Overlapping Multi-hop Clustering for Wireless Sensor Networks

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allows distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size

AROMA: Automatic Generation of Radio Maps for Localization Systems

WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure

Arabic Spelling Correction using Supervised Learning

In this work, we address the problem of spelling correction in the Arabic language utilizing the new corpus provided by QALB (Qatar Arabic Language Bank) project which is an annotated corpus of sentences with errors and their corrections. The corpus contains edit, add before, split, merge, add after, move and other error types. We are concerned with the first four error types as they contribute more than 90% of the spelling errors in the corpus. The proposed system has many models to address each error type on its own and then integrating all the models to provide an efficient and robust system that achieves an overall recall of 0.59, precision of 0.58 and F1 score of 0.58 including all the error types on the development set. Our system participated in the QALB 2014 shared task "Automatic Arabic Error Correction" and achieved an F1 score of 0.6, earning the sixth place out of nine participants.Comment: System description paper that is submitted in the EMNLP 2014 conference shared task "Automatic Arabic Error Correction" (Mohit et al., 2014) in the Arabic NLP workshop. 6 page

Here’s Your Number, Now Please Wait in Line: The Asylum Backlog, Federal Court Litigation, and Artificial Intelligence in Agency Adjudication

Asylum seekers are individuals who flee to other countries to find sanctuary from the persecution suffered within the borders of their home countries. The U.N. High Commissioner for Refugees estimated that by mid-2021 there were nearly 4.4 million individuals actively seeking asylum worldwide, and the most recent data available surprisingly suggest that the United States granted asylum to only 31,429 persons in 2020. The asylum system that is with us today was created when Congress enacted the Refugee Act with the goal of “respond[ing] to the urgent needs of persons subject to persecution in their homelands” and “provid[ing] a permanent and systematic procedure for the admission to this country” for refugees and asylum seekers. Despite what may have been the best of intentions, courts and scholars today recognize that the U.S. asylum process “is in tatters.” Although there are two methods by which an individual can gain asylum in the United States, this Comment principally concerns itself with affirmative asylum—the process by which a foreign national affirmatively applies to the U.S. Citizenship and Immigration Services (USCIS) for asylum. At the beginning of 2022, there were 196,714 affirmative asylum claims pending, and many applicants have waited in a state of legal limbo for over five years to receive a decision on their claim. To escape the indefinite queue, some have started bringing claims of unreasonable delay under the Administrative Procedure Act (APA) to federal courts. Because there are groups of asylum seekers who may be especially harmed by multiyear delays in adjudication, this Comment undertakes two separate but related tasks. First, it assesses whether the avenue for relief available to advocates and asylum seekers—federal court litigation—is actually viable for its purported ends. This Comment concludes that it is not. Second, it proposes a novel agency-side adjudicative mechanism, implemented through artificial intelligence technology, to more adequately provide reliable relief to especially vulnerable asylum seekers. The proposal offers a sketch of the new mechanism, wrestles with how artificial intelligence may be incorporated into it, and finally explores how the transparency and accountability of the agency’s automated decision-making may still be attained through current administrative law doctrines