Abstract: Organized crime, insurgency and terrorist organizations have a large and undermining impact on societies. This highlights the urgency to better understand the complex dynamics of these individuals and organizations in order to timely detect critical social phase transitions that form a risk for society. In this paper we introduce a new multi-level modelling approach that integrates insights from complex systems, criminology, psychology, and organizational studies with agent-based modelling. We use a bottom-up approach to model the active and adaptive reactions by individuals to the society, the economic situation and law enforcement activity. This approach enables analyzing the behavioral transitions of individuals and associated micro processes, and the emergent networks and organizations influenced by events at meso- and macro-level. At a meso-level it provides an experimentation analysis modelling platform of the development of opponent organization subject to the competitive characteristics of the environment and possible interventions by law enforcement. While our model is theoretically founded on findings in literature and empirical validation is still work in progress, our current model already enables a better understanding of the mechanism leading to social transitions at the macro-level. The potential of this approach is illustrated with computational results.

Abstract: Cross-modal information retrieval aims to search for semantically relevant data from various modalities when given a query from one modality. For text-image retrieval, a common solution is to map texts and images into a common semantic space and measure their similarity directly. Both the positive and negative examples are used for common semantic space learning. Existing work treats the positive/negative text-image pairs as equally positive/negative. However, we observe that many positive examples resemble the negative ones in some degrees and vice versa. These “hard examples” are challenging for existing models. In this paper, we aim to assign fine-grained labels for the examples to capture the degrees of “hardness”, thus enhancing cross-modal correlation learning. Specifically, we propose a siamese network on both the positive and negative examples to obtain their semantic similarities. For each positive/negative example, we use the text description of the image in the example to calculate its similarity with the text in the example. Based on these similarities, we assign fine-grained labels to both the positives and negatives and introduce these labels to a pairwise similarity loss function. The loss function benefits from the labels to increase the influence of hard examples on the similarity learning while maximizing the similarity of relevant text-image pairs and minimizing the similarity of irrelevant pairs. We conduct extensive experiments on the English Wikipedia, Chinese Wikipedia, and TVGraz datasets. Compared with state-of-the-art models, our model achieves significant improvement on the retrieval performance by incorporating with fine-grained labels.

Abstract: Video applications today are more often deploying content delivery networks (CDNs) for content delivery. However, by decoupling the owner of the content and the organization serving it, CDNs could be abused by attackers to commit network crimes. Traditional flow-level measurements for generating reputation of IPs and domain names for video applications are insufficient. In this paper, we present MeshTrust, a novel approach that assessing reputation of service providers on video traffic automatically. We tackle the challenge from two aspects: the multi- tenancy structure representation and CDN-centric trust model. First, by mining behavioral and semantic characteristics, a Mesh Graph consisting of video websites, CDN nodes and their relations is constructed. Second, we introduce a novel CDN-centric trust model which transforms Mesh Graph into Trust Graph based on extended network embedding methods. Based on the labeled nodes in Trust Graph, a reputation score can be easily calculated and applied to real-time reputation management on video traffic. Our experiments show that MeshTrust can differentiate normal and illegal video websites with accuracy approximately 95% in a real cloud environment.

Abstract: The ability of customer data collection in enterprise corporate information systems leads to the emergence of customer-centric algorithms and approaches. In this study, we consider the problem of choosing a candidate branch for closing based on the overall expected level of dissatisfaction of company customers with the location of remaining branches. To measure the availability of branches for individuals, we extract points of interests from the traces of visits using the clustering algorithm to find centers of interests. The following questions were further considered: (i) to which extent does spatial accessibility influence the choice of company branches by the customers? (ii) which algorithm provides a better trade-off between accuracy and computational complexity? These questions were studied in application to a bank branches network. In particular, data and domain restrictions from our bank-partner (one of the largest regional banks in Russia) were used. The results show that: (i) spatial accessibility significantly influences customers' choice (65%–75% of customers choose one of the top 5 branches by accessibility after closing the branch), (ii) the proposed greedy algorithm provides optimal solution in almost all of cases, (iii) output of the greedy algorithm may be further improved with a local search algorithm, (iv) instance of a problem with several dozens of branches and up to million customers may be solved with near-optimal quality in dozens of seconds.