Miracle-dz/UniFAR-FTUs huggingface dataset:
AN APPROACH FOR TESTING THE EXTRACT-TRANSFORM-LOAD PROCESS IN DATA WAREHOUSE SYSTEMS Submitted

AN APPROACH FOR TESTING THE EXTRACT-TRANSFORM-LOAD PROCESS IN DATA WAREHOUSE SYSTEMS Enterprises use data warehouses to accumulate data from multiple sources for data analysis and research. Since organizational decisions are often made based on the data stored in a data warehouse, all its components must be rigorously tested. In this thesis, we first present a comprehensive survey of data warehouse testing approaches, and then develop and evaluate an automated testing approach for validating the Extract-Transform-Load (ETL) process, which is a common activity in data warehousing. In the survey we present a classification framework that categorizes the testing and evaluation activities applied to the different components of data warehouses. These approaches include both dynamic analysis as well as static evaluation and manual inspections. The classification framework uses information related to what is tested in terms of the data warehouse component that is validated, and how it is tested in terms of various types of testing and evaluation approaches. We discuss the specific challenges and open problems for each component and propose research directions. The ETL process involves extracting data from source databases, transforming it into a form suitable for research and analysis, and loading it into a data warehouse. ETL processes can use complex one-to-one, many-to-one, and many-to-many transformations involving sources and targets that use different schemas, databases, and technologies. Since faulty implementations in any of the ETL steps can result in incorrect information in the target data warehouse, ETL processes must be thoroughly validated. In this thesis, we propose automated balancing tests that check for discrepancies between the data in the source databases and that in the target warehouse. Balancing tests ensure that the data obtained from the source databases is not lost or incorrectly modified by
A Speech-First Model for Repair Detection and Correction

Interpreting fttUy natural speech is an important goal for spoken language understanding systems. However, while corpus studies have shown that about 10% of spontaneous utterances contain selfcorrections, or REPAIRS, little is known about the extent to which cues in the speech signal may facilitate repair processing. We identify several cues based on acoustic and prosodic analysis of repairs in the DARPA Air Travel In.formation System database, and propose methods for exploiting these cues to detect and correct repairs.
Visual Query Suggestion *

Query suggestion is an effective approach to improve the usability of image search. Most existing search engines are able to automatically suggest a list of textual query terms based on users’ current query input, which can be called Textual Query Suggestion. This paper proposes a new query suggestion scheme named Visual Query Suggestion (VQS) which is dedicated to image search. It provides a more effective query interface to formulate an intent-specific query by joint text and image suggestions. We show that VQS is able to more precisely and more quickly help users specify and deliver their search intents. When a user submits a text query, VQS first provides a list of suggestions, each containing a keyword and a collection of representative images in a dropdown menu. If the user selects one of the suggestions, the corresponding keyword will be added to complement the initial text query as the new text query, while the image collection will be formulated as the visual query. VQS then performs image search based on the new text query using text search techniques, as well as content-based visual retrieval to refine the search results by using the corresponding images as query examples. We compare VQS with three popular image search engines, and show that VQS outperforms these engines in terms of both the quality of query suggestion and search performance.
Mechanics of Advanced Materials and Structures

In experiment-based validation, uncertainties and systematic biases in model predictions are reduced by either increasing the amount of experimental evidence available for model calibration—thereby mitigating prediction uncertainty—or increasing the rigor in the definition of physics and/or engineering principles—thereby mitigating prediction bias. Hence, decision makers must regularly choose between either allocating resources for experimentation or further code development. The authors propose a decision-making framework to assist in resource allocation strictly from the perspective of predictive maturity and demonstrate the application of this framework on a non-trivial problem of predicting the plastic deformation of polycrystals.
Certified Software

Certified software consists of a machine-executable progra m plus a formal machine-checkable proof that the software is free o f bugs with respect to a claim of dependability. The conventional w isdom is that certified software will never be feasible because the dependability of any real software must also rely on that of its unde rlying operating system and execution environment which is too low -level to be verifiable. In recent years, however, there have been ma ny advances in the theory and engineering of mechanized proof sys tems applied to verification of low-level code, including proofcarrying code, certified assembly programming, local reasoning and s eparation logic, certified linking of heterogeneous components, and certified or certifying compilation. In this article, I give an o verview of this exciting new field, focusing on both foundational ide as and key insights that make the work on certified software differ f rom traditional program verification systems. I will also descr ibe several exciting recent advances and challenging open problem s.
Health Multi-Terminology Portal

— Since the mid-90s, several quality-controlled health gateways were developed. In France, CISMeF is the leading health gate-way to index Internet resources from the main institutions, using the MeSH thesaurus and the Dublin Core metadata element set. Since 2005, the CISMeF team has switched from a mono-terminological world (MeSH) to a multi-terminology universe, with the integration of 27 terminologies in its information system. The principal objective of this work was to create a Health Multi-Terminology Portal (HMTP) connected to the CISMeF Terminology Data-base to search concepts among all the health terminologies available in French (or in English and translated in French) included in this portal and to browse it dynamically.
Experiences in Testing Automation of a Family of Functional- and GUI-similar Programs

This article presents experiences in the automation of a testing process. The main goal is the unified testing of not only one program, but a whole family of programs. The family is characterized by a common functionality and therefore similar GUI interfaces. The testing process integrates extraction of the application specific data from its executable and the usage of the capture and replay testing technique. The testing of various me mbers of application family is driven by a unified, common script. The approach is illustrated by a case study. As a family of applications under test a set of RSS aggregators was used. A unified test RSSscript was developed and verified in test experiments. The results of functional, performance and regression testing are presented. The benefits and limitations of the solution are discussed.
Effect of Evapotranspiration Rate on Almond Yield in California

Since 2011, California has been under drought conditions. These conditions have not only affected water availability for farmers, but also production. California’s second most valuable crop, almonds, has been affected by drought conditions. This study used three models (Model 1-3) to describe almond yield variability from year to year and almond yield variability within a year in Kern County, CA. The study evaluated 185 almond farms that were classified in three locations (east side, west side and north west side). The years of the study were 2011 (wet year) and 2013-2015 (drought condition years). Model 1 determined a functional regression between almond yield and annual evapotranspiration during the 4 years of the study. The R 2 was 7.9%, meaning low association between both variables and high unexplained variability (92.1%). Model 2 evaluated year to year variation. A regression function between almond yield and annual evapotranspiration after adjusting for location, precipitation, chilling hours and year was made. The R 2 of this model 62.6%, and all the variables used had a p2was higher than Model 1; however, there was high unexplained variability (47.4%). Model 3 evaluated within-year variation. A regression function between almond yield and annual evapotranspiration after adjusting for tree age and location (east, west and northwest side) was made for each year (2011 and 2013 -2015). Coefficient of variation of evapotranspiration and soil available water storage were analyzed as additional variables in Model 3; however, they were not introduced in Model 3 due to the low increase in R 2 in each year (2 of Model 3 for each year were, 60.4%, 49.7%, 53.8% and 53.2% for the years 2011, 2013-2015, respectively. Model 3 also had high unexplained almond yield variability in each year (39.6%-50.3%). This high unexplained variability leads to introduce additional variables to the functional regression model for further studies. Identifying these additional variables and having a functional regression model with high R 2 would lead to understand howlow evapotranspiration could potentially lead to a positive response on yield in drought conditions; thus, making farmers improve water use efficiency and hence, lowering production cost. However, the high unexplained variability clearly indicates that evapotranspiration is only one of many factors that influence yield. If improved yield is an important outcome, future studies must examine large- scale almond-producing farms with multiple agricultural system variables.
Automatic protocol field inference for deeper protocol understanding

Security tools have evolved dramatically in the recent years to combat the increasingly complex nature of attacks, but to be effective these tools need to be configured by experts that understand network protocols thoroughly. In this paper we present FieldHunter, which automatically extracts fields and infers their types; providing this much needed information to the security experts for keeping pace with the increasing rate of new network applications and their underlying protocols. FieldHunter relies on collecting application messages from multiple sessions and then applying statistical correlations is able to infer the types of the fields. These statistical correlations can be between different messages or other associations with meta-data such as message length, client or server IPs. Our system is designed to extract and infer fields from both binary and textual protocols. We evaluated FieldHunter on real network traffic collected in ISP networks from three different continents. FieldHunter was able to extract security relevant fields and infer their nature for well documented network protocols (such as DNS and MSNP) as well as protocols for which the specifications are not publicly available (such as SopCast) and from malware such as (Ramnit).
Effects, Capabilities, and Boxes From Scope-Based Reasoning to Type-Based Reasoning and Back

Reasoning about the use of external resources is an important aspect of many practical applications. Effect systems enable tracking such information in types, but at the cost of complicating signatures of common functions. Capabilities coupled with escape analysis offer safety and natural signatures, but are often overly coarse grained and restrictive. We present System C , which builds on and generalizes ideas from type-based escape analysis and demonstrates that capabilities and effects can be reconciled harmoniously. By assuming that all functions are second class , we can admit natural signatures for many common programs. By introducing a notion of boxed values , we can lift the restrictions of second-class values at the cost of needing to track degree-of-impurity information in types. The system we present is expressive enough to support effect handlers in full capacity. We practically evaluate System C in an implementation and prove its soundness.
The diffusion of new products in Latin America: a new comparative approach La difusion de productos nuevos en Latinoamerica: un novedoso acercamiento comparativo

Purpose – The purpose of this study is to examine the diffusion pattern of new products in Latin America, and to compare the results of principal component analysis with other descriptive approaches. Design/methodology/approach – The author studies the introduction of eight new products in nine Latin American countries, analysing their diffusion rate, market potential, adoption and take-off. He performs a principal component analysis and presents a biplot. The results obtained are compared using the Mantel test. Findings – The results indicate that the approaches used are complementary. The biplot describes 92 per cent of the explained variance. Except for a few cases, in general, the diffusion pattern in Latin America is mainly determined by cross-country wealth differences. Research limitations/implications – This study only analyses durable goods, and does not include any Central American country. Practical implications – This study helps companies to predict which will be the characteristics of the diffusion pattern of other new products and to forecast their future sales. Social implications – This study helps public actors to decide how to segment the Latin American market when implementing policies that encourage the diffusion of new products. Originality/value – This research extends our current knowledge on the diffusion of innovations in emerging and less developed countries. This is the first study to compare the characteristics of
A call-by-need lambda calculus

The mismatch between the operational semantics of the lambda calculus and the actual behavior of implementations is a major obstacle for compiler writers. They cannot explain the behavior of their evaluator in terms of source level syntax, and they cannot easily compare distinct implementations of different lazy strategies. In this paper we derive an equational characterization of call-by-need and prove it correct with respect to the original lambda calculus. The theory is a strictly smaller theory than the lambda calculus. Immediate applications of the theory concern the correctness proofs of a number of implementation strategies, e.g., the call-by-need continuation passing transformation and the realization of sharing via assignments.
Security Digital Evidence

Non-repudiation of digital evidence is required by various use cases in today’s business cases for example in the area of medical products but also in public use cases like congestion charges. These use cases have in common that at a certain time an evidence record is generated to attest for the occurrence of a certain event. To allow for non-repudiation of such an evidence record it is required to provide evidence on the used device itself, its configuration, and the software running at the time of the event. Digital signatures as used today provide authenticity and integrity of the evidence record. However the signature gives no information about the state of the Measurement Instrument at the time of operation. The attestation of the correct operation of the evidence collector is discussed in this paper and an implemented solution is presented.
Chapter IX Enhancing the Process of Knowledge Discovery in Geographic Databases Using Geo-Ontologies

This chapter introduces the problem of mining frequent geographic patterns and spatial association rules from geographic databases. In the geographic domain most discovered patterns are trivial, non-novel, and noninteresting, which simply represent natural geographic associations intrinsic to geographic data. A large amount of natural geographic associations are explicitly represented in geographic database schemas and geo-ontologies, which have not been used so far in frequent geographic pattern mining. Therefore, this chapter presents a novel approach to extract patterns from geographic databases using geo-ontologies as prior knowledge. The main goal of this chapter is to show how the large amount of knowledge represented in geo-ontologies can be used to avoid the extraction of patterns that are previ- ously known as noninteresting.
Fast Convergecast Over A Tree Based Routing Technology for Collision Avoidance in Wireless Sensor Network

In our task research the accompanying central inquiry - how quick would information be able to be gathered from a wireless sensor network arrange composed as tree? To address this, we investigate and assess various distinctive strategies utilizing practical simulation models under the many-to-one correspondence worldview known as converge cast. We initially consider time scheduling on a single frequency channel with the point of limiting the quantity of availabilities required (schedule length) to finish a converge cast. Next, we consolidate scheduling with transmission control to alleviate the impacts of obstruction, and demonstrate that while control helps in diminishing the schedule length under a single frequency, scheduling transmissions utilizing multiple frequencies is more efficient. To attain efficient communication between the nodes, interference must be considered. At that point, the information accumulation rate never again stays restricted by impedance yet by the topology of the routing tree To this end, we develop degree-compelled spanning trees and capacitated negligible spreading over trees, and show huge change in scheduling execution over various arrangement densities. Finally, we assess the effect of various obstruction and channel models on the schedule length.
Hybrid huberized support vector machines for microarray classification and gene selection

MOTIVATION The standard L(2)-norm support vector machine (SVM) is a widely used tool for microarray classification. Previous studies have demonstrated its superior performance in terms of classification accuracy. However, a major limitation of the SVM is that it cannot automatically select relevant genes for the classification. The L(1)-norm SVM is a variant of the standard L(2)-norm SVM, that constrains the L(1)-norm of the fitted coefficients. Due to the singularity of the L(1)-norm, the L(1)-norm SVM has the property of automatically selecting relevant genes. On the other hand, the L(1)-norm SVM has two drawbacks: (1) the number of selected genes is upper bounded by the size of the training data; (2) when there are several highly correlated genes, the L(1)-norm SVM tends to pick only a few of them, and remove the rest. RESULTS We propose a hybrid huberized support vector machine (HHSVM). The HHSVM combines the huberized hinge loss function and the elastic-net penalty. By doing so, the HHSVM performs automatic gene selection in a way similar to the L(1)-norm SVM. In addition, the HHSVM encourages highly correlated genes to be selected (or removed) together. We also develop an efficient algorithm to compute the entire solution path of the HHSVM. Numerical results indicate that the HHSVM tends to provide better variable selection results than the L(1)-norm SVM, especially when variables are highly correlated. AVAILABILITY R code are available at http://www.stat.lsa.umich. edu/~jizhu/code/hhsvm/.
International Journal of Advanced Research in Computer Science and Software Engineering

-Generally digital signature algorithms are based on a single hard problem like prime factorization problem, discrete logarithm problem, elliptic curve problem. If one finds solution of this single hard problem then these digital signature algorithms will no longer be secured and due to large computational power, this may be possible in future. There are many other algorithms which are based on the hybrid combination of prime factorization and discrete logarithms problem but different weaknesses and attacks have been developed against those algorithms. This paper also presents a new variant of digital signature algorithm which is based on two hard problems, prime factorization and discrete logarithm.
Landslide Triggering Induced By Barometric Pressure Changes

Unstable slopes often show time dependent movements which may be highly variable. In general these variations are often explained with changing water levels behind and in the slope resulting from rainfall. In engineering practice this dependency is commonly accepted. But experience shows, that in low permeable soils heavy rainfall will more likely run off at the surface rather than penetrating into the subsoil at a substantial rate. Thus the influence of rainfall on the piezometric line in low permeable soil should be expected to be minor and delayed in time. Because variations in pore water pressure control effective shear stress (Terzaghi and Peck, 1948) this accepted soil mechanical principle has been enhanced by including time dependent effects of external pressure variation and its influence on the soil below the piezometric line. So far accepted external pressure variation may originate in changes of water level (e. g. tidal effects in estuaries, water level draw down effects in reservoirs or waterways). In this paper it is proposed, that variations of barometric pressure acting on a slope may also influence pore water pressure conditions. Under certain conditions this effect may be decisive in triggering landslides. Extensive field measurements have been performed in unstable clay slopes. In this paper recent results will be presented of these measurements in progress as well as results of back calculations. Further information on this topic has already been published recently (Schulze and Köhler, 1999a; Köhler et. al.; 1999b; 1999c).
Organizational Climate's Role in Enterprise Social Software Usage: An Empirical Assessment

The focus of organizations' internal communication and collabora- tion infrastructures has recently been extended from conventional intranets to enterprise social software platforms (ESSPs). However, as ESSPs rely on con- tent creation and interaction through users, many platforms struggle. Previous research suggests that organizational climate plays an important role during IS adoption. Accordingly, our study quantitatively explores the influence of organ- izational climate on both contributive ESSP usage (knowledge sharing) and consumptive ESSP usage (knowledge consumption). Our results indicate that organizational climate - measured by the dimensions of trust, collaboration norms, and community identification - influences employees' ESSP usage be- haviors to a certain extent. In addition, collaboration norms appear to have a stronger impact on consumptive ESSP usage than on contributive ESSP usage. Lastly, trust was found to primarily support consumptive ESSP usage, not con- tributive usage.