A Two-Phase Genetic Algorithm for Image Registration

Image Registration (IR) is the process of aligning two (or more) images ofthe same scene taken at different times, different viewpoints and/or bydifferent sensors. It is an important, crucial step in various image analysistasks where multiple data sources are integrated/fused, in order to extracthigh-level information. Registration methods usually assume a relevant transformation model for agiven problem domain. The goal is to search for the "optimal" instance of thetransformation model assumed with respect to a similarity measure in question. In this paper we present a novel genetic algorithm (GA)-based approach forIR. Since GA performs effective search in various optimization problems, itcould prove useful also for IR. Indeed, various GAs have been proposed for IR.However, most of them assume certain constraints, which simplify thetransformation model, restrict the search space or make additionalpreprocessing requirements. In contrast, we present a generalized GA-basedsolution for an almost fully affine transformation model, which achievescompetitive results without such limitations using a two-phase method and amulti-objective optimization (MOO) approach. We present good results for multiple dataset and demonstrate the robustnessof our method in the presence of noisy data.

Netanyahu's Populism: An Overlooked Explanation for Israeli Foreign Policy

When discussing the current "wave of populism" sweeping the globe, there are certain names one expects to hear—Donald Trump, Marine Le Pen, Geert Wilders, or "Brexit." Benjamin Netanyahu is not among them, but perhaps he should be. Since 2009, the Israeli Prime Minister has pushed his nation further to the political right than at any point in Israel's nearly seven decades of existence. He has done this through a set of political tactics that bears a close resemblance to those currently employed by some of the world's most prominent populist leaders. While Netanyahu has not escaped criticism, both at home and abroad, for his conduct as Israeli Prime Minister, he has avoided some of the harsher judgments levied against more notable figures identified with the populist movement, including the label of "populist" itself. Nevertheless, Netanyahu's actions, particularly in the realm of foreign policy, offer some of the best examples of how populist leaders operate once in power and the effects such actions can have in the global political arena. Using Jan-Werner Müller's interpretation of populism as a guide, this paper applies the lessons of populist leadership to Netanyahu's political style as Israeli Prime Minister. In doing so, it illustrates the ways in which populist politics can shape national foreign policy and affect the global order.

Neither Two States nor One: The Palestine Question in the Age of Trump

In the opening weeks of his administration, President Donald Trump overturned a longstanding U.S. commitment to territorial partition and a two-state model for resolving the Israeli-Palestinian conflict. Israeli prime minister Benjamin Netanyahu seized the opportunity to demand “overriding security control over the entire area west of the Jordan River” while exploring regional approaches that bypass the Palestinians. At the same time, a host of Israeli politicians are reviving older models such as limited autonomy without political sovereignty and partial territorial annexation, or advocating for other forms of separation with Israel’s continued control. The resulting middle ground—neither two states nor one—poses a great risk to Palestinian self-determination. By situating recent developments in a broader historical context going back to the autonomy plan of Israeli prime minister Menachem Begin, this essay provides an overview of a shifting political discourse and examines the consequences for the fate of the Palestinians today.

Crowdfunding Civil Justice

Crowdfunding—the aggregation of numerous but modest individual contributions through specialized online platforms—is a relatively new finance method. In the last few years, it has started its incursion into the realm of civil litigation funding. Three unrelated events, which took place in different jurisdictions in 2017, demonstrate this evolving trend and its potential impact. In the United States, the Southern Poverty Law Center included the political activist Maajid Nawaz on a list of “anti-Muslim extremists.” Nawaz launched an independent campaign for crowdfunding a defamation action against the organization. In the United Kingdom, a wildlife protection organization brought a defamation action against Andy Wightman, a Member of the Scottish Parliament, over his blog posts about the plaintiff’s practices. Wightman raised more than £60,000 through a British crowdfunding platform to fight this lawsuit. In Israel, the acclaimed journalist Igal Sarna was found liable in defamation for a Facebook post scorning Israeli Prime Minister Benjamin Netanyahu. Sarna raised over $45,000 through a crowdfunding website to cover his liability. The Article provides a law and economics analysis of this emerging global trend, which may revolutionize the civil process in the near future. It argues, first, that the distinction between investment-based and non-investment-based crowdfunding models is crucial. In non-investment-based models, contributors expect only a non-monetary benefit (reward-based crowdfunding) or none at all (donation-based crowdfunding). In investment-based models, contributors expect financial return—a share in the fundraiser’s future gain (equity-crowdfunding) or repayment of the contribution with interest (debt-crowdfunding). The Article contends that investment-based litigation crowdfunding is generally a welcome phenomenon, because it enables parties to pursue meritorious claims and defenses without generating a significant risk of frivolous litigation. Thus, it should be minimally regulated by securing disclosure of relevant information to potential investors. Non-investment-based litigation crowdfunding should be more constrained. The analysis entails a second fundamental distinction between process costs and outcome costs. Process costs are any outlays incurred by either party in relation to the dispute resolution process and prior to its conclusion. These may include court charges, attorneys’ fees, witnesses’ and experts’ expenditures and remuneration, etc. In cases of incapacitating injury, process costs may also include the claimant’s living expenses throughout the process. Outcome costs are the amounts payable under the settlement or the judgment. The Article contends that non-investment-based crowdfunding of process costs should be subject to professional vetting. This will inhibit frivolous claims and defenses that waste scarce administrative resources and do not further the underlying goals of civil law. Non-investment-based crowdfunding of outcome costs should be prohibited, because it undermines at least one of the primary objectives of substantive law.

A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types

A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types

In this paper we introduce new types of square-piece jigsaw puzzles, where inaddition to the unknown location and orientation of each piece, a piece mightalso need to be flipped. These puzzles, which are associated with a number ofreal world problems, are considerably harder, from a computational standpoint.Specifically, we present a novel generalized genetic algorithm (GA)-basedsolver that can handle puzzle pieces of unknown location and orientation (Type2 puzzles) and (two-sided) puzzle pieces of unknown location, orientation, andface (Type 4 puzzles). To the best of our knowledge, our solver provides a newstate-of-the-art, solving previously attempted puzzles faster and far moreaccurately, handling puzzle sizes that have never been attempted before, andassembling the newly introduced two-sided puzzles automatically andeffectively. This paper also presents, among other results, the most extensiveset of experimental results, compiled as of yet, on Type 2 puzzles.

A practical approximation algorithm for the LTS estimator

The linear least trimmed squares (LTS) estimator is a statistical technique for fitting a linear model to a set of points. It was proposed by Rousseeuw as a robust alternative to the classical least squares estimator. Given a set of n points in R d , the objective is to minimize the sum of the smallest 50% squared residuals (or more generally any given fraction). There exist practical heuristics for computing the linear LTS estimator, but they provide no guarantees on the accuracy of the final result. Two results are presented. First, a measure of the numerical condition of a set of points is introduced. Based on this measure, a probabilistic analysis of the accuracy of the best LTS fit resulting from a set of random elemental fits is presented. This analysis shows that as the condition of the point set improves, the accuracy of the resulting fit also increases. Second, a new approximation algorithm for LTS, called Adaptive-LTS, is described. Given bounds on the minimum and maximum slope coefficients, this algorithm returns an approximation to the optimal LTS fit whose slope coefficients lie within the given bounds. Empirical evidence of this algorithm's efficiency and effectiveness is provided for a variety of data sets.

Civil Religion in Israel: from Begin to Netanyahu

JRC-Names: Multilingual entity name variants and titles as Linked Data

Since 2004 the European Commission's Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities ( persons and organisations) and their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/ Binyamin/Bibi/Benyamin/Biniamin/Netanyahu/Netanjahu/Neanyahou/Netahny/ ). This entity name variant data, known as JRCNames, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union's Open Data Portal.

DeepSign: Deep learning for automatic malware signature generation and classification

This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6% classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.