Automated story generation remains a difficult area of research because it lacks strong objective measures. Generated stories may be linguistically sound, but in many cases suffer poor narrative coherence required for a compelling, logically-sound st
ory. To address this, we present Fabula Entropy Indexing (FEI), an evaluation method to assess story coherence by measuring the degree to which human participants agree with each other when answering true/false questions about stories. We devise two theoretically grounded measures of reader question-answering entropy, the entropy of world coherence (EWC), and the entropy of transitional coherence (ETC), focusing on global and local coherence, respectively. We evaluate these metrics by testing them on human-written stories and comparing against the same stories that have been corrupted to introduce incoherencies. We show that in these controlled studies, our entropy indices provide a reliable objective measure of story coherence.
This Research suggests a new mechanism that aims to increase the effectiveness of
surveillance systems by extracting the moving objects coming from surveillance camera in
order to identify them and propose a new mechanism for indexing and storing i
n database
and classified them according to the basic characteristics and strong indicators and retrieval
when needed in less possible time.
The basic idea lies in the combination of the basic characteristics of the goal (color,
edges and texture) which ensures the best performance in extracting the basic target
features and depend on it as indexes, then nonlinear transfers has been done on the edges
of the target in order to get a picture bearing the minutest details, then conducted adverse
transfers on the edges of the target during the process retrieved from the database. Finally,
we propose a new mechanism for indexing all images tabase to Retrieval them in best
accuracy and less time, and a program had been achieved to realize this idea.
The Research suggests a novel model aims to reduce the time of search for image
files by proposing a new indexing mechanism to avoid the plague algorithm used with
indexing so that the access time to these files becomes as less as possible.
The fi
rst stage in this paper is to clarify the importance of archiving in organizing
files via designing a database, storing images in it and recording the times needed to
obtain the required files from the database. Then the indexing process for image files
stored in the database is applied by proposing a new algorithm -B+ Tree enhanced- for
organizing image files according to a certain mechanism to facilitate accessing any file,
conducting queries and recording the times used to get those files from the database to
compare them with the times required to access files before indexing in order to show the
efficiency of the proposed method.
We offered, in a previous paper, an ontology-based approach to recognize
constraints in free-form service requests and provide services for users. Our
system handles a service request by finding, from among many ontologies, the
domain ontology tha
t best matches the request and then uses the matched
ontology to generate the service request constraints. Although our system is
powerful in recognizing constraints and therefore servicing requests, the
recognition process is a bottleneck due to the number of the tested ontologies
and the amount of computations involved. This paper provides a novel
approach to speed up the recognition process by (1) using ontology indexing
and (2) excluding inapplicable regular expressions early in the process and thus
reducing the number of applied regular expressions. Our experiments show
that our techniques are effective in significantly reducing the amount of
computations and therefore speeding up the recognition process.
