stress and maintain the redox balance in AbDhn mutants. Finally, we show that the double deletion mutant ΔΔabdhn1-abdhn2 was highly compromised in its pathogenicity. By comparison to the wild-type, this mutant exhibited lower aggressiveness on B. oleracea leaves and a reduced capacity to be transmitted to Arabidopsis seeds via siliques. The double mutant was also affected with respect to conidiation, another crucial step in the epidemiology of the disease.
Weakly supervised strategies for natural object recognition in robotics

The paper aims at building a computer vision system for automatic image labeling in robotics scenarios. We show that the weak supervision provided by a human demonstrator, through the exploitation of the independent motion, enables a realistic Human-Robot Interaction (HRI) and achieves an automatic image labeling. We start by reviewing the underlying principles of our previous method for egomotion compensation [1], then we extend our approach removing the dependency on a known kinematics in order to provide a general method for a wide range of devices. From sparse salient features we predict the egomotion of the camera through a heteroscedastic learning method. Subsequently we use an object recognition framework for testing the automatic image labeling process: we rely on the State of the Art method from Yang et al. [2], employing local features augmented through a sparse coding stage and classified with linear SVMs. The application has been implemented and validated on the iCub humanoid robot and experiments are presented to show the effectiveness of the proposed approach. The contribution of the paper is twofold: first we overcome the dependency on the kinematics in the independent motion detection method, secondly we present a practical application for automatic image labeling through a natural HRI.
Constant Rate PCPs for Circuit-SAT with Sublinear Query Complexity

The PCP theorem (Arora et. al., J. ACM 45(1, 3)) says that every NP-proof can be encoded to another proof, namely, a probabilistically checkable proof (PCP), which can be tested by a verifier that queries only a small part of the PCP. A natural question is how large is the blow-up incurred by this encoding, i.e., how long is the PCP compared to the original NP-proof. The state-of-the-art work of Ben-Sasson and Sudan (SICOMP 38(2)) and Dinur (J. ACM 54(3)) shows that one can encode proofs of length n by PCPs of quasi-linear length that can be verified using a constant number of queries. In this work, we show that if the query complexity is relaxed to polynomial, then one can construct PCPs of linear length for circuit-SAT, and PCPs of length O(tlog t) for any language in NTIME(t). Our PCPs have perfect completeness and constant soundness. This is the first constant-rate PCP construction that achieves constant soundness with nontrivial query complexity. Our proof replaces the low-degree polynomials in algebraic PCP constructions with tensors of transitive algebraic geometry (AG) codes. We show that the automorphisms of an AG code can be used to simulate the role of affine transformations which are crucial in earlier high-rate algebraic PCP constructions. Using this observation we conclude that any asymptotically good family of transitive AG codes over a constant-sized alphabet leads to a family of constant-rate PCPs with polynomially small query complexity. Such codes are constructed for the first time for every message length.
Interactive Image Segmentation with Integrated use of the Markers and the Hierarchical Watershed Approaches

The watershed transform is a well-known approach for image segmentation. Watershed from markers and hierarchical watershed are derived from the watershed transform and are suitable for interactive image segmentation: in the former, the user can edit markers and control the segmentation result; in the latter, the user can select an image partition from a nested set of partitions. We investigate and propose ways to transition from one approach to other. Such transitions can be used to integrate both approaches in such a way that allow us to make full use of the strengths of both. We present examples that illustrate the use of the proposed transitions in conjunction with several interaction possibilities from both approaches.
Research in Transport and Congestion Control Mechanisms for Sensor Networks

This proposal presents a research agenda for investigating system support for reliable communication in sensor networks. The proposed research addresses two problems-transport issues and congestion control. Sensor networks come in a wide variety of forms, covering different geographical areas, being sparsely or densely deployed, using devices with a variety of energy and processing constraints, and implementing an assortment of sensing applications. One driving application of sensor networks is the reporting of conditions within a region where the environment abruptly changes due to an observed event, such as an habitat monitoring, target detection, earthquakes, floods, or fires. Sensor networks typically operate under an idle or light load and then suddenly become active in response to a detected or monitored event. Depending on the application this can result in the generation of large, sudden, and correlated data events that must be delivered to a small set of sinks without significantly disrupting the performance (fidelity) of the sensing application. The transport of such events is likely to lead to varying degrees of congestion in sensor networks, this is especially true for heterogeneous networks that support different applications performing various tasks in the field. In order to illustrate the congestion problem in sensor networks consider the following simple but realistic simulation scenario. In Figure 1 we show the impact of congestion on data dissemination in sensor networks for a moderate number of active sources with varying reporting rates. The ns-2 simulation results are for the well-known Directed Diffusion scheme [9] operating in a moderately sized 30-node sensor network using the IEEE 802.11 MAC with 6 active sources and 3 sinks. The 6 sources are randomly selected among the 30 nodes in the network and the 3 sinks are uniformly scattered across the
Statistical modelling of time series using non-decimated waveletrepresentations

This article proposes the use of time-ordered non-decimated wavelet or nondecimated wavelet packet transforms to provide flexible representations of a time series (explanatory). The resulting representations are then used as variables in a statistical model to provide predictions of another (response) time series. The statistical model provides valuable information about which components in the explanatory time series drive the response time series. To represent our explanatory time series we use a collection of basis functions known as wavelet packets. Each wavelet packet component of the exploratory time series corresponds to a particular linear combination of the time series and its lagged versions (regressive models). The construction of the wavelet packet transform ensures that all possible regressive models on a grid are rapidly computed. Hence our model fully explores “model-space” which may be parametrised in terms of the time-frequency plane. Our modelling methodology is illustrated with examples from two different arenas: (a) a wind power example using a generalized linear model relating wind speeds at one weather station to a time-ordered non-decimated wavelet packet transform of wind speeds and wind directions at another station; (b) a biomedical example shows how infant sleep states can be successfully classified using the time-ordered non-decimated wavelet packet transform of heart rate and linear discriminant analysis.
Integrating Machine Learning Techniques in a Guided Discovery Tutoring Environment: MEMOCAR

This chapter presents how Machine Learning Techniques can effectively contribute to improve the quality of interactions in Guided Discovery Tutoring Environments (GDTE) . We review several approaches to integrate Machine Learning in ITS. Most of these approaches use concept learning from examples to maintain a Student Model. We go along presenting an alternative use of induction techniques to learn concepts on the same data that are presented to the learner. We present on a concrete example how this approach is integrated in a GDTE called MEMOCAR, a Computer Aided Language Learning System for Chinese characters. Three main types of activity are identified in MEMOCAR: familiarization with Chinese characters, collaborative discovery of similarities between characters and exercises to test characters acquisition. The stage of familiarization is supported by exploration of hyperdata whilst collaborative discovery and exercises' diagnosis are supported by a tool based on CHARADE, a top-down induction system. Such integration offers a new alternative to the complex problem of making Guided Discovery Tutoring Environment more collaborative. Resume: Le theme aborde dans ce rapport est celui de l'utilisation de techniques d’apprentissage symbolique automatique (ASA) pour l'amelioration des interactions dans les environnements d’enseignement par la decouverte assiste par ordinateur. Nous presentons differents environnements integrant des techniques d'apprentissage symbolique automatique; principalement pour construire et/ou mettre a jour un modele de l'eleve. Nous presentons ensuite une integration originale de l’ASA dans le cadre d'un environnement dedie a l'apprentissage des caracteres chinois par la decouverte: MEMOCAR. Notre approche consiste a utiliser l'ASA pour faire apprendre au systeme sur les memes donnees que l'eleve, et a utiliser les resultats de cet apprentissage pour construire des interactions. Deux des principales activites du systeme MEMOCAR, la decouverte par l'apprenant de similarites entre caracteres et les exercices de test de l’apprentissage sont ainsi basees sur l'adaptation d'un algorithme inductif: CHARADE. Ce type d’integration de l'ASA dans les environnements d'apprentissage par la decouverte s'insere dans les recherches visant a rendre de tels environnements plus cooperatifs.
Polarization of τ lepton from scalar tau decay as a probe of neutralino mixing

The τ lepton arising from the scalar tau (τ̃ ) decay is naturally polarized. Pτ depends on the left–right mixing of the τ̃ and the gaugino–higgsino mixing of the neutralino. The polarization Pτ could be measured from the energy distribution of the decay products of τ at future e+e− colliders. A measurement of Pτ and of the τ̃ production cross section allows to determine both these mixing angles. E–mail: NOJIRIN@JPNKEKVX