Towards quantifying the impact of non-uniform information access in collaborative information retrieval

The majority of research into Collaborative Information Retrieval (CIR) has assumed a uniformity of information access and visibility between collaborators. However in a number of real world scenarios, information access is not uniform between all collaborators in a team e.g. security, health etc. This can be referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, there has not yet been any systematic investigation of the effect of MLCIR on search outcomes. To address this shortcoming, in this paper, we present the results of a simulated evaluation conducted over 4 different non-uniform information access scenarios and 3 different collaborative search strategies. Results indicate that there is some tolerance to removing access to the collection and that there may not always be a negative impact on performance. We also highlight how different access scenarios and search strategies impact on search outcomes

TRECVID: evaluating the effectiveness of information retrieval tasks on digital video

TRECVID is an annual exercise which encourages research in information retrieval from digital video by providing a large video test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of some semantic features, and the automatic segmentation of TV news broadcasts into non-overlapping news stories. TRECVID has a broad range of over 40 participating groups from across the world and as it is now (2004) in its 4th annual cycle it is opportune to stand back and look at the lessons we have learned from the cumulative activity. In this paper we shall present a brief and high-level overview of the TRECVID activity covering the data, the benchmarked tasks, the overall results obtained by groups to date and an overview of the approaches taken by selective groups in some tasks. While progress from one year to the next cannot be measured directly because of the changing nature of the video data we have been using, we shall present a summary of the lessons we have learned from TRECVID and include some pointers on what we feel are the most important of these lessons

Multilingual interactive experiments with Flickr

This paper presents a proposal for iCLEF 2006, the interactive track of the CLEF cross-language evaluation campaign. In the past, iCLEF has addressed applications such as information retrieval and question answering. However, for 2006 the focus has turned to text-based image retrieval from Flickr. We describe Flickr, the challenges this kind of collection presents to cross-language researchers, and suggest initial iCLEF tasks

A document management methodology based on similarity contents

The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Performance of laser assisted micro-milling (laμmill) of titanium alloy using micro ball end mill

Laser assisted milling technique turns to be complicated and unpredictable when the machining size is scaled to micro level. Furthermore, less has been reported on the application of ball end mill in laser assisted micro milling. This study was carried out to evaluate and clarify the machining characteristics of micro ball end mill in laser assisted micro milling of titanium alloy Ti6Al4V. Finite element method (FEM) simulation of preheating was used to determine the machining parameters and the cutting tool to laser beam distance. The performance of laser assisted micro milling using micro ball end mill was evaluated via experimental works using various feed, feed rate, depth of cut and cutting speed. The cutting force, tool wear, chip pattern, burr and machining surface conditions were compared between conventional micro milling and laser assisted micro milling. Machining simulation was also carried out to study and collect the supportive evidence to explain the chips formation mechanisms. The laser heating simulation model was built and validated to determine the cutting tool to laser beam distance. When the feed rates ranging from 52.5 to 210 mm/min, the workpiece temperature at machining region was increased from 128 °C to 178 °C when the cutting tool is located at 0.6 mm from the laser. At this condition, the creation of heat affected zone and melted zone were successfully avoided. This study has proven that laser assisted micro milling reduces the cutting force approximately 5 to 20 %, depending on the feed and depth of cut applied. However, it is also found out that the chip pattern has a strong correlation with tool wear rate and surface roughness. It was observed that loose arc chips were produced at the feed and depth of cut of 3.0 x 10-3 mm/flute and 0.02 mm, respectively. This type of chip is preferable due to less chip blocking, rubbing and chip compression effect. It is also proven that laser assisted micro milling technique is more effective when the workpiece temperature is increased to approximately 250 °C compared to 180 °C