GetThere: A Rural Passenger Information System Utilising Linked Data & Citizen Sensing

Postprint

Tradução e adaptação transcultural do domínio função física do Patient-Reported Outcomes Measurement Information System – PROMIS® - versão para a língua portuguesa

In 2004, the National Institutes of Health made available the Patient-Reported Outcomes Measurement Information System – PROMIS®, which is constituted of innovative item banks for health assessment. It is based on classical, reliable Patient-Reported Outcomes (PROs) and includes advanced statistical methods, such as Item Response Theory and Computerized Adaptive Test. One of PROMIS® Domain Frameworks is the Physical Function, whose item bank need to be translated and culturally adapted so it can be used in Portuguese speaking countries. This work aimed to translate and culturally adapt the PROMIS® Physical Function item bank into Portuguese. FACIT (Functional Assessment of Chronic Illness Therapy) translation methodology, which is constituted of eight stages for translation and cultural adaptation, was used. Fifty subjects above the age of 18 years participated in the pre-test (seventh stage). The questionnaire was answered by the participants (self-reported questionnaires) by using think aloud protocol, and cognitive and retrospective interviews. In FACIT methodology, adaptations can be done since the beginning of the translation and cultural adaption process, ensuring semantic, conceptual, cultural, and operational equivalences of the Physical Function Domain. During the pre-test, 24% of the subjects had difficulties understanding the items, 22% of the subjects suggested changes to improve understanding. The terms and concepts of the items were totally understood (100%) in 87% of the items. Only four items had less than 80% of understanding; for this reason, it was necessary to chance them so they could have correspondence with the original item and be understood by the subjects, after retesting. The process of translation and cultural adaptation of the PROMIS® Physical Function item bank into Portuguese was successful. This version of the assessment tool must have its psychometric properties validated before being made available for clinical use.

Efficient Prices at Any Cost: Does Algorithmic Trading Deter Information Acquisition?

I demonstrate a powerful tension between acquiring information and incorporating it into asset prices, the two core elements of price discovery. As a salient case, I focus on the transformative rise of algorithmic trading (AT) typically associated with improved price efficiency. Using a measure of the relative information content of prices and a comprehensive panel of 37,325 stock-quarters of SEC market data, I establish instead that algorithmic trading strongly decreases the net amount of information in prices. The increase in price distortions associated with the AT “information gap” is roughly $42.6 billion/year for U.S. common stocks around earnings announcement events alone. Information losses are concentrated among stocks with high shares of algorithmic liquidity takers relative to algorithmic liquidity makers, suggesting that aggressive AT powerfully deters fundamental information acquisition despite its importance for translating available information into prices.

Performance Information and Innovation in the Canadian Government (Working Paper 43)

The presentation made at the conference addressed the issue of linkages between performance information and innovation within the Canadian federal government1. This is a three‐part paper prepared as background to that presentation. • Part I provides an overview of three main sources of performance information - results-based systems, program evaluation, and centrally driven review exercises – and reviews the Canadian experience with them. • Part II identifies and discusses a number of innovation issues that are common to the literature reviewed for this paper. • Part III examines actual and potential linkages between innovation and performance information. This section suggests that innovation in the Canadian federal government tends to cluster into two groups: smaller initiatives driven by staff or middle management; and much larger projects involving major programs, whole departments or whole-of-government. Readily available data on smaller innovation projects is skimpy but suggests that performance information does not play a major role in stimulating these initiatives. In contrast, two of the examples of large-scale innovation show that performance information plays a critical role at all stages. The paper concludes by supporting the contention of others writing on this topic: that more research is needed on innovation, particularly on its link to performance information. In that context, other conclusions drawn in this paper are tentative but suggest that the quality of performance information is as important for innovation as it is for performance management. However, innovation is likely to require its own particular performance information that may not be generated on a routine basis for purposes of performance management, particularly in the early stages of innovation. And, while the availability of performance information can be an important success factor in innovation, it does not stand alone. The commonality of a number of other factors identified in the literature surveyed for this paper strongly suggests that equal if not greater priority needs to be given to attenuating factors that inhibit innovation and to nurturing incentives.

D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.

Subliminal or not? : An appraisal of semantic processing in the near absence of visual awareness

Stimuli that cannot be perceived (i.e., that are subliminal) can still elicit neural responses in an observer, but can such stimuli influence behavior and higher-order cognition? Empirical evidence for such effects has periodically been accepted and rejected over the last six decades. Today, many psychologists seem to consider such effects well-established and recent studies have extended the power of subliminal processing to new limits. In this thesis, I examine whether this shift in zeitgeist is matched by a shift in evidential strength for the phenomenon. This thesis consists of three empirical studies involving more than 250 participants, a simulation study, and a quantitative review. The conclusion based on these efforts is that several methodological, statistical, and theoretical issues remain in studies of subliminal processing. These issues mean that claimed subliminal effects might be caused by occasional or weak percepts (given the experimenters’ own definitions of perception) and that it is still unclear what evidence there is for the cognitive processing of subliminal stimuli. New data are presented suggesting that even in conditions traditionally claimed as “subliminal”, occasional or weak percepts may in fact influence cognitive processing more strongly than do the physical stimuli, possibly leading to reversed priming effects. I also summarize and provide methodological, statistical, and theoretical recommendations that could benefit future research aspiring to provide solid evidence for subliminal cognitive processing.

Use of sulfuryl fluoride in the management of strongly phosphine-resistant insect pest populations in bulk grain storages in Australia

Sulfuryl fluoride (SF), an effective structural fumigant, is registered recently as Profume™ for controlling insect pests of stored grains and processed commodities. Information on its effectiveness in disinfestation of bulk grain, however, is limited. The ongoing problem with the strong level of resistance to phosphine has been addressed recently through deployment of SF as a ‘resistance breaker’ in bulk storages in Australia. This paper discusses important results on the efficacy of SF against key phosphine- resistant insect pests, lesser grain borer, Rhyzopertha dominca, red flour beetle, Tribolium castaneum, rice weevil, Sitophilus oryzae and the rusty grain beetle, Cryptolestes ferrugineus. We have established CT (g-hm3) profiles for SF against these insect pests at two temperature regimes 25 and 30°C, that showed that both temperature and exposure period (t) has significant influence on the effectiveness of SF than the concentration. Over a seven days fumigation period, CTs of 800 and 400 g-hm3 achieved complete control of all the target pests, including the most strongly phosphine - resistant species, C. ferrugineus at 25 and 30°C, respectively. Results from four industry scale field trials involving currently registered rate of SF (1500 g-hm3) over 2–14 d exposure period, confirmed its effectiveness in achieving complete control of the target pest species. The assessment of postfumigation grain samples across all the test storages indicated that the reinfestation occurs after three months. Monitoring resistance to phosphine in C. ferrugineus over a six year period (2009–2015), showed a significant reduction in resistant populations after the introduction of SF into the fumigation strategy at problematic storage sites. Overall our research concludes that SF is a good candidate to be used as a ‘resistance breaker’ where phosphine resistance is prevalent.

Semantic Mining based on graph theory and ontologies. Case Study: Cell Signaling Pathways

In this paper we use concepts from graph theory and cellular biology represented as ontologies, to carry out semantic mining tasks on signaling pathway networks. Specifically, the paper describes the semantic enrichment of signaling pathway networks. A cell signaling network describes the basic cellular activities and their interactions. The main contribution of this paper is in the signaling pathway research area, it proposes a new technique to analyze and understand how changes in these networks may affect the transmission and flow of information, which produce diseases such as cancer and diabetes. Our approach is based on three concepts from graph theory (modularity, clustering and centrality) frequently used on social networks analysis. Our approach consists into two phases: the first uses the graph theory concepts to determine the cellular groups in the network, which we will call them communities; the second uses ontologies for the semantic enrichment of the cellular communities. The measures used from the graph theory allow us to determine the set of cells that are close (for example, in a disease), and the main cells in each community. We analyze our approach in two cases: TGF-β and the Alzheimer Disease.

Scalable semantic aware context storage

The number of connected devices collecting and distributing real-world information through various systems, is expected to soar in the coming years. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, but none of them have been widely adopted. In previous work we addressed this challenge, however our solution had some drawbacks: poor semantic extraction and scalability. In this paper we discuss ways to efficiently deal with representation schemes' diversity and propose a novel d-dimension organization model. Our evaluation shows that d-dimension model improves scalability and semantic extraction.

On the application of contextual IoT service discovery in Information Centric Networks

The continuous flow of technological developments in communications and electronic industries has led to the growing expansion of the Internet of Things (IoT). By leveraging the capabilities of smart networked devices and integrating them into existing industrial, leisure and communication applications, the IoT is expected to positively impact both economy and society, reducing the gap between the physical and digital worlds. Therefore, several efforts have been dedicated to the development of networking solutions addressing the diversity of challenges associated with such a vision. In this context, the integration of Information Centric Networking (ICN) concepts into the core of IoT is a research area gaining momentum and involving both research and industry actors. The massive amount of heterogeneous devices, as well as the data they produce, is a significant challenge for a wide-scale adoption of the IoT. In this paper we propose a service discovery mechanism, based on Named Data Networking (NDN), that leverages the use of a semantic matching mechanism for achieving a flexible discovery process. The development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring the interoperability among different devices and applications. We assessed the performance of our solution through the implementation and deployment of a proof-of-concept prototype. Obtained results illustrate the potential of integrating semantic and ICN mechanisms to enable a flexible service discovery in IoT scenarios.

Semantic features for context organization

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.

The influence of surface and deep cues on primary and secondary school students’ assessment of relevance in Web menus

International audience

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

A Retrospective Socio-Semantic Analysis of the PRO-VE Conferences

Part 19: Knowledge Management in Networks