Disrupting Illicit Supply Networks: New Applications of Operations Research and Data Analytics to End Modern Slavery

Report from a 2017 National Science Foundation workshop on promising research directions for applications of operations research and data analytics toward the disruption of illicit supply networks like human trafficking. The workshop was funded by the NSF’s Operations Engineering (ENG) and the Law & Social Sciences Program (SBE) under grant # CMMI-1726895. The report addresses the opportunity to apply advances from the fields of operations research, management science, analytics, machine learning, and data science toward the development of disruptive interventions against illicit networks. Such an extension of the current research agenda for trafficking would move understanding of such dynamic systems from descriptive characterization and predictive estimation toward improved dynamic operational control.Bureau of Business Researc

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

A Feminist Law Meets an Androcentric Criminal Justice System : Gender-Based Violence in Spain

This article discusses how practices in the Spanish criminal justice system relate to Organic Law 1/2004 on measures against gender-based violence. We examine the predominant construction of the problem and the secondary victimization of women. Data were collected from two sources: participant observation at police victim support units and courts dealing with violence against women, and in-depth interviews with abused women and legal and psychosocial professionals. Our analysis has uncovered a lack of institutional resources for detecting psychological violence and negative stereotyping of female victims. We conclude that a gender perspective should be incorporated into criminal justice system practices

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Two-mode networks of New Education: How was the reform movement built up in the United Kingdom?

This dissertation examined the structure and development of the New Education Movement in the United Kingdom between 1875 and 1935. New Education was a reform movement that aimed to address the changing educational needs of societies and accommodate new moral doctrines and discoveries in the field of child psychology. Past literature has shown that the movement was ideologically fragmented. Thus, this dissertation implemented the previously proposed idea of treating New Education as a social movement rather than as a paradigm and analysing the networks between reformers. To identify the key reformers, a reputational sampling method was applied. The study adopted a mixed methods approach, where information about key reformers’ connections to various organisations was gathered from biographies and histories and treated as co-affiliation data (n1 = 58, n2 = 49). These quantitative data were complemented by documentary evidence that was used to validate and illustrate the observations made using two-mode network analysis. Article I presents a case study of the network of Robert Baden-Powell, the founder of the worldwide Scout Movement. The study indicated a change in 1911, when Baden-Powell’s network connections were increasingly dominated by a group of new educationalists to the detriment of the social reformers. This shift demonstrates how Baden-Powell became involved in the wider New Education Movement to make his scheme more relevant to the contemporary political agenda. Article II revealed the overall structure of the New Education Movement and its evolution. Two-mode network analysis showed that until 1905, the movement was in a pre-institutional phase and was divided into two subgroups. From 1905 onwards, the movement became institutionalised and more interconnected. This development followed the appointment of the first professors of education in the UK and the founding of new organisations that aimed to impact society more directly than those established during the previous phase. Article III examined the various roles of reformers during the institutional phase of New Education. Drawing from the literature on social movements, the analysis provided empirical support for the previously proposed idea that there were two kinds of prominence within the movement: that of conveners, who formed close-knit groups with like-minded people, and that of mediators, who built bridges between such groups. How these roles promoted the progress of the movement is discussed based on both quantitative and qualitative data. The results collectively demonstrated that New Education was built on grassroots action and social ties rather than shared ideologies or theories. Analysis of the composition of the subgroups showed a temporal change within the movement. Until the first decade of the 20th century, there was a distinction between social reformers and another group of reformers who focused more strictly on educational questions. After that, this division became less important with regard to the case organisation, the Scout Movement and the New Education Movement as a whole. The movement not only became more influential but also more unified. While filling a gap in New Education research, the dissertation illustrated a method of analysing social movements using co-affiliation data. Creating network data from less structured documentary sources instead of using pre-collected datasets enables contributions to a wider variety of topics. Thus, the study contributes to the scholarly discussion on how network analysis can provide a new tool for revealing the past.--- Tässä väitöskirjassa tarkasteltiin New Education -liikkeen rakennetta ja kehitystä Yhdistyneissä kuningaskunnassa vuosina 1875–1935. New Education -liikkeen tavoitteena oli koulutuksen uudistaminen niin, että se vastaisi paremmin yhteiskuntien muuttuneita tarpeita ja ottaisi huomioon muuttuneita moraalikäsityksiä ja lapsipsykologian kehitystä. Aikaisempi kirjallisuus on osoittanut, että liike oli ideologisesti hajanainen. Väitöskirjassa toteutettiin aiemmin esitetty ajatus New Education -liikeen tarkastelemisesta kansalaisliikkeenä sen sijaan, että sitä tarkasteltaisiin paradigmana. Liikettä analysoitiin uudistajien välisten verkostojen kautta. Keskeisten uudistajien tunnistamiseksi käytettiin maineeseen perustuvaa otantamenetelmää. Tutkimus toteutettiin monimenetelmätutkimuksena, jossa tietoa keskeisistä uudistajien yhteyksistä eri organisaatioihin koottiin elämäkerroista ja historiikeista ja tietoja käsiteltiin jäsenyysverkostona (n1 = 58, n2 = 49). Näitä määrällisiä tietoja täydennettiin dokumenttiaineisolla, jota käytettiin verkostoanalyysillä tehtyjen havaintojen varmentamiseen ja havainnollistamiseen. Artikkeli I esitteli tapaustutkimuksen maailmanlaajuisen partioliikkeen perustajan Robert Baden-Powellin verkostosta. Tutkimus osoitti, että vuonna 1911 tapahtui muutos, jonka jälkeen Baden-Powellin verkostoyhteyksiä hallitsi joukko koulutuksen uudistajia ja yhteydet yhteiskunnallisiin uudistajiin vähenivät. Tämä muutos osoittaa, että Baden-Powell liittyi laajempaan New Education -liikkeeseen, jotta hänen partio-ohjelmansa palvelisi paremmin ajan poliittisia tavoitteita. Artikkeli II kuvasi New Education -liikkeen yleistä rakennetta ja kehitystä. Jäsenyysverkoston analyysi osoitti, että liike oli vuoteen 1905 asti esi-institutionaalisessa vaiheessa ja jakautui kahteen alaryhmään. Vuodesta 1905 lähtien liike institutionalisoitui ja siitä tuli tiiviimpi. Tätä kehitystä edelsivät ensimmäisten kasvatustieteen professorien nimittäminen Isoon-Britanniaan ja sellaisten järjestöjen perustaminen, jotka pyrkivät vaikuttamaan yhteiskuntaan suoremmin kuin esiinstitutionaalisessa vaiheessa perustetut järjestöt. Artikkeli III tarkasteli uudistajien erilaisia rooleja New Education -liikkeen institutionaalisen vaiheen aikana pohjautuen kansalaisliikkeitä käsittelevään tutkimukseen. Tutkimus antoi vahvistusta aikaisemmin esitetylle näkemykselle, että kansalaisliikkeessä voi olla kahdenlaisia tärkeitä rooleja: koollekutsujat muodostivat tiiviitä ryhmiä saman mielisten ihmisten kesken ja välittäjät rakensivat siltoja tällaisten ryhmien välille. Sitä, miten nämä roolit edistivät liikkeen kehitystä, pohditaan sekä määrällisten että laadullisten havaintojen pohjalta. Osatutkimusten tulokset osoittivat, että New Education -liike rakentui ruohonjuuritason toiminnalle ja sosiaalisille sidoksille pikemmin kuin yhteisille ideologioille tai teorioille. Alaryhmien kokoonpanojen analyysi osoitti ajallisen muutoksen. Liike oli 1910-luvulle asti jakautunut yhteiskunnallisten uudistajien ryhmään ja kasvatuskysymyksiin keskittyneeseen ryhmään. Tämän jälkeen jaosta tuli vähemmän tärkeä sekä tutkitun tapausorganisaation, partioliikkeen, että koko New Education -liikkeen kannalta. Liikkeestä ei siten tullut vain vaikutusvaltaisempi, vaan myös yhtenäisempi. Väitöskirja täydensi aiempaa New Education -tutkimusta, minkä lisäksi siinä esiteltiin menetelmä kansalaisliikkeiden analysoimiseen jäsenyysverkostojen avulla. Verkostoaineiston kokoaminen rakenteeltaan vaihtelevista dokumenttilähteistä sen sijaan että hyödynnettäisiin valmiiksi kerättyä aineistoa mahdollistaa menetelmän käyttämisen laajemmin eri tutkimuskohteisiin. Väitöskirja edistää siten tieteellistä keskustelua siitä, miten verkostoanalyysi voi tarjota uuden työkalun menneisyyden tutkimiseen

Potentialfinder - Fostering Network Innovation by Connecting Data Owners Using Scaled Business-Relevant Pattern Recognition and Clustering

The advancement in collection, computing and storage technologies has led to an exponential growth of available data in multiple disciplines. However, the human capacity of analyzing this data does not grow at the same rate, leaving a vast amount of potential disparate, invisible and unused. We want to enhance the capability of humans to automatically find relevant patterns in data to leverage potential in this increasing sea of data. We present an innovation network creation framework and Python library that detects exponential growth patterns from publicly available tabular data. It works as a magnifying glass to reveal the most relevant parts of the data and the processes represented by it. The extracted exponential patterns can be useful for topic or disease detection as well as for organisations such as venture capital and consulting firms to improve investment decisions. Additionally, startups and innovation units in corporates can leverage these information to base their business models on insights into sectors, markets or customer segments with exponential growth. To foster the innovation based on the revealed patterns, we connect the respective data owners that uploaded similar patterns. This paper proposes a framework for networked innovation creation including a) an algorithm to automatically detect exponential, b) approaches to scale its application to public tabular data in different sizes and formats, c) a similarity network connecting the found patterns to innovation networks, d) a clustering to group the data owners and enable co- and crowd innovation. We run experiments on large scale data for all steps to provide evidence for cost-efficiency, scalability and feasibility of the contributions

Automatic human behaviour anomaly detection in surveillance video

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions

The Internet as a means of stuying transnationalism and Diaspora?

Dieser Text beschäftigt sich mit der Frage, wie das Internet verwendet werden kann, um die Entwicklung von Interessen, Kontakten und Netzwerken von Migranten zu untersuchen und so zwischen einer transnationalen Gemeinschaft und einer Diaspora zu unterscheiden. Da das Internet zu einem zentralen Kommunikationsmittel geworden ist, insbesondere für räumlich von einander getrennte Gruppen, können die Analyse von Webseiten, ihre Nutzung und die daraus entstehenden Kommunikationswege dazu beitragen, Unterschiede und Ähnlichkeiten transnationaler Gemeinschaften und Diaspora zu verstehen. Auf Basis empirischer Daten und Erfahrungen aus dem aktuellen Forschungsprojekt der Autoren werden drei Formen nationaler, transnationaler und internationaler Beziehungen diskutiert: transnationale (online) Gemeinschaften, (virtuelle) Diaspora, und ethnische (online) Öffentlichkeiten. This paper addresses the question, how the Internet can be used to study developments in migrants’ interests, contacts and networks and so differentiate between transnational communities and Diaspora. As the Internet has become a central means of communication, especially true for geographically dispersed entities, the analysis of internet sites, their uses and the thus emerging communication paths can add to the understanding of differences and similarities of transnational communities and Diaspora. Based on empirical data and experiences collected as part of the authors´ ongoing research project on the political online activities from migrants in Germany, three different forms of national, transnational and international relationships will be discussed: transnational (online) communities,(virtual) Diasporas, and ethnic (online) public spheres

Towards hypergraph cognitive networks as feature-rich models of knowledge

Semantic networks provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns. Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N=3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed