"I Buy American": The American Image as Reflected in Israeli Advertising

Analysis of advertisements in Israeli newspapers over the past decade has shown that American symbols have been widely used to market an array of consumer goods. Products made in America, Israel, or other countries are marketed with some sort of American angle by invoking America's values, symbols, landscapes, or lifestyle. Using both qualitative and quantitative methods, we analyzed how central components of the nation-state/national sphere—language, flag, political leaders, borders, landscapes, and sights—are reflected in advertising. We attempt to show how social values metamorphose, shedding their "Israeliness" and taking on an American veneer. Using Israel as an example may provide a test case for the Americanization process in other societies at the turn of the millennium.     

Recent progress in road and lane detection: a survey

The problem of road or lane perception is a crucial enabler for advanced driver assistance systems. As such, it has been an active field of research for the past two decades with considerable progress made in the past few years. The problem was confronted under various scenarios, with different task definitions, leading to usage of diverse sensing modalities and approaches. In this paper we survey the approaches and the algorithmic techniques devised for the various modalities over the last 5 years. We present a generic break down of the problem into its functional building blocks and elaborate the wide range of proposed methods within this scheme. For each functional block, we describe the possible implementations suggested and analyze their underlying assumptions. While impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps and suggest research directions that may bridge them.     

Fertigation for minimizing environmental pollution by fertilizers

Intensification of agriculture by irrigation and enhanced use of fertilizers may generate pollution by increased levels of nutrients in underground and surface waters. Most of the irrigation is by open systems having a relatively low efficiency of water application. A higher efficiency may be gained by pressurized irrigation systems. Drip irrigation generates a restricted root system requiring frequent nutrient supply that may be satisfied by applying fertilizers in irrigation water, i.e. by fertigation. Maximization of crop yield and quality and minimization of leaching below the rooting volume may be achieved by managing fertilizers concentrations in measured quantities of irrigation water, according to crop requirements.     

Querying business processes with BP-QL

We present in this paper BP-QL, a novel query language for querying business processes. The BP-QL language is based on an intuitive model of business processes, an abstraction of the emerging BPEL (business process execution language) standard. It allows users to query business processes visually, in a manner very analogous to how such processes are typically specified, and can be employed in a distributed setting, where process components may be provided by distinct providers.     

Efficient Learning of Relational Object Class Models

We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts’ appearance, as well as location and scale relations between parts. The object class is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information, and nodes describing object parts. The model’s parameters, however, are optimized to reduce a loss function of the training error, as in discriminative methods. We show how boosting techniques can be extended to optimize the relational model proposed, with complexity linear in the number of parts and the number of features per image. This efficiency allows our method to learn relational models with many parts and features. The method has an advantage over purely generative and purely discriminative approaches for learning from sets of salient features, since generative method often use a small number of parts and features, while discriminative methods tend to ignore geometrical relations between parts. Experimental results are described, using some bench-mark data sets and three sets of newly collected data, showing the relative merits of our method in recognition and localization tasks.     

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

International Journal of Computer Vision - Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become...     

User assisted separation of reflections from a single image using a sparsity prior

When we take a picture through transparent glass the image we obtain is often a linear superposition of two images: the image of the scene beyond the glass plus the image of the scene reflected by the glass. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there are an infinite number of valid decompositions. In this paper we focus on an easier problem: user assisted separation in which the user interactively labels a small number of gradients as belonging to one of the layers. Even given labels on part of the gradients, the problem is still ill-posed and additional prior knowledge is needed. Following recent results on the statistics of natural images we use a sparsity prior over derivative filters. This sparsity prior is optimized using the terative reweighted least squares (IRLS) approach. Our results show that using a prior derived from the statistics of natural images gives a far superior performance compared to a Gaussian prior and it enables good separations from a modest number of labeled gradients.     

Using Multiple Solution Tasks for the Evaluation of Students’ Problem-Solving Performance in Geometry

Abstract

The article demonstrates that multiple solution tasks (MSTs) in the context of geometry can serve as a research instrument for evaluating geometry knowledge and creativity. Geometry knowledge is evaluated based on the correctness and connectedness of solutions, whereas creativity is evaluated based on a combination of fluency, flexibility, and originality of solutions. In this article, the MST research instrument is introduced in connection with the theoretical analysis of the research literature and then explained and analyzed using geometry students’ performance results on one MST. The analysis shows that the research instrument differentiates between students belonging to high- and regular-level instruction groups and sheds light on the interrelations between components of geometry knowledge and creativity.     

Continuous input nonlocal games

We present a family of nonlocal games in which the inputs the players receive are continuous. We study three representative members of the family. For the first two a team sharing quantum correlations (entanglement) has an advantage over any team restricted to classical correlations. We conjecture that this is true for the third member of the family as well.