Study of wrist pulse signals using time domain spatial features

Blood travels throughout the body and thus its flow is modulated by changes in body condition. As a consequence, the wrist pulse signal contains important information about the status of the human body. In this work we have employed signal processing techniques to extract important information from these signals. Radial artery pulse pressure signals are acquired at wrist position noninvasively for several subjects for two cases of interest, viz. before and after exercise, and before and after lunch. Further analysis is performed by fitting a bi-modal Gaussian model to the data and extracting spatial features from the fit. The spatial features show statistically significant (p < 0.001) changes between the groups for both the cases, which indicates that they are effective in distinguishing the changes taking place due to exercise or food intake. Recursive cluster elimination based support vector machine classifier is used to classify between the groups. A high classification accuracy of 99.71% is achieved for the exercise case and 99.94% is achieved for the lunch case. This paper demonstrates the utility of certain spatial features in studying wrist pulse signals obtained under various experimental conditions. The ability of the spatial features in distinguishing changing body conditions can be potentially used for various healthcare applications. (C) 2015 Elsevier Ltd. All rights reserved.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

Old glass paperweights, their art, construction, and distinguishing features,

Illustrated t.-p. in color.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

The evolution of information while building cross-domain models of a design: A video experiment

Models capturing the connectivity between different domains of a design, e.g. between components and functions, can provide a tool for tracing and analysing aspects of that design. In this paper, video experiments are used to explore the role of cross-domain modelling in building up information about a design. The experiments highlight that cross-domain modelling can be a useful tool to create and structure design information. Findings suggest that consideration of multiple domains encourages discussion during modelling, helps identify design aspects that might otherwise be overlooked, and can help promote consideration of alternative design options. Copyright © 2002-2012 The Design Society. All rights reserved.

Cross-Domain Multitask Learning with Latent Probit Models

Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.

Utilizing cross-domain SOAP Web services using client-side languages in an enterprise mashup platform

Service oriented architectures (SOA) based on Simple Object Access Protocol (SOAP) Web services have attracted the attention of enterprises mainly for business-to-business integration and to create composite applications that execute business processes. An existing problem is the lack of preoccupation with non technical users due to the fact that to create a composite application to fulfill users needs, it is necessary to be in contact with IT staff. To overcome this issue, enterprises can take advantage of web 2.0, 'introducing in the development stage some technologies like mashups and some concepts like user empowerment, collaborative work and collective intelligence. Some results [3] [13] have shown how web 2.0 concepts can help non technical users to produce relative complex business processes. However, traditional enterprise requirements goes beyond typical web 2.0 solutions in several aspects: (1) traditional enterprise systems are based on heterogeneous stack of technologies that are not directly exploitable from a web-based client (where SOAP web services play an important role); (2) web browsers set some cross-domain security constraints making difficult to integrate services from diverse domains. In this paper, a contribution to two web 2.0 research projects [14] [15] partially solves the problems described: provide a way to invoke cross-domain backend services (based on SOAP technologies) directly only using clientside languages, without a need for any adaptation layer. © 2010 ACM.

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.

Terminology Cross-Domain Final Report

The InterPARES 2 Terminology Cross-Domain has created three terminological instruments in service to the project, and by extension, Archival Science. Over the course of the five-year project this Cross-Domain has collected words, definition, and phrases from extant documents, research tools, models, and direct researcher submission and discussion. From these raw materials, the Cross-Domain has identified a systematic and pragmatic way establishing a coherent view on the concepts involved in dynamic, experiential, and interactive records and systems in the arts, sciences, and e-government.The three terminological instruments are the Glossary, Dictionary, and Ontologies. The first of these is an authoritative list of terms and definitions that are core to our understanding of the evolving records creation, keeping, and preservation environments. The Dictionary is a tool used to facilitate interdisciplinary communication. It contains multiple definitions for terms, from multiple disciplines. By using this tool, researchers can see how Archival Science deploys terminology compared to Computer Science, Library and Information Science, or Arts, etc. The third terminological instrument, the Ontologies, identify explicit relationships between concepts of records. This is useful for communicating the nuances of Diplomatics in the dynamic, experiential, and interactive environment.All three of these instruments were drawn from a Register of terms gathered over the course of the project. This Register served as a holding place for terms, definitions, and phrases, and allowed researchers to discuss, comment on, and modify submissions. The Register and the terminological instruments were housed in the Terminology Database. The Database provides searching, display, and file downloads – making it easy to navigate through the terminological instruments.Terminology used in InterPARES 1 and the UBC Project was carried forward to this Database. In this sense, we are building on our past knowledge, and making it relevant to the contemporary environment.

Investigating the Roles and Requirements, Manifestations and Management of Metadata in the Creation of Reliable and Preservation of Authentic Electronic Entities Created by Dynamic, Interactive and Experiential Systems: Report on the Work and Findings of the Interpares 2 Description Cross Domain Group

Metadata that is associated with either an information system or an information object for purposes of description, administration, legal requirements, technical functionality, use and usage, and preservation, plays a critical role in ensuring the creation, management, preservation and use and re-use of trustworthymaterials, including records. Recordkeeping1 metadata, of which one key type is archival description, plays a particularly important role in documenting the reliability and authenticity of records and recordkeeping systemsas well as the various contexts (legal-administrative, provenancial, procedural, documentary, and technical) within which records are created and kept as they move across space and time. In the digital environment, metadata is also the means by which it is possible to identify how record components – those constituent aspects of a digital record that may be managed, stored and used separately by the creator or the preserver – can be reassembled to generate an authentic copy of a record or reformulated per a user’s request as a customized output package.Issues relating to the creation, capture, management and preservation of adequate metadata are, therefore, integral to any research study addressing the reliability and authenticity of digital entities, regardless of the community, sector or institution within which they are being created. The InterPARES 2 Description Cross-Domain Group (DCD) examined the conceptualization, definitions, roles, and current functionality of metadata and archival description in terms of requirements generated by InterPARES 12. Because of the needs to communicate the work of InterPARES in a meaningful way across not only other disciplines, but also different archival traditions; to interface with, evaluate and inform existing standards, practices and other research projects; and to ensure interoperability across the three focus areas of InterPARES2, the Description Cross-Domain also addressed its research goals with reference to wider thinking about and developments in recordkeeping and metadata. InterPARES2 addressed not only records, however, but a range of digital information objects (referred to as “entities” by InterPARES 2, but not to be confused with the term “entities” as used in metadata and database applications) that are the products and by-products of government, scientific and artistic activities that are carried out using dynamic, interactive or experiential digital systems. The nature of these entities was determined through a diplomatic analysis undertaken as part of extensive case studies of digital systems that were conducted by the InterPARES 2 Focus Groups. This diplomatic analysis established whether the entities identified during the case studies were records, non-records that nevertheless raised important concerns relating to reliability and authenticity, or “potential records.” To be determined to be records, the entities had to meet the criteria outlined by archival theory – they had to have a fixed documentary format and stable content. It was not sufficient that they be considered to be or treated as records by the creator. “Potential records” is a new construct that indicates that a digital system has the potential to create records upon demand, but does not actually fix and set aside records in the normal course of business. The work of the Description Cross-Domain Group, therefore, addresses the metadata needs for all three categories of entities.Finally, since “metadata” as a term is used today so ubiquitously and in so many different ways by different communities, that it is in peril of losing any specificity, part of the work of the DCD sought to name and type categories of metadata. It also addressed incentives for creators to generate appropriate metadata, as well as issues associated with the retention, maintenance and eventual disposition of the metadata that aggregates around digital entities over time.