Throughout the seventy-year history of bilateral relations, which are especially sensitive to events in the Palestinian-Israeli conflict, Turkey and Israel have repeatedly been on the verge of their complete breach. A serious deterioration occurred after the incident with the Turkish «Gaza Freedom Flotilla» in May 2010. The author examines how the Israeli expert community assesses aspects of the relationship between the Israeli government headed by Prime Minister Benjamin Netanyahu and Turkish leader Recep Tayyip Erdogan, as well as his policy on a number of issues on the Israeli and regional agenda.
President Trump’s Middle East Peace Plan is tilted in favour of Israel. The prime motivation behind it is to put a favourable end to the long-standing Israeli-Palestinian conflict. The Peace Plan has drawn great global response with those terming the Peace Plan as unreasonable outnumbering those who claim that the Peace Plan is devised to perfection. Despite the negative public opinion, President Trump still happens to be very confident about the prospects of his Peace Plan. The Peace Plan has very conveniently diverted attention from the domestic politics of both, President Trump and Prime Minister Benjamin Netanyahu and is thereby, suspected to be a part of another possible political strategy. With a multitude of players in action, this paper shall attempt to draw a comprehensive account of all the prospects of Trump’s Middle East Peace Plan.
We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place between the years: 2016–2018. DL has unique capability of streamlining the description, analysis, and monitoring of coral reefs, saving time, and obtaining higher reliability and accuracy compared with error-prone human performance. Coral reefs are the most diverse and complex of marine ecosystems, undergoing a severe decline worldwide resulting from the adverse synergistic influences of global climate change, ocean acidification, and seawater warming, exacerbated by anthropogenic eutrophication and pollution. DL is an extension of some of the concepts originating from machine learning that join several multilayered neural networks. Machine learning refers to algorithms that automatically detect patterns in data. In the case of corals these data are underwater photographic images. Based on “learned” patterns, such programs can recognize new images. The novelty of DL is in the use of state-of-art computerized image analyses technologies, and its fully automated methodology of dealing with large data sets of images. Automated Image recognition refers to technologies that identify and detect objects or attributes in a digital video or image automatically. Image recognition classifies data into selected categories out of many. We show that Neural Network methods are already reliable in distinguishing corals from other benthos and non-coral organisms. Automated recognition of live coral cover is a powerful indicator of reef response to slow and transient changes in the environment. Improving automated recognition of coral species, DL methods already recognize decline of coral diversity due to natural and anthropogenic stressors. Diversity indicators can document the effectiveness of reef bioremediation initiatives. We explored the current applications of deep learning for corals and benthic image classification by discussing the most recent studies conducted by researchers. We review the developments in the field, point out their current limitations, and outline their timelines and unique potential. We also discussed a few future research directions in the fields of deep learning. Future needs are the age detection of single species, in order to track trends in their population recruitment, decline, and recovery. Fine resolution, at the polyp level, is still to be developed, in order to allow separation of species with similar macroscopic features. That refinement of DL will allow such comparisons and their analyses. We conclude that the usefulness of future, more refined automatic identification will allow reef comparison, and tracking long term changes in species diversity. The hitherto unused addition of intraspecific coral color parameters, will add the inclusion of physiological coral responses to environmental conditions and change thereof. The core aim of this review was to underscore the strength and reliability of the DL approach for documenting coral reef features based on an evaluation of the currently available published uses of this method. We expect that this review will encourage researchers from computer vision and marine societies to collaborate on similar long-term joint ventures.
Jews, Muslims and Jerusalem: Disputes and Dialogues examines MuslimJewish relations during significant periods of history in the Middle East, Asia and Africa. A deep concern in the Muslim Arab world concerns the status of the Al-Aqsa Mosque and Dome of the Rock. Israels continued occupation of the West Bank since 1967, and its control of East Jerusalem, has reinforced anti-Jewish (Judeophobia) and anti-Israel movements. The most prominent are the Hamas, the Liberation Party (tahrir), the Islamic Jihad, Hizbullah, the Islamic rulers in Iran, and recently Turkey. Conversely, amongst Jews in Israel and the Diaspora (and amongst many Christians) the last decades have witnessed a rise in extreme Islamophobia in reaction to Arab terrorist attacks, and out of a religious-cultural prejudice against Muslims. Spearheading these trends are members of the Jewish underground, Gush Emunim, Loyalists of the Temple Mount, Holy Temple organizations, and members of the religious Zionist and political movements, the Bayit Yehudi Party and Likud Party. It is noteworthy that there are numerous proactive movements for coexistence and peace amongst Jews and Muslims in Israel and throughout the world, and in that prevailing spirit dozens of ongoing religious and cultural dialogues are maintained. These interactions, and the political and economic engagement at state level, are distinguished by ambivalence given not only the historical record but through contemporary zealotary by hardliners. The US, the UN and the EU have tried to mediate, but to no avail. President Trumps Deal of the Century has abandoned Washingtons neutrality. PM Netanyahu promotes Israeli sovereignty over Jerusalem. This book is the most comprehensive, integrated and updated study on these formidable issues. Given the increasingly volatile language by hardline players the Middle East is at a point of critical historical change: Is it to be a political settlement via dialogue or a downward spiral to a dispute that in an age of offensive weaponry available to all parties can only have dire consequences.
Ethnic group classification is a well-researched problem, which has beenpursued mainly during the past two decades via traditional approaches of imageprocessing and machine learning. In this paper, we propose a method ofclassifying an image face into an ethnic group by applying transfer learningfrom a previously trained classification network for large-scale datarecognition. Our proposed method yields state-of-the-art success rates of99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups:African, Asian, Caucasian, and Indian.
Handwriting-based gender classification is a well-researched problem that hasbeen approached mainly by traditional machine learning techniques. In thispaper, we propose a novel deep learning-based approach for this task.Specifically, we present a convolutional neural network (CNN), which performsautomatic feature extraction from a given handwritten image, followed byclassification of the writer's gender. Also, we introduce a new dataset oflabeled handwritten samples, in Hebrew and English, of 405 participants.Comparing the gender classification accuracy on this dataset against humanexaminers, our results show that the proposed deep learning-based approach issubstantially more accurate than that of humans.
In this paper, we present a deep neural network (DNN) training approachcalled the "DeepMimic" training method. Enormous amounts of data are availablenowadays for training usage. Yet, only a tiny portion of these data is manuallylabeled, whereas almost all of the data are unlabeled. The training approachpresented utilizes, in a most simplified manner, the unlabeled data to thefullest, in order to achieve remarkable (classification) results. Our DeepMimicmethod uses a small portion of labeled data and a large amount of unlabeleddata for the training process, as expected in a real-world scenario. Itconsists of a mentor model and a student model. Employing a mentor modeltrained on a small portion of the labeled data and then feeding it only withunlabeled data, we show how to obtain a (simplified) student model that reachesthe same accuracy and loss as the mentor model, on the same test set, withoutusing any of the original data labels in the training of the student model. Ourexperiments demonstrate that even on challenging classification tasks thestudent network architecture can be simplified significantly with a minorinfluence on the performance, i.e., we need not even know the original networkarchitecture of the mentor. In addition, the time required for training thestudent model to reach the mentor's performance level is shorter, as a resultof a simplified architecture and more available data. The proposed methodhighlights the disadvantages of regular supervised training and demonstratesthe benefits of a less traditional training approach.
Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data. Acquiring high-resolution ground truth (GT) by human interpretation is both cost-ineffective and inconsistent. We propose, instead, to utilize high-resolution, hyperspectral images for solving this problem, by unmixing these images to obtain reliable GT for training a deep network. Specifically, we simulate GT from high-resolution, hyperspectral FENIX images, and use it for training a convolutional neural network (CNN) for pixel-based classification. We show how the model can be transferred successfully to classify new mid-resolution VENμS imagery.