Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e. privacy, and the network costs will also be removed

Connectedness of users-items networks and recommender systems

Recommender systems have become an important issue in network science. Collaborative filtering and its variants are the most widely used approaches for building recommender systems, which have received great attention in both academia and industry. In this paper, we studied the relationship between recommender systems and connectivity of users-items bipartite network. This results in a novel recommendation algorithm. In our method recommended items are selected based on the eigenvector corresponding to the algebraic connectivity of the graph - the second smallest eigenvalue of the Laplacian matrix. Since recommending an item to a user equals to adding a new link to the users-items bipartite graph, the intuition behind the proposed approach is that the items should be recommended to the users such that the least increase in the connectedness of the network (i.e., the algebraic connectivity) is obtained. Through experiments on a number of benchmark datasets, we showed that the proposed connectivity-based recommendation method has comparable results to a number of commonly used recommendation methods. These results shed light on the relation between the evolution of users' behavior and network topology

Recommender systems based on collaborative filtering and resource allocation

Recommendation systems are important part of electronic commerce, where appropriate items are recommended to potential users. The most common algorithms used for constructing recommender systems in commercial applications are collaborative filtering methods and their variants, which is mainly due to their simple implementation. In these methods, structural features of bipartite network of users and items are used and potential items are recommended to the users based on a similarity measure that shows how similar the behavior of the users is. Indeed, the performance of the memory-based CF algorithms heavily depends on the quality of similarities obtained among users/items. As the obtained similarities are more reliable, better performance for the recommender systems is expected. In this paper, we propose three models to extract reliability of similarities estimated in classic recommenders. We incorporate the obtained reliabilities to improve performance of the recommender systems. In the proposed algorithms for reliability extraction, a number of elements are taken into account including the structure of the user-item bipartite network, the individual profile of the users, i.e., how many items they have rated, and that of the items, i.e., how many users have rated them. Among the proposed methods, the method based on resource allocation provides the highest performance as compared to others. Our numerical results on two benchmark datasets (Movielens and Netflix) shows that employing resource allocation in classical recommenders significantly improves their performance. These results are of great importance since including resource allocation in the systems does not increase their computational complexity

Sign prediction in social networks based on users reputation and optimism

Online social networks are significant part of real life. Participation in social networks varies based on users needs or interests. Often, people participate in these platforms due to their interests. Social media not only consist of dense connected components (communities), but also these platforms are dynamic. The dynamism also includes formation and deformation of connections. In some online social networks, connections are mapped to positive and negative links. Positive connections are sign of friendship or trust, while negative links show enmity or distrust. Community structures and temporal traces can also be observed in signed networks. Networks with both positive and negative connections occur in various fields of applications. Reliable prediction of edge sign has a significant influence on friendship formation or enmity prevention. Prediction of edge signs have been considerably explored, however, we intend to discover simple and noticeable social properties in order to identify the connections’ future in networks consisting of both positive and negative links. In order to approach this goal, we investigate real-world signed social networks and build several prediction models. Additionally, simple social properties of trust/distrust networks are employed. Two local nodal measures, called reputation and optimism, are introduced. A node’s reputation indicates how popular the node is. Conversely, its optimism measures its voting pattern toward others. To reduce inherent biases in voting, we also introduce an algorithm to compute the nodes’ reputation and optimism. These rank-based metrics are computed based on nodes’ ranking scores in the network. Furthermore, we employ reputation and optimism of trustor and trustee to predict the sign of the edges in a number of real signed networks including Epinions, Slashdot and Wikipedia. Finally, several classifiers are applied for this purpose. Our experiments show that these simple features have superior performance over state of the art methods

Nationwide prediction of drought conditions in Iran based on remote sensing data

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the country. Satellite-based remote sensing dataa^that are freely availablea^give information on vegetation conditions and land cover. In this paper, we constructed artificial neural network to model (and forecast) drought conditions based on satellite imagery. To this end, standardized precipitation index (SPI) was used as a measure of drought severity. A number of features including normalized difference vegetation index (NDVI), vegetation condition index (VCI), and temperature condition index (TCI) were extracted from NOAA-AVHRR images. The model received these features as input and outputted the SPI value (or drought condition). Applying the model to the data of stations for which the precipitation data were available, we showed that it could forecast the drought condition with an accuracy of up to 90 percent. Furthermore, TCI was found to be the best marker of drought conditions among satellite-based features. We also found multilayer perceptron better than radial basis function networks and support vector machines forecasting drought conditions