When and why threats go undetected: Impacts of event rate and shift length on threat detection accuracy during airport baggage screening

Objective: We aimed to assess the impact of task demands and individual characteristics on threat detection in baggage screeners. Background: Airport security staff work under time constraints to ensure optimal threat detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate threat detection. Method: We examined threat detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. Results: Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse threat detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. Conclusion: Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that threats go undetected. Application: Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal threat detection and passenger safety.

Game theoretic, decentralized, local information based algorithm for community detection in social graphs

Motivated by the observation that communities in real world social networks form due to actions of rational individuals in networks, we propose a novel game theory inspired algorithm to determine communities in networks. The algorithm is decentralized and only uses local information at each node. We show the efficacy of the proposed algorithm through extensive experimentation on several real world social network data sets.

An Event Driven Fusion Approach for Enjoyment Recognition in Real-time

Social signals and interpretation of carried information is of high importance in Human Computer Interaction. Often used for affect recognition, the cues within these signals are displayed in various modalities. Fusion of multi-modal signals is a natural and interesting way to improve automatic classification of emotions transported in social signals. Throughout most present studies, uni-modal affect recognition as well as multi-modal fusion, decisions are forced for fixed annotation segments across all modalities. In this paper, we investigate the less prevalent approach of event driven fusion, which indirectly accumulates asynchronous events in all modalities for final predictions. We present a fusion approach, handling short-timed events in a vector space, which is of special interest for real-time applications. We compare results of segmentation based uni-modal classification and fusion schemes to the event driven fusion approach. The evaluation is carried out via detection of enjoyment-episodes within the audiovisual Belfast Story-Telling Corpus.

Falling in love outwards: Eco-social work and the sensuous event

Summary: Social work is a discipline that focuses on the person-in-the-environment. However, the social domains of influence have traditionally received more attention from the profession compared with the impact of the natural world on human well-being. With the development of ecological theories, and growing threats to the environment, this gap has been addressed and now the notion of eco-social work is attracting more interest. This article builds on this corpus of work by exploring, and augmenting, the thinking of the philosopher, David Abram, and his phenomenological investigation of perception, meaning, embodiment, language and Indigenous experience. The implications for eco-social work are then addressed.

Findings: The development of Abram’s philosophical thesis is charted by reviewing his presentation of the ideas of the European phenomenologists, Edmund Husserl and Maurice Merleau-Ponty. It is argued that Abram uses phenomenology to explore the character of perception and the sensual foundations of language which, in Indigenous cultures, are connected with the natural world. A gap in Abram’s thinking is then revealed showing the need to set human perception and language within an understanding of power. Overall, this re-worked thesis is underpinned by a meta-narrative in which ecology engages with philosophy, psychology and Indigenous experience.

Applications: By grounding such ideas in Slavoj Žižek’s construct of the sensuous event, three applications within social work are evinced, namely: (i) reflecting on the sensuous event in social work education; (ii) rekindling the sensuous event with Indigenous Peoples; and (iii) instigating the sensuous event with non-Indigenous populations.

3D-nanostructured Au electrodes for the event-specific detection of MON810 transgenic maize

In the present work, the development of a genosensor for the event-specific detection of MON810 transgenic maize is proposed. Taking advantage of nanostructuration, a cost-effective three dimensional electrode was fabricated and a ternary monolayer containing a dithiol, a monothiol and the thiolated capture probe was optimized to minimize the unspecific signals. A sandwich format assay was selected as a way of precluding inefficient hybridization associated with stable secondary target structures. A comparison between the analytical performance of the Au nanostructured electrodes and commercially available screen-printed electrodes highlighted the superior performance of the nanostructured ones. Finally, the genosensor was effectively applied to detect the transgenic sequence in real samples, showing its potential for future quantitative analysis.

Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner).

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

In situ detection of the hypermethylation-induced inactivation of the p16 gene as an early event in oncogenesis

We have developed a technique, methylation-specific PCR in situ hybridization (MSP-ISH), which allows for the methylation status of specific DNA sequences to be visualized in individual cells. We use MSP-ISH to monitor the timing and consequences of aberrant hypermethylation of the p16 tumor suppresser gene during the progression of cancers of the lung and cervix. Hypermethylation of p16 was localized only to the neoplastic cells in both in situ lesions and invasive cancers, and was associated with loss of p16 protein expression. MSP-ISH allowed us to dissect the surprising finding that p16 hypermethylation occurs in cervical carcinoma. This tumor is associated with infection of the oncogenic human papillomavirus, which expresses a protein, E7, that inactivates the retinoblastoma (Rb) protein. Thus, simultaneous Rb and p16 inactivation would not be needed to abrogate the critical cyclin D–Rb pathway. MSP-ISH reveals that p16 hypermethylation occurs heterogeneously within early cervical tumor cell populations that are separate from those expressing viral E7 transcripts. In advanced cervical cancers, the majority of cells have a hypermethylated p16, lack p16 protein, but no longer express E7. These data suggest that p16 inactivation is selected as the most effective mechanism of blocking the cyclin D–Rb pathway during the evolution of an invasive cancer from precursor lesions. These studies demonstrate that MSP-ISH is a powerful approach for studying the dynamics of aberrant methylation of critical tumor suppressor genes during tumor evolution.

Detection of concealed information using event-related potentials

Existe un interés considerable en hallar métodos que nos ayuden a saber cuándo una persona miente y cuándo dice la verdad desde un punto de vista forense. Actualmente, una de las líneas de investigación se inclina hacia el uso de potenciales relacionados con eventos. Se pretende hacer una revisión de los artículos que estudian estos procedimientos mediante distintos métodos: propiedades, fiabilidad, validez y limitaciones. Los resultados indican tasas de acierto en la discriminación de culpables en un rango de 7 al 100 por ciento, y en la de inocentes de 31 a 100 por ciento. La gran variabilidad y la posibilidad de “falsear” las respuestas llevan a cuestionar la inexactitud utilizada en algunos círculos mediáticos respecto a las cualidades y finalidades de dicha prueba. Se concluye la necesidad de profundizar más la posibilidad de que esta prueba sea utilizada con fines forenses.

Social Security could improve its management and detection of postentitlement changes by using postadjudication appraisal data /

"January 20, 1984."