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Abbreviation Completion is a novel technique to improve the efficiency of code-writing by supporting code completion of multiple keywords based on non-predefined abbreviated input - a different approach from conventional code completion that finds one keyword at a time based on an exact character match. Abbreviated
Many misunderstandings can occur during remote interaction due to different user domain competency levels, different cognitive capacity of users as well as different user backgrounds. In this paper, we propose an adaptive keyword/summary presentation approach that aims at identifying potential misunderstandings of
Current keyword search by Google, Yahoo, and so on gives enormous unsuitable results. A solution to this perhaps is to annotate semantics to textual web data to enable semantic search, rather than keyword search. However, pure manual annotation is very time-consuming. Further, searching high level concept such as
With the development of internet, web information increases fast, how to filter information which users wanted quickly and accurately is becoming a big problem. But the traditional keyword based search system's recall rate and precision are yet to be improved. Kam-so, the user interesting collaborative filtering model
Our goal is to use the vast repositories of available open source code to generate specific functions or classes that meet a user's specifications. The key words here are specifications and generate. We let users specify what they are looking for as precisely as possible using keywords, class or method signatures
Rooted in multi-document summarization, maximum marginal relevance (MMR) is a widely used algorithm for meeting summarization (MS). A major problem in extractive MS using MMR is finding a proper query: the centroid based query which is commonly used in the absence of a manually specified query, can not significantly outperform a simple baseline system. We introduce a simple yet robust algorithm to...
Speech Recognition (ASR), Multilingual Text-to-Speech system with other enhanced features like keyword search facility, Intelligent/ Auto customization in accordance with user and paper independent classified headings. The integration of ASR enables user to operate the system in complete hands free mode.
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
presented. Extensible experiment results demonstrate that annotated web services by our proposed method can more satisfy requirements of service requesters than keyword-based described web services. It can achieve higher service discovery effectiveness.
The authors' information retrieval approach automatically extracts users' intentions when they interact with a device to access information, obviating the need for keyword inputs. The approach extracts these intentions by analyzing basic operations such as zooming, centering, and panning on a map, and applying them as
A "keywords cloud" learning interest/difficult reminding system based on learners' video watching logs and subtitles is proposed for promoting self-paced MOOC learning. By identifying the hot video segments (via video seek event counts) and weighting the keywords of hot video segments, we are able to
option, say, limiting search to few links. To reduce the time spent by users, a web link extraction tool has been designed and implemented in Java, that analyzes the ways of extracting web link information using a standard interface. The Test Scenario has been presented with various keywords like Higher Education
It has become common to search necessary services and contents using the Internet, but it is difficult to find exactly what one is looking for through keywords as each service is described in just too many ways. We developed "laddering" search service system that matches the needs of the users with the search targets
As the traditional way of information retrieval is based on keywords that the computer cannot understand users' potential semantic and personalized query requirements, the paper proposes an ontology-based method of the product information retrieval. Using the product information retrieval as an example, the framework
Our goal is to use the vast repositories of available open source code to generate specific functions or classes that meet a user's specifications. The key words here are specifications and generate. We let users specify what they are looking for as precisely as possible using keywords, class or method signatures
degree of relevancy for the user than is currently available with conventional methods, for example, using matching keywords. We describe here our method and the relation between the scenes and discuss a prototype system.
Managing photos by using visual features (e.g., color and texture) is known to be a powerful, yet imprecise, retrieval paradigm because of the semantic gap problem. The same is true if search relies only on keywords (or tags), derived from either the image context or user-provided annotations. In this paper we present
In this paper, we present the AI Goggles system, which can instantly describe objects and scenes in the real world and retrieve visual memories about them using keywords input by the users. This is a stand-alone wearable system working on a tiny mobile computer. Also, the system can quickly learn unknown objects and
, flooding, and global warming. In particular, the Sticker navigates user-generated contents with three dimensional view of space-time(2D+1D) and allows users to retrieve related information with the moving phenomena in a spatiotemporal domain as well as interesting keywords.
filtering recommendation is implemented using intelligent agents. The agents work together for recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.