Intelligent computing and sensing for active safety on construction sites

On obstacle-cluttered construction sites where heavy equipment is in use, safety issues are of major concern. The main objective of this paper is to develop a framework with algorithms for obstacle avoidance and path planning based on real-time three-dimensional job site models to improve safety during equipment operation. These algorithms have the potential to prevent collisions between heavy equipment vehicles and other on-site objects. In this study, algorithms were developed for image data acquisition, real-time 3D spatial modeling, obstacle avoidance, and shortest path finding and were all integrated to construct a comprehensive collision-free path. Preliminary research results show that the proposed approach is feasible and has the potential to be used as an active safety feature for heavy equipment.

An investigation of unique challenges in National Historic relics and scenic sites (NHRSSs) in Chinese research literature 1982-2008 using co-word analysis

National Housing Relics and Scenic Sites (NHRSSs) in China are the equivalent of National Parks in the West but have contrasting features and broader roles when compared to their Western counterparts. By reviewing and analysing more than 370 academic sources, this paper identifies 6 major issue clusters and future challenges that will influence the management of NHRSSs over time. It also provides a number of cases to illustrate the particular features of NHRSSs. Identifying the hot issues and important challenges in Chinese NHRSSs will provide valuable insights into priorities now being discussed in highly populated areas of the World.

Individual user behaviour modelling for effective web recommendation

With the growth of the Web, E-commerce activities are also becoming popular. Product recommendation is an effective way of marketing a product to potential customers. Based on a user’s previous searches, most recommendation methods employ two dimensional models to find relevant items. Such items are then recommended to a user. Further too many irrelevant recommendations worsen the information overload problem for a user. This happens because such models based on vectors and matrices are unable to find the latent relationships that exist between users and searches. Identifying user behaviour is a complex process, and usually involves comparing searches made by him. In most of the cases traditional vector and matrix based methods are used to find prominent features as searched by a user. In this research we employ tensors to find relevant features as searched by users. Such relevant features are then used for making recommendations. Evaluation on real datasets show the effectiveness of such recommendations over vector and matrix based methods.

Improving web database search incorporating users query information

The growing importance and need of data processing for information extraction is vital for Web databases. Due to the sheer size and volume of databases, retrieval of relevant information as needed by users has become a cumbersome process. Information seekers are faced by information overloading - too many result sets are returned for their queries. Moreover, too few or no results are returned if a specific query is asked. This paper proposes a ranking algorithm that gives higher preference to a user’s current search and also utilizes profile information in order to obtain the relevant results for a user’s query.

Identifying interests of web users for effective recommendations

Search log data is multi dimensional data consisting of number of searches of multiple users with many searched parameters. This data can be used to identify a user’s interest in an item or object being searched. Identifying highest interests of a Web user from his search log data is a complex process. Based on a user’s previous searches, most recommendation methods employ two-dimensional models to find relevant items. Such items are then recommended to a user. Two-dimensional data models, when used to mine knowledge from such multi dimensional data may not be able to give good mappings of user and his searches. The major problem with such models is that they are unable to find the latent relationships that exist between different searched dimensions. In this research work, we utilize tensors to model the various searches made by a user. Such high dimensional data model is then used to extract the relationship between various dimensions, and find the prominent searched components. To achieve this, we have used popular tensor decomposition methods like PARAFAC, Tucker and HOSVD. All experiments and evaluation is done on real datasets, which clearly show the effectiveness of tensor models in finding prominent searched components in comparison to other widely used two-dimensional data models. Such top rated searched components are then given as recommendation to users.

Clustering of web users using the tensor decomposed models

We propose to use the Tensor Space Modeling (TSM) to represent and analyze the user’s web log data that consists of multiple interests and spans across multiple dimensions. Further we propose to use the decomposition factors of the Tensors for clustering the users based on similarity of search behaviour. Preliminary results show that the proposed method outperforms the traditional Vector Space Model (VSM) based clustering.

Web 2.0 applications in enterprises and education

Web 2.0 technology and concepts are being used increasingly by organisations to enhance knowledge, efficiency, engagement and reputation. Understanding the concepts of Web 2.0, its characteristics, and how the technology and concepts can be adopted, is essential to successfully reap the potential benefits. In fact, there is a debate about using the Web 2.0 idiom to refer to the concept behind it; however, this term is widely used in literature as well as in industry. In this paper, the definition of Web 2.0 technology, its characteristics and the attributes, will be presented. In addition, the adoption of such technology is further explored through the presentation of two separate case examples of Web 2.0 being used: to enhance an enterprise; and to enhance university teaching. The similarities between these implementations are identified and discussed, including how the findings point to generic principles of adoption.

Negotiation 2.0 in the twitter age : using Web 2.0 applications to facilitate successful negotiation outcomes

The high-pressure, cross-cultural, cross-factional and frequently cross-national nature of contemporary negotiation means that there are a number of clements potentially hampering efforts to achieve successful negotiation outcomes from face-to-face interactions. These hindrances include: resource scarcity (for example, finances, technology and facilities), time scarcity, geographical separation, lack of a COnl1110n language and an inability to Inaintain a consistent ongoing dialogue.

Automated gathering of Web information: An in-depth examination of agents interacting with search engines

The Web has become a worldwide repository of information which individuals, companies, and organizations utilize to solve or address various information problems. Many of these Web users utilize automated agents to gather this information for them. Some assume that this approach represents a more sophisticated method of searching. However, there is little research investigating how Web agents search for online information. In this research, we first provide a classification for information agent using stages of information gathering, gathering approaches, and agent architecture. We then examine an implementation of one of the resulting classifications in detail, investigating how agents search for information on Web search engines, including the session, query, term, duration and frequency of interactions. For this temporal study, we analyzed three data sets of queries and page views from agents interacting with the Excite and AltaVista search engines from 1997 to 2002, examining approximately 900,000 queries submitted by over 3,000 agents. Findings include: (1) agent sessions are extremely interactive, with sometimes hundreds of interactions per second (2) agent queries are comparable to human searchers, with little use of query operators, (3) Web agents are searching for a relatively limited variety of information, wherein only 18% of the terms used are unique, and (4) the duration of agent-Web search engine interaction typically spans several hours. We discuss the implications for Web information agents and search engines.