Using COTS Search Engines and Custom Query Strategies at CLEF

This paper presents a system for bilingual information retrieval using commercial off-the-shelf search engines (COTS). Several custom query construction, expansion and translation strategies are compared. We present the experiments and the corresponding results for the CLEF 2004 event

BIKE: Bilingual Keyphrase Experiments

This paper presents a novel strategy for translating lists of keyphrases. Typical keyphrase lists appear in scientific articles, information retrieval systems and web page meta-data. Our system combines a statistical translation model trained on a bilingual corpus of scientific papers with sense-focused look-up in a large bilingual terminological resource. For the latter, we developed a novel technique that benefits from viewing the keyphrase list as contextual help for sense disambiguation. The optimal combination of modules was discovered by a genetic algorithm. Our work applies to the French / English language pair

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Modellgetriebene Testfallkonstruktion durch Domänenexperten im Kontext von Systemfamilien

This work presents MTCC (Model-Driven Test Case Construction), an approach to the construction of acceptance tests by domain experts for testing system families based on feature models. MTCC is applied to the application domain of Digital Libraries. The basic hypothesis of this thesis is that the involvement of domain experts in the testing process for members of system families is possible on the basis of feature models and that such a testing approach has a positive influence on the efficiency and effectiveness of testing. Application quality benefits from the involvement of domain experts because tests specified by domain experts reflect their needs and requirements and therefore can serve as an executable specification. One prerequisite for the inclusion of domain experts is tooling that supports the specification of automated tests without formal modeling or programming skills. In MTCC, models of automated acceptance tests are constructed with a graphical editor based on models that represent the test-relevant functionality of a system under test as feature models and finite state machines. Feature models for individual testable systems are derived from domain-level systems for the system family. The use of feature models by the test reuse system of MTCC facilitates the systematic reuse of test models for the members of system families. MTCC is a Model-Driven test automation approach that aims at increasing the efficiency of test execution by automation while keeping independence from the implementation of the testee or the test harness in use. Because tests in MTCC are abstract models that represent the intent of the test independent from implementation specifics, MTCC employs a template-based code generation approach to generate executable test cases.Diese Arbeit stellt den Model-driven Test Case Construction (MTCC) Ansatz zur Konstruktion von wiederverwendbaren Akzeptanztests durch Domänenexperten im Kontext von Systemfamilien vor. Die Hypothese der vorliegenden Arbeit ist, dass die Einbeziehung von Domänenexperten in einen automatisierten Testprozess für die Systeme einer Systemfamilie auf Grundlage eines modellgetriebenen Ansatzes möglich ist und dass ein solcher Ansatz die Effektivität und Effizienz des Testens steigert. Die Qualität von Systemen profitiert von der Einbeziehung von Domänenexperten da deren Anforderungen durch die Tests wiedergegeben werden und diese somit als ausführbare Spezifikation dienen. Eine Voraussetzung für die Einbeziehung von Domänenexperten in das Testen ist eine Form der Werkzeugunterstützung die die Spezifikation von automatisierten Tests ohne Kenntnisse der Programmierung oder der formalen Modellierung gestattet. In MTCC werden abstrakte Modelle von Akzeptanztests mittels eines einfachen, graphischen Editors realisiert der seinerseits auf Modellen basiert die die testrelevant Funktionalität von Testlingen mittels Featuremodellen und endlichen Automaten darstellen. Featuremodelle repräsentieren einzelne Systeme der zu testenden Systemfamilie die von Domänenmodellen auf Ebene der der Systemfamilie abgeleitet sind. MTCC ist ein modellgetriebener Ansatz zur Automatisierung von Tests der der Steigerung der Effektivität der Testausführung dient und zugleich von den Spezifika der Implementierung des Testlings und der jeweiligen Software zur Testausführung entkoppelt. Da Tests in MTCC abstrakte Modelle sind, die die Semantik eines Tests unabhängig von Implementierungswissen ausdrücken, verwendet MTCC ein auf Templates basierendes Verfahren zur Generierung von ausführbaren Testskripten

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered

NRC-IIT's ILT Group at the Cross-Language Evaluation Forum

This technical report is an extended version of the article \u201cUsing COTS Search Engines and Custom Query Strategies at CLEF\u201d (Nadeau et al., 2004) published in the proceedings of CLEF 2004. It presents Sbire, a system for bilingual information retrieval using commercial off-the-shelf search engines (COTS). The development of Sbire was motivated by a participation in the CLEF evaluation. For this reason, some development presented in this report cannot be applied to a real-word crosslanguage information retrieval. For instance, we present how to build a query from a textual topic and this \u201cquery construction\u201d step is done by the user in a real-world IR system. The report emphasizes on our experiments in the CLEF environment as well as on the final Sbire architecture. It is mainly intended for developers of cross-lingual information retrieval system that aspire to participate in CLEF or similar events.Le pr\ue9sent rapport technique constitue une version \ue9tendue de l'article \uab Using COTS Search Engines and Custom Query Strategies at CLEF \ubb (Nadeau et al., 2004) publi\ue9 lors des d\ue9lib\ue9rations du CLEF 2004. Il pr\ue9sente Sbire, un syst\ue8me de r\ue9cup\ue9ration d'information bilingue faisant appel \ue0 des moteurs de recherche du commerce. Sbire a \ue9t\ue9 d\ue9velopp\ue9 dans le cadre d'une participation \ue0 l'\ue9valuation par le CLEF. C'est pourquoi certains des d\ue9veloppements d\ue9crits dans le pr\ue9sent rapport ne peuvent pas s'appliquer \ue0 la r\ue9cup\ue9ration d'information interlinguale du monde r\ue9el. Par exemple, nous montrons comment construire une requ\ueate \ue0 l'aide d'un sujet textuel; cette \ue9tape de \uab construction d'une requ\ueate \ubb est men\ue9e par l'utilisateur dans un syst\ue8me IR du monde r\ue9el. Le rapport met l'accent sur notre exp\ue9rience de l'environnement du CLEF de m\ueame que sur l'architecture finale du Sbire. Il s'adresse principalement aux d\ue9veloppeurs de syst\ue8mes de r\ue9cup\ue9ration d'information interlinguale qui aspirent \ue0 participer au CLEF ou \ue0 des \ue9v\ue9nements semblables.NRC publication: Ye

XXV Congreso Argentino de Ciencias de la Computación - CACIC 2019: libro de actas

Trabajos presentados en el XXV Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Río Cuarto los días 14 al 18 de octubre de 2019 organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y Facultad de Ciencias Exactas, Físico-Químicas y Naturales - Universidad Nacional de Río CuartoRed de Universidades con Carreras en Informátic

Using COTS Search Engines and Custom Query Strategies at CLEF. Cross-Language Evaluation Forum CLEF

Abstract. This paper presents a system for bilingual information retrieval using commercial off-the-shelf search engines (COTS). Several custom query construction, expansion and translation strategies are compared. We present the experiments and the corresponding results for the CLEF 2004 event.