A general class of simple majority decision rules based on linguistic opinions

In this paper we have introduced a class of decision rules related to simple majority, by considering individual intensities of preference. These intensities will be shown by means of linguistic labels. In order to compare the amount of opinion obtained by each alternative, we have considered the total ordered monoid generated by the sums of the original labels, according to an addition and an ordering. In this general framework different sets of linguistic labels can be employed and these sets can be represented by means of diverse mathematical objects. Moreover, on these mathematical representations of linguistic labels several orderings can be considered. Thus, flexibility is an important feature of this new class of group decision making procedures. Some examples of putting in practice the simple majority decision rules based on linguistic labels are provided, and the main properties of these voting systems are analyzed. It is worth emphasizing that these properties are satisfied for any total ordered monoid, regardless of the mathematical representation of linguistic labels or the ordering used to compare collective opinions.     

A Novel Framework for Human Activity Recognition with Time Labelled Real Time Sensor Data

Human activity recognition is an effective approach for identifying the characteristics of historical data. In the past decades, different shallow classifiers and handcrafted features were used to identify the activities from the sensor data. These approaches are configured for offline processing and are not suitable for sequential data. This article proposes an adaptive framework for human activity recognition using a deep learning mechanism. This deep learning approach forms the deep belief network (DBN), which contains a visible layer and hidden layers. The processing of raw sensor data is performed by these layers and the activity is identified at the top most layers. The DBN is tested using the real time environment with the help of mobile devices that contain an accelerometer, a magnetometer, and a gyroscope. The results are analyzed with the metrics of precision, recall, and the F1-score. The results proved that the proposed method has a higher F1_score when compared to the existing approach.     

Relations among categorization, induction, recognition, and similarity: Comment on Sloutsky and Fisher (2004).

V. M. Sloutsky and A. V. Fisher (see record 2004-14313-002) reported 5 experiments documenting relations among categorization, induction, recognition, and similarity in children as well as adults and proposed a new model of induction, SINC (similarity, induction, categorization). Those authors concluded that induction depends on perceptual similarity rather than conceptual knowledge. Despite the useful contributions of this work, there are some important limitations. The experimental designs examined a limited range of phenomena that are not the most revealing about the use of nonperceptual information. The main results involved a simple triad task, for which the SINC model's predictions are equivalent to the predictions of previous models of inductive reasoning. It is also unclear whether the SINC model can account for the observed relations between similarity and recognition. Implications for future work on induction and related cognitive activities are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Retailers' tagging practices: a potential liability?

This study investigates the coverage of federally mandated information on over‐the‐counter (OTC) drug labels by electronic article surveillance (EAS) tags applied to the exterior of cartons. Using adult‐strength analgesics containing acetaminophen as a case study, researchers investigated the issue in Houston, Texas (24 stores) and Lansing, Michigan (33 stores). The information obscured by EAS tags was identified and classified for a total of 849 packages using a standardized data collection instrument. The results indicated that 293 packages examined, or 34.5%, had information mandated by the US Food and Drug Administration (US FDA) fully or partially obscured by the EAS tags. Retailers and manufacturers should be aware of such practices to reduce potential liability. Recommendations for improving EAS tag usage on OTC products are presented. Copyright © 2004 John Wiley & Sons, Ltd.     

气敏类智能包装标签技术的研究进展

目的研究气体监测类智能包装标签技术的原理、特点及研究进展。方法综述气敏类智能包装中泄漏指示标签、新鲜度指示标签、射频识别标签的研究现状及应用。重点阐述面向CO_2气体监测和识别的智能包装的分类及其制作方法,并对CO_2传感器在气敏类智能包装中的应用进行阐述。结论近年来智能包装标签技术方面取得了大量研究成果,推动了食品保鲜包装行业的快速发展。气敏类智能包装能够有效检测和监测被包装食品的质量及安全性,对推进包装技术的信息化与智能化起到了重要作用。CO_2传感器在食品新鲜度智能监测方面具有广泛应用,也是未来食品保鲜包装技术领域的发展方向。     

Contribution of green labels in electricity retail markets to fostering renewable energy

In European countries, retailers are obliged to disclose the energy source and the related environmental impacts of their portfolio over the preceding year. The electricity supplied in the Dutch retail market is presented as renewable energy for 34%, but this relatively high share is for 69% based on certificates (Guarantees of Origin) which are imported from in particular Norway. The certificates are used to sell green electricity to consumers. The premium for green electricity which is actually paid by Dutch consumers is no more than a few percentages of the retail price. The low level of this premium is related to the abundant supply of certificates at low marginal costs from Norway. This also means that the premium for green electricity is too low to give an incentive for investments in new capacity. Hence, the current labelling system for renewable electricity is mainly valuable, besides being an instrument for tracking and tracing of renewable energy, as a marketing instrument for electricity retailers. The effectiveness of Guarantees of Origin as a policy instrument to foster renewable electricity sources is weak. This effectiveness can be raised by implementing restrictions on the international trade or the issuance of new certificates.     

The wine headache: Consumer perceptions of sulfites and willingness to pay for non-sulfited wines

A panel of 223 alcohol consumers recruited in a liquor store participated in a survey/best–worst experiment investigating perceptions on sulfites and willingness to pay for non-sulfited wines. We find that 34% of our sample experiences headaches after consuming moderate amounts of wine, and sulfites are the most frequently attributed cause. Based on a rank ordered logit estimation of best–worst choices, headache syndrome sufferers are willing to pay a ceteris paribus premium of $1.23 per bottle to avoid added sulfites. However, results from a (logit) model of purchase intentions suggest that quality and price are most important, with differentiating labels (no sulfite added, organic) playing only a marginal role. Marketing implications for the wine industry are offered, and negative perceptions toward sulfites are contextualized within the hypothesis of a “lightning rod” effect induced by the “contains sulfites” warning label.     

Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.