Abstract This paper introduces SensoGraph, a novel approach for fast sensory evaluation using two-dimensional geometric techniques. In the tasting sessions, the assessors follow their own criteria to place samples on a tablecloth, according to the similarity between samples. In order to analyse the data collected, first a geometric clustering is performed to each tablecloth, extracting connections between the samples. Then, these connections are used to construct a global similarity matrix. Finally, a graph drawing algorithm is used to obtain a 2D consensus graphic, which reflects the global opinion of the panel by (1) positioning closer those samples that have been globally perceived as similar and (2) showing the strength of the connections between samples. The proposal is validated by performing four tasting sessions, with three types of panels tasting different wines, and by developing a new software to implement the proposed techniques. The results obtained show that the graphics provide similar positionings of the samples as the consensus maps obtained by multiple factor analysis (MFA), further providing extra information about connections between samples, not present in any previous method. The main conclusion is that the use of geometric techniques provides information complementary to MFA, and of a different type. Finally, the method proposed is computationally able to manage a significantly larger number of assessors than MFA, which can be useful for the comparison of pictures by a huge number of consumers, via the Internet.
Projective Mapping has been successfully used with many different kinds of products, among which the application to wine stands out . Other examples of beverages analysed by these methods are beers , citrus juices , drinking waters high alcohol products , hot beverages , lemon iced teas , powdered juices , or smoothies . The book by @cite_6 details more products to which consumer based descriptive methodologies have been applied.
@cite_6
2506076252
Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it -- once they are ready. The same is true for beers, wines, gourmet foods -- or any products where users have acquired tastes: the best' products may not be the most accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products -- in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews. "Sensory characterization is one of the most powerful, sophisticated and extensively applied tools in sensory science. This book focuses on sensory characterization of food and non-food products, providing an overview of classical and novel alternative methodologies. A complete description of the methodologies would be provided. Each description would be accompanied by detailed information for their implementation, discussion of examples of applications and case-studies. The implementation of the majority of the methodologies would be performed in the statistical free software R, which would make the book very useful for people non-familiar with complex statistical software"-- There has been a recent surge of research looking at the reporting of food consumption on social media. The topic of alcohol consumption, however, remains poorly investigated. Social media has the potential to shed light on a topic that, traditionally, is difficult to collect fine-grained information on. One social app stands out in this regard: Untappd is an app that allows users to ‘check-in’ their consumption of beers. It operates in a similar fashion to other location-based applications, but is specifically tailored to the collection of information on beer consumption. In this paper, we explore beer consumption through the lens of social media. We crawled Untappd in real time over a period of 112 days, across 40 cities in the United States and Europe. Using this data, we shed light on the drinking habits of over 369k users. We focus on per-user and per-city characterisation, highlighting key behavioural trends. Abstract This paper deals with the method of projective mapping and its use in sensory analysis. Projective mapping is a method which allows naive consumers to map products on a two dimension space, based on similarities and differences in the products. An experiment was carried out on seven blueberry soups, whereby sensory profiling was undertaken using a trained panel and projective mapping. Preference assessment was undertaken using a small group of consumers. Results indicated that the three replicates of the mapping exercise produced visually very similar maps, at least on the first two dimensions. However, it was found that consumers perceived the samples in somewhat different ways as high-lighted by RV coefficients. The consensus mapping dimensions were compared to those from the profile data, and it was apparent that the best similarity was found when comparing the first dimension, thus suggesting good agreement on the obvious aspects of the product. The internal preference map also revealed major product contrasts along this dimension with some weak evidence of segmentation. The overall conclusion of this paper raises further fundamental questions about consensus spaces with consumers, and the question of dimensionality of consumer perception as compared to trained panels. Abstract The present study examines the link between alcohol consumption and personality, using the California Psychological Inventory in a large well-adjusted sample and tests four hypotheses, including one that states that persons who drink no alcohol at all are less well adjusted than persons who drink moderate amounts. The results confirm the hypotheses. Alcohol consumed correlates positively with sociability and extraversion, but negatively with conscientiousness and willingness to conform. The results also show, in a large sample and using a comprehensive personality inventory, that persons who drink no alcohol at all are a little more withdrawn, a little less ambitious, a little less generally well-organised and competent, than people who drink in moderation. SUMMARY This paper offers an approach for handling retention time shifts in resolving chromatographic data using the PARAFAC2 model. In Part I of this series an algorithm for PARAFAC2 was developed and extended toN-way arrays. It was discussed that the PARAFAC2 model has a number of attractive features. It is unique under mild conditions though it puts fewer restrictions on the data than the well-known PARAFAC1 model. This has important implications for the modeling of chromatographic data in which retention time shifts can be regarded as a violation of the assumption of parallel proportional profiles underlying the PARAFAC1 model. The PARAFAC2 model does not assume parallel proportional elution profiles, but only that the matrix of elution profiles preserve its ‘inner-product structure’ from sample to sample. This means that the cross-products of the matrix holding the elution profiles in its columns remain constant. Here an application using chromatographic separation based on the molecular size of thick juice samples from the beet sugar industry illustrates the benefit of using the PARAFAC2 model. Copyright © 1999 John Wiley & Sons, Ltd. MSRLM is the release of our internal language modeling tool chain used in Microsoft Research. It was used in our submission for NIST MT 2006. The main difference with other freely available tools is that it was designed to scale to large amounts of data. We successfully built a language model on high end hardware on 40 billion words of web data within 8 hours. It only supports a minimal set of features. Large gigaword language models may be consumed in a first pass machine translation decoding without further processing. This document describes the implementation and usage of the tools summarily. It is our stated goal and hope that this release will be useful to the scientific community. The tool may not be used in a commercial product, or to build models used in a commercial product, or in for any commercial purpose. In addition, we require that you kindly cite this technical report when publishing results derived with this language model tool chain. This describes the LM tool which is available as: http: research.microsoft.com research downloads details 78e26f9c-fc9a-44bb-80a769324c62df8c details.aspx In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5 accuracy using the surveys, our results showed over 75 accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on off patterns. Abstract The Amundsen Sea is one of the most productive polynyas in the Antarctic per unit area and is undergoing rapid changes including a reduction in sea ice duration, thinning ice sheets, retreat of glaciers and the potential collapse of the Thwaites Glacier in Pine Island Bay. A growing body of research has indicated that these changes are altering the water mass properties and associated biogeochemistry within the polynya. Unfortunately difficulties in accessing the remote location have greatly limited the amount of in situ data that has been collected. In this study data from a Teledyne-Webb Slocum glider was used to supplement ship-based sampling along the Dotson Ice Shelf (DIS). This autonomous underwater vehicle revealed a detailed view of a meltwater laden outflow from below the western flank of the DIS. Circumpolar Deep Water intruding onto the shelf drives glacial melt and the supply of macronutrients that, along with ample light, supports the large phytoplankton blooms in the Amundsen Sea Polynya. Less well understood is the source of micronutrients, such as iron, necessary to support this bloom to the central polynya where chlorophyll concentrations are highest. This outflow region showed decreasing optical backscatter with proximity to the bed indicating that particulate matter was sourced from the overlying glacier rather than resuspended sediment. This result suggests that particulate iron, and potentially phytoplankton primary productivity, is intrinsically linked to the magnitude and duration of sub-glacial melt from Circumpolar Deep Water intrusions onto the shelf. Abstract Studies have indicated that profiling and (dis)similarity scaling yield different perceptual product maps. Conceptually, these two procedures are different. This paper looks at a third and alternative method of producing a two-dimensional, perceptual map utilizing a projective-type method whereby individual assessors themselves are required to place products on the space according to the similarities and differences they perceive. However, visual comparison of the final results provided by each assessor is difficult and, hence, generalized Procrustes analysis is applied to compare each assessor's map for similarity with the others. In this study it was found that the perceptual map derived from projective mapping was as similar to the map derived from profiling as from dissimilarity scaling. However, consistency over repeated trials was greater for projective mapping than for the other two methods. It is suggested that projective mapping could be a potentially useful technique for linking sensory analysis and consumer research data. We consider the problem of entity tagging: given one or more named entities from a specific domain, the goal is to automatically associate descriptive phrases, referred to as etags (entity tags), to each entity. Consider a product catalog containing product names and possibly short descriptions. For a product in the catalog, say Ricoh G600 Digital Camera, we want to associate etags such as "water resistant", "rugged" and "outdoor" to it, even though its name or description does not mention those phrases. Entity tagging can enable more effective search over entities. We propose to leverage signals in web documents to perform such tagging. We develop techniques to perform such tagging in a domain independent manner while ensuring high precision and high recall. Unmanned aerial vehicles (UAVs) have the potential to significantly impact early detection and monitoring of plant diseases. In this paper, we present preliminary work in developing a UAV-mounted sensor suite for detection of citrus greening disease, a major threat to Florida citrus production. We propose a depth-invariant sensing methodology for measuring reflectance of polarized amber light, a metric which has been found to measure starch accumulation in greening-infected leaves. We describe the implications of adding depth information to this method, including the use of machine learning models to discriminate between healthy and infected leaves with validation accuracies up to 93 . Additionally, we discuss stipulations and challenges of use of the system with UAV platforms. This sensing system has the potential to allow for rapid scanning of groves to determine the spread of the disease, especially in areas where infection is still in early stages, including citrus farms in California. Although presented in the context of citrus greening disease, the methods can be applied to a variety of plant pathology studies, enabling timely monitoring of plant health—impacting scientists, growers, and policymakers. Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology. In recent years, PDF has become the de-facto standard for the exchange of print-oriented documents on the Web. This includes many business documents such as financial reports, newsletters and patent applications, and there are many commercial applications that require data to be extracted from these documents and processed by computer systems. A number of products currently exist on the market that navigate, extract and transform data from HTML pages; a process known as wrapping. One such methodology is Lixto, a product of research at our institute. However, none of these products are currently able to work with PDF files. We are investigating this possibility as part of the NEX-TWRAP project. This paper describes our work in progress, and details some of the low-level page segmentation techniques that we have investigated Previous studies on informational cascades have stressed the importance of informational social influences in decision-making. When people use the product evaluations of others to indicate product quality on the Internet, online herd behavior occurs. This work presents four studies examining herd behavior of online book purchasing. The first two studies addressed how two cues frequently found on the Internet, i.e., star ratings and sales volume, influence consumer online product choices. The last two studies investigated the relative effectiveness of different recommendation sources. The experimental results demonstrated that subjects use the product evaluations and choices of others as cues in making purchasing book decisions on the Internet bookstore. Additionally, recommendations of other consumers exerted a greater influence on subject choices than recommendations of an expert. Finally, recommendations from recommender system influenced online consumer choices more than those from website owners. The results and implications of this research are discussed. Preface 1. An introduction to rippling 2. Varieties of rippling 3. Productive use of failure 4. A formal account of rippling 5. The scope and limitations of rippling 6. From rippling to a general methodology 7. Conclusions Appendix 1. An annotated calculus and a unification algorithm Appendix 2. Definitions of functions used in this book Bibliography Index. Authors Jim Jeffers and James Reinders spent two years helping educate customers about the prototype and pre-production hardware before Intel introduced the first Intel Xeon Phi coprocessor. They have distilled their own experiences coupled with insights from many expert customers, Intel Field Engineers, Application Engineers and Technical Consulting Engineers, to create this authoritative first book on the essentials of programming for this new architecture and these new products. This book is useful even before you ever touch a system with an Intel Xeon Phi coprocessor. To ensure that your applications run at maximum efficiency, the authors emphasize key techniques for programming any modern parallel computing system whether based on Intel Xeon processors, Intel Xeon Phi coprocessors, or other high performance microprocessors. Applying these techniques will generally increase your program performance on any system, and better prepare you for Intel Xeon Phi coprocessors and the Intel MIC architecture. A practical guide to the essentials of the Intel Xeon Phi coprocessor Presents best practices for portable, high-performance computing and a familiar and proven threaded, scalar-vector programming model Includes simple but informative code examples that explain the unique aspects of this new highly parallel and high performance computational product Covers wide vectors, many cores, many threads and high bandwidth cache memory architecture Table of Contents 1.?Introduction 2.?High Performance examples 3.?Benchmarking Apps 4.?Real-world Situations 5.?Lots of Data (Vectors) 6.?Lots of Tasks (not Threads) 7.?Processing Parallelism 8.?Coprocessor Architecture 9.?Coprocessor System Software 10.?Linux on the Coprocessor 11.?Math Library 12.?MPI 13.?Profiling 14.?Summary Non-photorealistic rendering (NPR) is a combination of computer graphics and computer vision that produces renderings in various artistic, expressive or stylized ways such as painting and drawing. This book focuses on image and video based NPR, where the input is a 2D photograph or a video rather than a 3D model. 2D NPR techniques have application in areas as diverse as consumer and professional digital photography and visual effects for TV and film production. The book covers the full range of the state of the art of NPR with every chapter authored by internationally renowned experts in the field, covering both classical and contemporary techniques. It will enable both graduate students in computer graphics, computer vision or image processing and professional developers alike to quickly become familiar with contemporary techniques, enabling them to apply 2D NPR algorithms in their own projects. In this paper, we dedicate to the topic of aspect ranking, which aims to automatically identify important product aspects from online consumer reviews. The important aspects are identified according to two observations: (a) the important aspects of a product are usually commented by a large number of consumers; and (b) consumers' opinions on the important aspects greatly influence their overall opinions on the product. In particular, given consumer reviews of a product, we first identify the product aspects by a shallow dependency parser and determine consumers' opinions on these aspects via a sentiment classifier. We then develop an aspect ranking algorithm to identify the important aspects by simultaneously considering the aspect frequency and the influence of consumers' opinions given to each aspect on their overall opinions. The experimental results on 11 popular products in four domains demonstrate the effectiveness of our approach. We further apply the aspect ranking results to the application of document-level sentiment classification, and improve the performance significantly. This book is the first of its kind and is produced as a result of the efforts by a consortium of database companies called the Object Database Management Group (ODMG). With this book, standards are defined for object management systems and this will be the foundational book for object-oriented database product.