Fintech strategy: E-Reputation

Digital media use applied to professional atmosphere is increasing its relevance in the companies of the 21st century. As a consequence, organisations should bear this fact in mind and adapt their corporate strategies to the new market demands. Online social networks offer customers the possibility to bring attention to issues they believe need to be addresses by organisations. Added to this, e-reputation plays a pivotal role in determining a company's competitive advantage. This paper studies the role of e-communications in an organisation's strategy and how analytics and modelling techniques are used to take advantage of the digital media content. It discusses the historical reputational challenged experienced by banks and how can the current fintech industry take advantage of past errors. Finally, an overview of effective mechanisms to improve e-reputation is presented

Overcoming the Digital Tsunami in e-Discovery: is Visual Analysis the Answer?

New technologies are generating potentially discoverable evidence in electronic form in ever increasing volumes. As a result, traditional techniques of document search and retrieval in pursuit of electronic discovery in litigation are becoming less viable. One potential new technological solution to the e-discovery search and retrieval challenge is Visual Analysis (VA). VA is a technology that combines the computational power of the computer with graphical representations of large datasets to enable interactive analytic capabilities. This article provides an overview of VA technology and how it is being applied in the analysis of e-mail and other electronic documents in the field of e-discovery, as well as discussing several challenges and limitations of the technology. The article concludes that VA has the potential to overcome some of the limitations of current search and retrieval techniques, but that addressing the digital tsunami is more likely to be achieved by using VA in combination with other search and retrieval technologies in the context of creating an effective data governance program

eHealth Conversations : using information management, dialogue, and knowledge exchange to move toward universal

The publication of eHealth Conversations, developed with the support of the Spanish Agency for International Development Cooperation (AECID), represents a major step forward for the PAHO/WHO Strategy, since it explores ways of implementing regional mechanisms with free and equitable access to information and knowledge sharing. These initiatives aim to advance the goals of more informed, equitable, competitive, and democratic societies, where access to health information is considered a basic right. This publication is one of the instruments used by PAHO/WHO to develop the initiatives outlined in the Strategy, which coincides with the global eHealth strategy. One of the fundamental needs for the improvement of eHealth is the dissemination of information, and PAHO/WHO is assuming a leading role in this effort. The development of this new electronic publication is a key step in disseminating information that will be useful for decision makers on applying these technologies for the health of the Americas. This electronic book is one of the products of PAHO/WHO’s project: “eHealth Conversations: Using Information Management, Dialogue, and Knowledge Exchange to Move Toward Universal Access to Health.” Participants in these conversations included experts on electronic health and other specialties. Through virtual dialogues, the experts contributed with knowledge and reflections on the present and the future of eHealth in the Americas, analyzed the situation, and made recommendations for the implementation of electronic health initiatives. These recommendations are not only intended for PAHO/ WHO, but also for governments and the private sector. The aim of the project is to guarantee the convergence of local, national, and regional initiatives regarding the adoption and application of ICTs for public health, with special attention on critical issues in this field. It also intends to strengthen individual and collective capacities of health workers and institutions, connecting them in a network of on-line health networks, as well as to reinforce the PAHO/WHO eHealth program.Acknowledge the Spanish Agency for International Development Cooperation (AECID) for its financial support in preparing this publication and developing the project titled “eHealth Conversations: Using Information Management, Dialogue, and Knowledge Exchange to Move Toward Universal Access to Health;

Information technology breeds new age terrorism

This thesis examines the impact information technologies have had on the age-old phenomenon of terrorism. It looks at how terrorism has evolved into what has come to be known as information terrorism over this Information Era. Information revolution has introduced a new paradigm called Information Warfare for conflict among nations based upon attacking information infrastructures. The political attractions and deterrents to using these new information warfare methods are discussed at great length. The information age is affecting not only the types of targets and weapons terrorist choose, but also the ways in which such groups operate and structure their organizations. This paper will discuss how several of the dangerous terrorist organizations today are using information technology such as computers, software, telecommunication devices, and the Internet - to better organize and coordinate dispersed activities. Like the large numbers of private corporations that have embraced IT to operate more efficiently and with greater flexibility, terrorists are harnessing the power of IT to enable new operational doctrines and forms of organization. The final chapters offer prescriptions and solutions for integrating information technology into the framework of the United States\u27 grand strategy to decrease the security threat and facilitate international cooperation in this area

Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

[ES] Hoy en día, la sociedad tiene acceso y posibilidad de contribuir a grandes cantidades de contenidos presentes en Internet, como redes sociales, periódicos online, foros, blogs o plataformas de contenido multimedia. Todo este tipo de medios han tenido, durante los últimos años, un impacto abrumador en el día a día de individuos y organizaciones, siendo actualmente medios predominantes para compartir, debatir y analizar contenidos online. Por este motivo, resulta de interés trabajar sobre este tipo de plataformas, desde diferentes puntos de vista, bajo el paraguas del Procesamiento del Lenguaje Natural. En esta tesis nos centramos en dos áreas amplias dentro de este campo, aplicadas al análisis de contenido en línea: análisis de texto en redes sociales y resumen automático. En paralelo, las redes neuronales también son un tema central de esta tesis, donde toda la experimentación se ha realizado utilizando enfoques de aprendizaje profundo, principalmente basados en mecanismos de atención. Además, trabajamos mayoritariamente con el idioma español, por ser un idioma poco explorado y de gran interés para los proyectos de investigación en los que participamos. Por un lado, para el análisis de texto en redes sociales, nos enfocamos en tareas de análisis afectivo, incluyendo análisis de sentimientos y detección de emociones, junto con el análisis de la ironía. En este sentido, se presenta un enfoque basado en Transformer Encoders, que consiste en contextualizar \textit{word embeddings} pre-entrenados con tweets en español, para abordar tareas de análisis de sentimiento y detección de ironía. También proponemos el uso de métricas de evaluación como funciones de pérdida, con el fin de entrenar redes neuronales, para reducir el impacto del desequilibrio de clases en tareas \textit{multi-class} y \textit{multi-label} de detección de emociones. Adicionalmente, se presenta una especialización de BERT tanto para el idioma español como para el dominio de Twitter, que tiene en cuenta la coherencia entre tweets en conversaciones de Twitter. El desempeño de todos estos enfoques ha sido probado con diferentes corpus, a partir de varios \textit{benchmarks} de referencia, mostrando resultados muy competitivos en todas las tareas abordadas. Por otro lado, nos centramos en el resumen extractivo de artículos periodísticos y de programas televisivos de debate. Con respecto al resumen de artículos, se presenta un marco teórico para el resumen extractivo, basado en redes jerárquicas siamesas con mecanismos de atención. También presentamos dos instancias de este marco: \textit{Siamese Hierarchical Attention Networks} y \textit{Siamese Hierarchical Transformer Encoders}. Estos sistemas han sido evaluados en los corpora CNN/DailyMail y NewsRoom, obteniendo resultados competitivos en comparación con otros enfoques extractivos coetáneos. Con respecto a los programas de debate, se ha propuesto una tarea que consiste en resumir las intervenciones transcritas de los ponentes, sobre un tema determinado, en el programa "La Noche en 24 Horas". Además, se propone un corpus de artículos periodísticos, recogidos de varios periódicos españoles en línea, con el fin de estudiar la transferibilidad de los enfoques propuestos, entre artículos e intervenciones de los participantes en los debates. Este enfoque muestra mejores resultados que otras técnicas extractivas, junto con una transferibilidad de dominio muy prometedora.[CA] Avui en dia, la societat té accés i possibilitat de contribuir a grans quantitats de continguts presents a Internet, com xarxes socials, diaris online, fòrums, blocs o plataformes de contingut multimèdia. Tot aquest tipus de mitjans han tingut, durant els darrers anys, un impacte aclaparador en el dia a dia d'individus i organitzacions, sent actualment mitjans predominants per compartir, debatre i analitzar continguts en línia. Per aquest motiu, resulta d'interès treballar sobre aquest tipus de plataformes, des de diferents punts de vista, sota el paraigua de l'Processament de el Llenguatge Natural. En aquesta tesi ens centrem en dues àrees àmplies dins d'aquest camp, aplicades a l'anàlisi de contingut en línia: anàlisi de text en xarxes socials i resum automàtic. En paral·lel, les xarxes neuronals també són un tema central d'aquesta tesi, on tota l'experimentació s'ha realitzat utilitzant enfocaments d'aprenentatge profund, principalment basats en mecanismes d'atenció. A més, treballem majoritàriament amb l'idioma espanyol, per ser un idioma poc explorat i de gran interès per als projectes de recerca en els que participem. D'una banda, per a l'anàlisi de text en xarxes socials, ens enfoquem en tasques d'anàlisi afectiu, incloent anàlisi de sentiments i detecció d'emocions, juntament amb l'anàlisi de la ironia. En aquest sentit, es presenta una aproximació basada en Transformer Encoders, que consisteix en contextualitzar \textit{word embeddings} pre-entrenats amb tweets en espanyol, per abordar tasques d'anàlisi de sentiment i detecció d'ironia. També proposem l'ús de mètriques d'avaluació com a funcions de pèrdua, per tal d'entrenar xarxes neuronals, per reduir l'impacte de l'desequilibri de classes en tasques \textit{multi-class} i \textit{multi-label} de detecció d'emocions. Addicionalment, es presenta una especialització de BERT tant per l'idioma espanyol com per al domini de Twitter, que té en compte la coherència entre tweets en converses de Twitter. El comportament de tots aquests enfocaments s'ha provat amb diferents corpus, a partir de diversos \textit{benchmarks} de referència, mostrant resultats molt competitius en totes les tasques abordades. D'altra banda, ens centrem en el resum extractiu d'articles periodístics i de programes televisius de debat. Pel que fa a l'resum d'articles, es presenta un marc teòric per al resum extractiu, basat en xarxes jeràrquiques siameses amb mecanismes d'atenció. També presentem dues instàncies d'aquest marc: \textit{Siamese Hierarchical Attention Networks} i \textit{Siamese Hierarchical Transformer Encoders}. Aquests sistemes s'han avaluat en els corpora CNN/DailyMail i Newsroom, obtenint resultats competitius en comparació amb altres enfocaments extractius coetanis. Pel que fa als programes de debat, s'ha proposat una tasca que consisteix a resumir les intervencions transcrites dels ponents, sobre un tema determinat, al programa "La Noche en 24 Horas". A més, es proposa un corpus d'articles periodístics, recollits de diversos diaris espanyols en línia, per tal d'estudiar la transferibilitat dels enfocaments proposats, entre articles i intervencions dels participants en els debats. Aquesta aproximació mostra millors resultats que altres tècniques extractives, juntament amb una transferibilitat de domini molt prometedora.[EN] Nowadays, society has access, and the possibility to contribute, to large amounts of the content present on the internet, such as social networks, online newspapers, forums, blogs, or multimedia content platforms. These platforms have had, during the last years, an overwhelming impact on the daily life of individuals and organizations, becoming the predominant ways for sharing, discussing, and analyzing online content. Therefore, it is very interesting to work with these platforms, from different points of view, under the umbrella of Natural Language Processing. In this thesis, we focus on two broad areas inside this field, applied to analyze online content: text analytics in social media and automatic summarization. Neural networks are also a central topic in this thesis, where all the experimentation has been performed by using deep learning approaches, mainly based on attention mechanisms. Besides, we mostly work with the Spanish language, due to it is an interesting and underexplored language with a great interest in the research projects we participated in. On the one hand, for text analytics in social media, we focused on affective analysis tasks, including sentiment analysis and emotion detection, along with the analysis of the irony. In this regard, an approach based on Transformer Encoders, based on contextualizing pretrained Spanish word embeddings from Twitter, to address sentiment analysis and irony detection tasks, is presented. We also propose the use of evaluation metrics as loss functions, in order to train neural networks for reducing the impact of the class imbalance in multi-class and multi-label emotion detection tasks. Additionally, a specialization of BERT both for the Spanish language and the Twitter domain, that takes into account inter-sentence coherence in Twitter conversation flows, is presented. The performance of all these approaches has been tested with different corpora, from several reference evaluation benchmarks, showing very competitive results in all the tasks addressed. On the other hand, we focused on extractive summarization of news articles and TV talk shows. Regarding the summarization of news articles, a theoretical framework for extractive summarization, based on siamese hierarchical networks with attention mechanisms, is presented. Also, we present two instantiations of this framework: Siamese Hierarchical Attention Networks and Siamese Hierarchical Transformer Encoders. These systems were evaluated on the CNN/DailyMail and the NewsRoom corpora, obtaining competitive results in comparison to other contemporary extractive approaches. Concerning the TV talk shows, we proposed a text summarization task, for summarizing the transcribed interventions of the speakers, about a given topic, in the Spanish TV talk shows of the ``La Noche en 24 Horas" program. In addition, a corpus of news articles, collected from several Spanish online newspapers, is proposed, in order to study the domain transferability of siamese hierarchical approaches, between news articles and interventions of debate participants. This approach shows better results than other extractive techniques, along with a very promising domain transferability.González Barba, JÁ. (2021). Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172245TESI

The ultra-marathoners of human smuggling: defending forward against dark networks that can transport terrorists across American land borders

National legislation requires America’s homeland security agencies to disrupt transnational human smuggling organizations capable of transporting terrorist travelers to all U.S. borders. Federal agencies have responded with programs targeting extreme-distance human smuggling networks that transport higher-risk immigrants known as special interest aliens (SIAs) from some 35 countries of interest in the Middle East, North Africa, and Asia where terrorist organizations operate. Yet ineffectiveness and episodic targeting are indicated, in part by continued migration from those countries to the U.S. southwestern border since 9/11. Should an attack linked to SIA smuggling networks occur, homeland security leaders likely will be required to improve counter-SIA interdiction or may choose to do so preemptively. This thesis asks how SIA smuggling networks function as systems and, based on this analysis, if their most vulnerable fail points can be identified for better intervention targeting. Using NVivo qualitative analysis software, the study examined 19 U.S. court prosecutions of SIA smugglers and other data to produce 20 overarching conclusions demonstrating how SIA smuggling functions. From these 20 conclusions, seven leverage points were extracted and identified for likely law enforcement intervention success. Fifteen disruption strategies, tailored to the seven leverage points, are recommended.Outstanding ThesisManager and Program Specialist, Texas Department of Public Safety, Intelligence and Counterterrorism DivisionApproved for public release; distribution is unlimited

Undergraduate and Graduate Course Descriptions, 2022 Summer

Wright State University undergraduate and graduate course descriptions from Summer 2022