Hybrid consumption paths in the attribute space: A model and application with scanner data

This paper presents and estimates a dynamic choice model in the attribute space considering rational consumers. In light of the evidence of several state-dependence patterns, the standard attribute-based model is extended by considering a general utility function where pure inertia and pure variety-seeking behaviors can be explained in the model as particular linear cases. The dynamics of the model are fully characterized by standard dynamic programming techniques. The model presents a stationary consumption pattern that can be inertial, where the consumer only buys one product, or a variety-seeking one, where the consumer shifts among varied products.We run some simulations to analyze the consumption paths out of the steady state. Underthe hybrid utility assumption, the consumer behaves inertially among the unfamiliar brandsfor several periods, eventually switching to a variety-seeking behavior when the stationary levels are approached. An empirical analysis is run using scanner databases for three different product categories: fabric softener, saltine cracker, and catsup. Non-linear specifications provide the best fit of the data, as hybrid functional forms are found in all the product categories for most attributes and segments. These results reveal the statistical superiority of the non-linear structure and confirm the gradual trend to seek variety as the level of familiarity with the purchased items increases.

A PHR system with policy-based fine-grained access control and revocation mechanism

Collaborative sharing of information is becoming much more needed technique to achieve complex goals in today's fast-paced tech-dominant world. Personal Health Record (PHR) system has become a popular research area for sharing patients informa- tion very quickly among health professionals. PHR systems store and process sensitive information, which should have proper security mechanisms to protect patients' private data. Thus, access control mechanisms of the PHR should be well-defined. Secondly, PHRs should be stored in encrypted form. Cryptographic schemes offering a more suitable solution for enforcing access policies based on user attributes are needed for this purpose. Attribute-based encryption can resolve these problems, we propose a patient-centric framework that protects PHRs against untrusted service providers and malicious users. In this framework, we have used Ciphertext Policy Attribute Based Encryption scheme as an efficient cryptographic technique, enhancing security and privacy of the system, as well as enabling access revocation. Patients can encrypt their PHRs and store them on untrusted storage servers. They also maintain full control over access to their PHR data by assigning attribute-based access control to selected data users, and revoking unauthorized users instantly. In order to evaluate our system, we implemented CP-ABE library and web services as part of our framework. We also developed an android application based on the framework that allows users to register into the system, encrypt their PHR data and upload to the server, and at the same time authorized users can download PHR data and decrypt it. Finally, we present experimental results and performance analysis. It shows that the deployment of the proposed system would be practical and can be applied into practice.

Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome.

PURPOSE: Myeloma is a clonal malignancy of plasma cells. Poor-prognosis risk is currently identified by clinical and cytogenetic features. However, these indicators do not capture all prognostic information. Gene expression analysis can be used to identify poor-prognosis patients and this can be improved by combination with information about DNA-level changes. EXPERIMENTAL DESIGN: Using single nucleotide polymorphism-based gene mapping in combination with global gene expression analysis, we have identified homozygous deletions in genes and networks that are relevant to myeloma pathogenesis and outcome. RESULTS: We identified 170 genes with homozygous deletions and corresponding loss of expression. Deletion within the "cell death" network was overrepresented and cases with these deletions had impaired overall survival. From further analysis of these events, we have generated an expression-based signature associated with shorter survival in 258 patients and confirmed this signature in data from two independent groups totaling 800 patients. We defined a gene expression signature of 97 cell death genes that reflects prognosis and confirmed this in two independent data sets. CONCLUSIONS: We developed a simple 6-gene expression signature from the 97-gene signature that can be used to identify poor-prognosis myeloma in the clinical environment. This signature could form the basis of future trials aimed at improving the outcome of poor-prognosis myeloma.

From perfect strangers to research partners: a study of relationships, resource sharing and perceived benefits within R&D cooperation networks

Doctoral thesis in Marketing and Strategy.

Destination image of Girona: an online text-mining approach

The main objective of this Master Thesis is to discover more about Girona’s image as a tourism destination from different agents’ perspective and to study its differences on promotion or opinions. In order to meet this objective, three components of Girona’s destination image will be studied: attribute-based component, the holistic component, and the affective component. It is true that a lot of research has been done about tourism destination image, but it is less when we are talking about the destination of Girona. Some studies have already focused on Girona as a tourist destination, but they used a different type of sample and different methodological steps. This study is new among destination studies in the sense that it is based only on textual online data and it follows a methodology based on text-miming. Text-mining is a kind of methodology that allows people extract relevant information from texts. Also, after this information is extracted by this methodology, some statistical multivariate analyses are done with the aim of discovering more about Girona’s tourism image

Visualizing key authenticity: turning your face into your public key

La informació biomètrica s'ha convertit en una tecnologia complementària a la criptografia que permet administrar còmodament les dades criptogràfiques. Són útils dues necessitats importants: en primer lloc, posar aquestes dades sempre a mà i, a més, fent fàcilment identificable el seu legítim propietari. En aquest article es proposa un sistema que integra la signatura biomètrica de reconeixement facial amb un esquema de signatura basat en la identitat, de manera que la cara de l'usuari esdevé la seva clau pública i la ID del sistema. D'aquesta manera, altres usuaris poden verificar els missatges utilitzant fotos del remitent, proporcionant un intercanvi raonable entre la seguretat del sistema i la usabilitat, així com una manera molt més senzilla d'autenticar claus públiques i processos de distribució.

From 'Security for Privacy' to 'Privacy for Security'

This article envisions the use of context-awareness to improve single sign-on solutions (SSO) for mobile users. The attribute-based SSO is expected to increase users' perceived ease of use of the system and service providers' authentication security of the application. From these two features we derive two value propositions for a new business model for mobile platforms. The business model can be considered as an instantiation of the privacy-friendly business model pattern presented in our previous work, reinforcing our claim that privacy-friendly value propositions are possible and can be used to obtain a competitive advantage.

Public values for badgers, bovine TB reduction and management strategies

This paper applies an attribute-based stated choice experiment approach to estimate the value that society places on changes to the size of the badger population in England and Wales. The study was undertaken in the context of a rising incidence of bovine tuberculosis (bTB) in cattle and the government's review of current bTB control policy. This review includes consideration of culling badgers to reduce bTB in cattle, since badgers are thought to be an important wildlife reservoir for the disease. The design of the CE involved four attributes (size of badger population, cattle slaughtered due to bTB, badger management strategy and household tax) at four levels with eight choice sets of two alternatives presented to respondents. Telephone interviews were undertaken with over 400 respondents, which elicited their attitudes and preferences concerning badgers, bTB in cattle and badger management strategies. The study estimated a willingness to pay of 0.10 pound per household per year per 100,000 badgers and 1.52 pound per household per year per 10,000 cattle slaughtered due to bTB which aggregated to 22 per badger and 3298 pound per bTB slaughtered animal for all households in England and Wales. Management strategy toward badgers had a very high valuation, highlighting the emotive issue of badger culling for respondents and the importance of government policy towards badgers.

Spatio-Temporal Event Model for Cyber-Physical Systems

The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatiotemporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.

Partitioning the Choice Task Makes Starbucks Coffee Taste Better

Consumers are often less satisfied with a product chosen from a large assortment than a limited one. Experienced choice difficulty presumably causes this as consumers have to engage in a great number of individual comparisons. In two studies we tested whether partitioning the choice task so that consumers decided sequentially on each individual attribute may provide a solution. In a Starbucks coffee house, consumers who chose from the menu rated the coffee as less tasty when chosen from a large rather than a small assortment. However, when the consumers chose it by sequentially deciding about one attribute at a time, the effect reversed. In a tailored-suit customization, consumers who chose multiple attributes at a time were less satisfied with their suit, compared to those who chose one attribute at a time. Sequential attribute-based processing proves to be an effective strategy to reap the benefits of a large assortment.

Contribución a la automatización de sistemas de respuesta frente a intrusiones mediante ontologías

La seguridad en redes informáticas es un área que ha sido ampliamente estudiada y objeto de una extensa investigación en los últimos años. Debido al continuo incremento en la complejidad y sofisticación de los ataques informáticos, el aumento de su velocidad de difusión, y la lentitud de reacción frente a las intrusiones existente en la actualidad, se hace patente la necesidad de mecanismos de detección y respuesta a intrusiones, que detecten y además sean capaces de bloquear el ataque, y mitiguen su impacto en la medida de lo posible. Los Sistemas de Detección de Intrusiones o IDSs son tecnologías bastante maduras cuyo objetivo es detectar cualquier comportamiento malicioso que ocurra en las redes. Estos sistemas han evolucionado rápidamente en los últimos años convirtiéndose en herramientas muy maduras basadas en diferentes paradigmas, que mejoran su capacidad de detección y le otorgan un alto nivel de fiabilidad. Por otra parte, un Sistema de Respuesta a Intrusiones (IRS) es un componente de seguridad que puede estar presente en la arquitectura de una red informática, capaz de reaccionar frente a los incidentes detectados por un Sistema de Detección de Intrusiones (IDS). Por desgracia, esta tecnología no ha evolucionado al mismo ritmo que los IDSs, y la reacción contra los ataques detectados es lenta y básica, y los sistemas presentan problemas para ejecutar respuestas de forma automática. Esta tesis doctoral trata de hacer frente al problema existente en la reacción automática frente a intrusiones, mediante el uso de ontologías, lenguajes formales de especificación de comportamiento y razonadores semánticos como base de la arquitectura del sistema de un sistema de respuesta automática frente a intrusiones o AIRS. El objetivo de la aproximación es aprovechar las ventajas de las ontologías en entornos heterogéneos, además de su capacidad para especificar comportamiento sobre los objetos que representan los elementos del dominio modelado. Esta capacidad para especificar comportamiento será de gran utilidad para que el AIRS infiera la respuesta óptima frente a una intrusión en el menor tiempo posible. Abstract Security in networks is an area that has been widely studied and has been the focus of extensive research over the past few years. The number of security events is increasing, and they are each time more sophisticated, and quickly spread, and slow reaction against intrusions, there is a need for intrusion detection and response systems to dynamically adapt so as to better detect and respond to attacks in order to mitigate them or reduce their impact. Intrusion Detection Systems (IDSs) are mature technologies whose aim is detecting malicious behavior in the networks. These systems have quickly evolved and there are now very mature tools based on different paradigms (statistic anomaly-based, signature-based and hybrids) with a high level of reliability. On the other hand, Intrusion Response System (IRS) is a security technology able to react against the intrusions detected by IDS. Unfortunately, the state of the art in IRSs is not as mature as with IDSs. The reaction against intrusions is slow and simple, and these systems have difficulty detecting intrusions in real time and triggering automated responses. This dissertation is to address the existing problem in automated reactions against intrusions using ontologies, formal behaviour languages and semantic reasoners as the basis of the architecture of an automated intrusion response systems or AIRS. The aim is to take advantage of ontologies in heterogeneous environments, in addition to its ability to specify behavior of objects representing the elements of the modeling domain. This ability to specify behavior will be useful for the AIRS in the inference process of the optimum response against an intrusion, as quickly as possible.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Toward Never-Ending Object Learning for Robots

Thesis (Ph.D.)--University of Washington, 2016-06

Integrating structured data using property precedence

Data integration systems offer uniform access to a set of autonomous and heterogeneous data sources. One of the main challenges in data integration is reconciling semantic differences among data sources. Approaches that been used to solve this problem can be categorized as schema-based and attribute-based. Schema-based approaches use schema information to identify the semantic similarity in data; furthermore, they focus on reconciling types before reconciling attributes. In contrast, attribute-based approaches use statistical and structural information of attributes to identify the semantic similarity of data in different sources. This research examines an approach to semantic reconciliation based on integrating properties expressed at different levels of abstraction or granularity using the concept of property precedence. Property precedence reconciles the meaning of attributes by identifying similarities between attributes based on what these attributes represent in the real world. In order to use property precedence for semantic integration, we need to identify the precedence of attributes within and across data sources. The goal of this research is to develop and evaluate a method and algorithms that will identify precedence relations among attributes and build property precedence graph (PPG) that can be used to support integration.

Sharing in the Rain: Secure and Efficient Data Sharing for the Cloud

Cloud storage has rapidly become a cornerstone of many businesses and has moved from an early adopters stage to an early majority, where we typically see explosive deployments. As companies rush to join the cloud revolution, it has become vital to create the necessary tools that will effectively protect users' data from unauthorized access. Nevertheless, sharing data between multiple users' under the same domain in a secure and efficient way is not trivial. In this paper, we propose Sharing in the Rain – a protocol that allows cloud users' to securely share their data based on predefined policies. The proposed protocol is based on Attribute-Based Encryption (ABE) and allows users' to encrypt data based on certain policies and attributes. Moreover, we use a Key-Policy Attribute-Based technique through which access revocation is optimized. More precisely, we show how to securely and efficiently remove access to a file, for a certain user that is misbehaving or is no longer part of a user group, without having to decrypt and re-encrypt the original data with a new key or a new policy.