Tour recommendation for groups

Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data

A differential semantic algorithm for query relevant web page recommendation

With the exponential rise in the amount of information in the World Wide Web, there is a need for a much efficient algorithm for Web Search. The traditional keyword matching as well as the standard statistical techniques is insufficient as the Web Pages they recommend are not highly relevant to the query. With the growth in Semantic Web, an algorithm which semantically computes the most relevant Web Pages is required. In this paper, a methodology which computes the semantic heterogeneity between the keywords, content words and query words for web page recommendation is incorporated. A Differential Adaptive PMI Algorithm is formulated for with varied thresholds for recommending the Web Pages based on the input query. The proposed methodology yields an accuracy of 0.87 which is much better than the existing strategies. © 2016 IEEE

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

Systematic approaches to mine, predict and visualize biological functions

With advances in high-throughput technologies and next-generation sequencing, the amount of genomic and proteomic data is dramatically increasing in the post-genomic era. One of the biggest challenges that has arisen is the connection of sequences to their activities and the understanding of their cellular functions and interactions. In this dissertation, I present three different strategies for mining, predicting and visualizing biological functions. In the first part, I present the COMputational Bridges to Experiments (COMBREX) project, which facilitates the functional annotation of microbial proteins by leveraging the power of scientific community. The goal is to bring computational biologists and biochemists together to expand our knowledge. A database-driven web portal has been built to serve as a hub for the community. Predicted annotations will be deposited into the database and the recommendation system will guide biologists to the predictions whose experimental validation will be more beneficial to our knowledge of microbial proteins. In addition, by taking advantage of the rich content, we develop a web service to help community members enrich their genome annotations. In the second part, I focus on identifying the genes for enzyme activities that lack genetic details in the major biological databases. Protein sequences are unknown for about one-third of the characterized enzyme activities listed in the EC system, the so-called orphan enzymes. Our approach considers the similarities between enzyme activities, enabling us to deal with broad types of orphan enzymes in eukaryotes. I apply our framework to human orphan enzymes and show that we can successfully fill the knowledge gaps in the human metabolic network. In the last part, I construct a platform for visually analyzing the eco-system level metabolic network. Most microbes live in a multiple-species environment. The underlying nutrient exchange can be seen as a dynamic eco-system level metabolic network. The complexity of the network poses new visualization challenges. Using the data predicted by Computation Of Microbial Ecosystems in Time and Space (COMETS), I demonstrate that our platform is a powerful tool for investigating the interactions of the microbial community. We apply it to the exploration of a simulated microbial eco-system in the human gut. The result reflects both known knowledge and novel mutualistic interactions, such as the nutrients exchanges between E. coli, C. difficile and L. acidophilus

A fuzzy logic-based text classification method for social media

Social media offer abundant information for studying people’s behaviors, emotions and opinions during the evolution of various rare events such as natural disasters. It is useful to analyze the correlation between social media and human-affected events. This study uses Hurricane Sandy 2012 related Twitter text data to conduct information extraction and text classification. Considering that the original data contains different topics, we need to find the data related to Hurricane Sandy. A fuzzy logic-based approach is introduced to solve the problem of text classification. Inputs used in the proposed fuzzy logic-based model are multiple useful features extracted from each Twitter’s message. The output is its degree of relevance for each message to Sandy. A number of fuzzy rules are designed and different defuzzification methods are combined in order to obtain desired classification results. This work compares the proposed method with the well-known keyword search method in terms of correctness rate and quantity. The result shows that the proposed fuzzy logic-based approach is more suitable to classify Twitter messages than keyword word method

Integrated analysis of gene expression by association rules discovery

BACKGROUND: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process. RESULTS: In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work. CONCLUSION: The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package