Visual analysis of terrorism events extracted from the public knowledge bases

Understanding the conceptual, temporal and social properties of terrorism is essential to unlock terrorists’ minds, track terrorists and make policies for emergency responses to terrorists’ attacks. The primary goal of this poster is to introduce an integrative approach to study the ideology of terrorism, social structures of terrorists’ organizations and consistent patterns of terrorism events hidden in the public knowledge bases. Our system, Storylines, provides a novel storytelling framework to examine our approach and to help user visually and interactively explore textual information from multiple resources with diverse perspectives. The system enables user study a body of unstructured text without prior knowledge of its thematic structure and automatically find Who, When, What and Where in a salient story. It integrates natural language processing, latent semantic indexing and social network analysis. Our system has been applied to the 2006 VAST contest data that is a synthesized dataset. The data sets of test cases we use for this poster also include a concentrated terrorism news resource ICT (http://www.ict.org.il/) and a web resource related to Cuban Missile Crisis

Speaker Normalization for Self-supervised Speech Emotion Recognition

Large speech emotion recognition datasets are hard to obtain, and small datasets may contain biases. Deep-net-based classifiers, in turn, are prone to exploit those biases and find shortcuts such as speaker characteristics. These shortcuts usually harm a model's ability to generalize. To address this challenge, we propose a gradient-based adversary learning framework that learns a speech emotion recognition task while normalizing speaker characteristics from the feature representation. We demonstrate the efficacy of our method on both speaker-independent and speaker-dependent settings and obtain new state-of-the-art results on the challenging IEMOCAP dataset.Comment: ICASSP 2

Clinical features and outcomes of diffuse endocapillary proliferation Henoch-Schönlein purpura nephritis in children

OBJECTIVE: To investigate the outcomes of childhood diffuse endocapillary proliferation Henoch-Schönlein purpura nephritis (DEP-HSPN) in response to early diagnosis and prompt treatment. METHODS: Eleven cases of DEP-HSPN in children were investigated in comparison to HSPN without diffuse endocapillary proliferation (non-DEP-HSPN). RESULTS: DEP-HSPN had a higher prevalence of nephrotic syndrome but a lower prevalence of hematuria compared to non-DEP-HSPN. IgA, IgG and IgM antibody deposition was found in DEP-HSPN by histopathological examination. Proteinuria cleared in all 11 cases through treatment with steroids and/or immunosuppressive drugs. However, half of the DEP-HSPN patients continuously had hematuria after treatment. CONCLUSION: The early diagnosis and prompt initiation of immunosuppressive treatment based on renal biopsy are important for achieving favorable outcomes

Visualizing the evolution of social networks

We are particularly interested in how a social network evolves over time. We construct social networks as follows. Vertices in a network represent individuals. Interrelationships between these individuals include a wide variety of types, for instance, exchanging emails and appearing in the same news. The strengths of such interrelationships are represented as edges. The evolution of a social network over a period of time, ranging from days in networks derived from news to years in networks associated with a working group, is studied by studying network snapshots taken from a series of consecutive time intervals within the entire period of time. Participating actors in the network are clustered and weighted based on attributes such as email send-reply pairs, interaction thread, and time-based centrality measures. We demonstrate two examples of how emergent linkage patterns can be identified so as to improve our understanding of the social dynamics of the underlying group. One example is based on an email archive of a working group; the other is based on a set of synthesized news data. For example, one may want to find out the most active group member through email conversations during a given period. We will demonstrate how our approach can help not only to understand the roles of actors in terms of their influence and contributions in social networks, but also to generate hypotheses and evidence for visual analytics

Visualizing an enterprise social network from email

Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '06, p. 83.Understanding the patterns of sending and receiving email within an organization may help us to understand the historical and social dimensions of that organization [1, 3]. In addition to asynchronous person-to-person communication, email is also used for other purposes such as task management and personal archives. However, tools for visualizing, analyzing, and understanding communication patterns tend to focus on a static view of such patterns. We are developing a visualization system to help users perform tasks from perspectives based on temporal and connectivity patterns. Specifically, our system is designed to support browsing the email archive of W3C working groups. The archive has been made available to the TREC 2005 Enterprise Competition. This system supports both temporal views and linkage views. Temporal views can help users easily find a mail. Linkage views show emails that belong to the same discussion group. Linkage views make it easy to extract related information by showing the relationships of those messages and senders

Trends in conceptual modeling: Citation analysis of the ER conference papers (1979-2005)

Paper presented at The 11th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2007). Madrid, Spain.We analyze thematic trends and challenging issues in conceptual modeling based on the metadata of 943 research papers published in a series of conferences on conceptual modeling (known as the ER conferences) between 1979 and 2005. We specifically address 1) all-time prominent challenges in conceptual modeling, 2) current challenges and emerging trends, and 3) the structure and dynamics of the conceptual modeling community. We utilize CiteSpace, a progressive domain visualization tool, to identify and visualize the movement of research fronts and intellectual bases, persistent clusters of papers, critical paths connecting these clusters, and the evolution of co-authorship networks as well as citation networks. The work contributes an indepth analysis of a major forum of conceptual modeling and a practical method that one can use as frequently as needed to keep abreast of the state of the art of conceptual modeling

Visualization of protein-protein interaction network for knowledge discovery

Paper presented at the 2006 IEEE International Conference on Granular Computing, Atlanta, GA.A new visualization tool, called "Visual Concept Explorer (VCE)", was developed to visualize concept relationships in bio-medical literatura VCE integrates Pathfinder Network Scaling and Kohonen Self-organizing Feature Map Algorithm for visual mapping. As a case study, VCE was applied to visualize a chromatin protein-protein interaction (PPI) network The mapping results demonstrated that VCE could explore the semantic structure and latent domain knowledge hidden in protein-protein interaction data sets generatedfrom bio-medical literature

Recovery of oil with unsaturated fatty acids and polyphenols from chaenomelessinensis (Thouin) Koehne: Process optimization of pilot-scale subcritical fluid assisted extraction

The potential effects of three modern extraction technologies (cold-pressing, microwaves and subcritical fluids) on the recovery of oil from Chaenomelessinensis (Thouin) Koehne seeds have been evaluated and compared to those of conventional chemical extraction methods (Soxhlet extraction). This oil contains unsaturated fatty acids and polyphenols. Subcritical fluid extraction (SbFE) provided the highest yield—25.79 g oil/100 g dry seeds—of the three methods. Moreover, the fatty acid composition in the oil samples was analysed using gas chromatography–mass spectrometry. This analysis showed that the percentages of monounsaturated (46.61%), and polyunsaturated fatty acids (42.14%), after applying SbFE were higher than those obtained by Soxhlet, cold-pressing or microwave-assisted extraction. In addition, the oil obtained under optimized SbFE conditions (35 min extraction at 35 °C with four extraction cycles), showed significant polyphenol (527.36 mg GAE/kg oil), and flavonoid (15.32 mg RE/kg oil), content, had a good appearance and was of high quality

Analyzing the impact of Sloan Digital Sky Survey on astronomical literature: A multiple perspective approach

As part of an ongoing and ambitious project to support scientific discoveries in astronomy, in particular based on the use of Sloan Digital Sky Survey data, this article addresses a number of practical issues concerning the analysis of the impact of such large-scale survey datasets on scientific discoveries in terms of trends and patterns in scientific publications that utilize the data. We take a multi-perspective approach to the analysis of the SDSS literature, namely a statistical perspective, a network analysis perspective, and a text analysis perspective. This study reveals practical issues that have theoretical and methodological implications on the applications of scientometrics and bibliometrics on astronomical literature

Diketopiperazine alkaloids from a mangrove rhizosphere soil derived fungus Aspergillus effuses H1-1

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