Computer Science

Background Human immunodeficiency virus-1 (HIV-1) has a minimal genome of only 9 genes, which encode 15 proteins. HIV-1 thus depends on the human host for virtually every aspect of its life cycle. The universal language of communication in biological systems, including between pathogen and host, is via signal transduction pathways. The fundamental units of these pathways are protein protein interactions.

Abstract HIV-1 Vpr, a nonstructural viral protein associated with virus particles, has a positive role in the efficient transport of PIC into the nucleus of non-dividing target cells and enhances virus replication in primary T cells. Vpr is a 96 amino acid protein and the structure by NMR shows three helical domains. Vpr has been shown to exist as dimers and higher order oligomers.

Abstract Due to the increasingly larger and more interdisciplinary nature of scientific reporting, it is becoming more difficult to identify all the potentially relevant, citeable articles in reference lists of publications such as scientific papers, reports, grant proposals and patent applications. Authors may miss and/or give inaccurate citations, potentially hindering progress in a discipline and on a personal level, and change the importance and impact of an investigator's work.

Abstract Human immunodeficiency virus-1 (HIV-1) in acquired immune deficiency syndrome (AIDS) relies on human host cell proteins in virtually every aspect of its life cycle. Knowledge of the set of interacting human and viral proteins would greatly contribute to our understanding of the mechanisms of infection and subsequently to the design of new therapeutic approaches. This work is the first attempt to predict the global set of interactions between HIV-1 and human host cellular proteins.

Scientific programmers have traditionally programmed in entirely sequential languages such as Fortran, C or Pascal and it could be argued that object-orientation is not a concept that they would need. Yet computer science departments that give courses to scientists and engineers would like to consider teaching them in Java, rather than in one of the older languages. This paper addresses the dual issues of how Java can best supply everything that the older languages do, and then what it can meaningfully give in added value, especially in the networking and parallel area. Experience with developing solutions in Java to some fifty typical numerical problems has led to a coherent object-oriented approach and a couple of essential support classes for teaching and production work.

Two advances in system design over the past decade are brought together in this paper to enhance the process of building distributed systems. Component based programming separates the construction of computational units from their configuration into complex systems. An accepted technique for configuring components is to use an architectural description languages such as Darwin. The resulting system then runs on an environment such as Regis. Arriving later, Java emphasises the presence of the network, and thereby facilitates large scale distributed programming. This paper presents the design, implementation and assessment of Jaden, a runtime environment written in and based on Java, which will support component based programming. Though most of the functions of Darwin's Regis environment are present in Jaden, its internal structure is very different. Our results show that using Java's built-in networking makes for a simpler system with controllable system overhead and good scalability properties.

Two advances in system design over the past decade are brought together in this paper to enhance the process of building distributed systems. Component based programming separates the construction of computational units from their configuration into complex systems. An accepted technique for configuring components is to use an architectural description languages such as Darwin. The resulting system then runs on an environment such as Regis. Arriving later, Java emphasises the presence of the network, and thereby facilitates large scale distributed programming. This paper presents the design, implementation and assessment of Jaden, a runtime environment written in and based on Java, which will support component based programming. Though most of the functions of Darwin's Regis environment are present in Jaden, its internal structure is very different. Our results show that using Java's built-in networking makes for a simpler system with controllable system overhead and good scalability properties.

Agencies consisting of simple agents can be deployed on the Internet to solve problems that are too complex for single autonomous Internet agents. We deploy Bayesian agencies on the Internet to achieve emergent belief propagation through the collective behavior of simple agents organized into heterarchies of agencies. Collectively the agents use probabilistic reasoning to deal with the uncertainty and non-determinism that they will encounter on the Internet. The simplicity of the agents as well as the minimal interaction between them allows us to use a commercially available application framework, namely Enterprise JavaBeans, to develop and deploy our Bayesian agencies on the Internet.

Alchemi and the Globus Toolkit are open source software toolkits for implementing a Grid. Although both toolkits are designed for the same purpose, their architecture and underlying technology are completely different. Thus, a performance comparison of a Grid implementation in Alchemi with a similar Grid implementation in the Globus Toolkit will be interesting. We built a testbed to compare the performance of the two toolkits. This paper includes tables and graphs to illustrate the comparison.

0 7 4 0 -7 4 5 9 / 9 8 / $ 1 0 . 0 0 © 1 9 9 8 I E E E M a y / J u n e 1 9 9 8 I E E E S o f t w a r e 3 1 uery and analysis lie at the heart of a geographic information system. The ability to analyze geographical patterns and relationships differentiates GISs from computer cartography, computer-aided design, remote sensing, and database management. Computer scientists have researched relational database query languages for more than a decade, but a spatial query language for GISs has different requirements.

Online social networking sites such as Flickr and Facebook provide a diverse range of functionalities that foster online communities to create and share media content. In particular, Flickr groups are increasingly used to aggregate and share photos about a wide array of topics or themes. Unlike photo repositories where images are typically organized with respect to static topics, the photo sharing process as in Flickr often results in complex time-evolving social and visual patterns. Characterizing such time-evolving patterns can enrich media exploring experience in a social media repository. In this paper, we propose a novel framework that characterizes distinct timeevolving patterns of group photo streams. We use a nonnegative joint matrix factorization approach to incorporate image content features and contextual information, including associated tags, photo owners and post times. In our framework, we consider a group as a mixture of themeseach theme exhibits similar patterns of image content and context. The theme extraction is to best explain the observed image content features and associations with tags, users and times. Extensive experiments on a Flickr dataset suggest that our approach is able to extract meaningful evolutionary patterns from group photo streams. We evaluate our method through a tag prediction task. Our prediction results outperform baseline methods, which indicate the utility of our theme based joint analysis.

In this paper we develop a recommendation framework to connect image content with communities in online social media. The problem is important because users are looking for useful feedback on their uploaded content, but finding the right community for feedback is challenging for the end user. Social media are characterized by both content and community. Hence, in our approach, we characterize images through three types of features: visual features, user generated text tags, and social interaction (user communication history in the form of comments). A recommendation framework based on learning a latent space representation of the groups is developed to recommend the most likely groups for a given image. The model was tested on a large corpus of Flickr images comprising 15,689 images. Our method outperforms the baseline method, with a mean precision 0.62 and mean recall 0.69. Importantly, we show that fusing image content, text tags with social interaction features outperforms the case of only using image content or tags. Figure 1: The overall system overview of our group recommendation framework.

In this paper we develop a recommendation framework to connect image content with communities in online social media. The problem is important because users are looking for useful feedback on their uploaded content, but finding the right community for feedback is challenging for the end user. Social media are characterized by both content and community. Hence, in our approach, we characterize images through three types of features: visual features, user generated text tags, and social interaction (user communication history in the form of comments). A recommendation framework based on learning a latent space representation of the groups is developed to recommend the most likely groups for a given image. The model was tested on a large corpus of Flickr images comprising 15,689 images. Our method outperforms the baseline method, with a mean precision 0.62 and mean recall 0.69. Importantly, we show that fusing image content, text tags with social interaction features outperforms the case of only using image content or tags.

This paper develops a context aware framework to address the diverse communication needs of a modern enterprise. Such enterprises are characterized by workers in different locations, subject to different policies, using different communication devices, and having varying degrees of skill sets. This diversity poses challenges in finding the most effective human worker (agent) for tasks like fielding a customer request, helping another agent with additional expert knowledge, or more generally help complete a task like a supply chain exception. We focus on the problem of routing communications to the most effective agent using a spectrum of contextual knowledge: availability, media type, activity, expertise, and location. We determine an optimal 'request-to-agent' routing based on several metrics of effectiveness depending on the communication context. The optimal agent is selected to communicate on a specific media who minimizes the expected duration of interaction while maximizes the probability of successful call completion. Based on our model we have conducted simulations involving context aware and noncontext aware routing scenarios. The results indicate that the context aware routing outperforms other conventional requestrouting techniques. The work presented here can impact routing algorithms, as well as address problems related to enterprise staffing and temporal variation of context for the agent.

Rich media social networks promote not only creation and consumption of media, but also communication about the posted media item. What causes a conversation to be interesting, that prompts a user to participate in the discussion on a posted video? We conjecture that people participate in conversations when they find the conversation theme interesting, see comments by people whom they are familiar with, or observe an engaging dialogue between two or more people (absorbing back and forth exchange of comments). Importantly, a conversation that is interesting must be consequential -i.e. it must impact the social network itself.

Rich media social networks promote not only creation and consumption of media, but also communication about the posted media item. What causes a conversation to be interesting, that prompts a user to participate in the discussion on a posted video? We conjecture that people participate in conversations when they find the conversation theme interesting, see comments by people whom they are familiar with, or observe an engaging dialogue between two or more people (absorbing back and forth exchange of comments). Importantly, a conversation that is interesting must be consequential -i.e. it must impact the social network itself.

This paper develops a context aware framework to address the diverse communication needs of a modern enterprise. Such enterprises are characterized by workers in different locations, subject to different policies, using different communication devices, and having varying degrees of skill sets. This diversity poses challenges in finding the most effective human worker (agent) for tasks like fielding a customer request, helping another agent with additional expert knowledge, or more generally help complete a task like a supply chain exception. We focus on the problem of routing communications to the most effective agent using a spectrum of contextual knowledge: availability, media type, activity, expertise, and location. We determine an optimal 'request-to-agent' routing based on several metrics of effectiveness depending on the communication context. The optimal agent is selected to communicate on a specific media who minimizes the expected duration of interaction while maximizes the probability of successful call completion. Based on our model we have conducted simulations involving context aware and noncontext aware routing scenarios. The results indicate that the context aware routing outperforms other conventional requestrouting techniques. The work presented here can impact routing algorithms, as well as address problems related to enterprise staffing and temporal variation of context for the agent.

The paper develops a novel computational framework for predicting communication flow in social networks based on several contextual features. The problem is important because prediction of communication flow can impact timely sharing of specific information across a wide array of communities. We determine the intent to communicate and communication delay between users based on several contextual features in a social network corresponding to (a) neighborhood context, (b) topic context and (c) recipient context. The intent to communicate and communication delay are modeled as regression problem which are efficiently estimated using Support Vector Regression. We predict the intent and the delay, on a time slice using past communication and have excellent prediction results on a real-world dataset from MySpace.com with an accuracy of 13-16%. We show that the intent to communicate is more significantly influenced by contextual factors compared to the delay.

The paper develops a novel computational framework for predicting communication flow in social networks based on several contextual features. The problem is important because prediction of communication flow can impact timely sharing of specific information across a wide array of communities. We determine the intent to communicate and communication delay between users based on several contextual features in a social network corresponding to (a) neighborhood context, (b) topic context and (c) recipient context. The intent to communicate and communication delay are modeled as regression problems which are efficiently estimated using Support Vector Regression. We predict the intent and the delay, on an interval of time using past communication data. We have excellent prediction results on a real-world dataset from MySpace.com with an accuracy of 13-16%. We show that the intent to communicate is more significantly influenced by contextual factors compared to the delay.