Betrayal, Distrust, and Rationality: Smart Counter-Collusion Contracts for Verifiable Cloud Computing

Cloud computing has become an irreversible trend. Together comes the pressing need for verifiability, to assure the client the correctness of computation outsourced to the cloud. Existing verifiable computation techniques all have a high overhead, thus if being deployed in the clouds, would render cloud computing more expensive than the on-premises counterpart. To achieve verifiability at a reasonable cost, we leverage game theory and propose a smart contract based solution. In a nutshell, a client lets two clouds compute the same task, and uses smart contracts to stimulate tension, betrayal and distrust between the clouds, so that rational clouds will not collude and cheat. In the absence of collusion, verification of correctness can be done easily by crosschecking the results from the two clouds. We provide a formal analysis of the games induced by the contracts, and prove that the contracts will be effective under certain reasonable assumptions. By resorting to game theory and smart contracts, we are able to avoid heavy cryptographic protocols. The client only needs to pay two clouds to compute in the clear, and a small transaction fee to use the smart contracts. We also conducted a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network.Comment: Published in ACM CCS 2017, this is the full version with all appendice

Global Diversification and IPO Returns

A large number of newly listed firms have significant involvement in international business activity. In this paper, we examine the effect of international business activity on the pricing of initial public offerings (IPOs), post-IPO performance, and survival. In a large sample of U.S. IPOs over 1981–2012, we find that firms with exports and/or foreign sales prior to going public have significantly lower underpricing than firms without international business activity. Furthermore, firms with international business activity significantly outperform purely domestic IPO firms over 3- and 5-year periods after going public and have a significantly higher survival rate. Overall, we provide strong evidence that global diversification has an economically significant effect on the valuation and subsequent performance of firms going public

γ\boldsymbol{\gamma}-Net: Superresolving SAR Tomographic Inversion via Deep Learning

Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolving TomoSAR inversion approach, $\boldsymbol{\gamma}$-Net, to tackle this challenge. $\boldsymbol{\gamma}$-Net adopts advanced complex-valued learned iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative optimization step in sparse reconstruction. Simulations show the height estimate from a well-trained $\boldsymbol{\gamma}$-Net approaches the Cram\'er-Rao lower bound while improving the computational efficiency by 1 to 2 orders of magnitude comparing to the first-order CS-based methods. It also shows no degradation in the super-resolution power comparing to the state-of-the-art second-order TomoSAR solvers, which are much more computationally expensive than the first-order methods. Specifically, $\boldsymbol{\gamma}$-Net reaches more than $90\%$ detection rate in moderate super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at limited baselines demonstrates that the proposed algorithm outperforms the second-order CS-based method by a fair margin. Test on real TerraSAR-X data with just 6 interferograms also shows high-quality 3-D reconstruction with high-density detected double scatterers

HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion

Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomographic inversion (TomoSAR). However, the currently available deep learning-based TomoSAR algorithms are limited to three-dimensional (3D) reconstruction. The extension of deep learning-based algorithms to four-dimensional (4D) imaging, i.e., differential TomoSAR (D-TomoSAR) applications, is impeded mainly due to the high-dimensional weight matrices required by the network designed for D-TomoSAR inversion, which typically contain millions of freely trainable parameters. Learning such huge number of weights requires an enormous number of training samples, resulting in a large memory burden and excessive time consumption. To tackle this issue, we propose an efficient and accurate algorithm called HyperLISTA-ABT. The weights in HyperLISTA-ABT are determined in an analytical way according to a minimum coherence criterion, trimming the model down to an ultra-light one with only three hyperparameters. Additionally, HyperLISTA-ABT improves the global thresholding by utilizing an adaptive blockwise thresholding scheme, which applies block-coordinate techniques and conducts thresholding in local blocks, so that weak expressions and local features can be retained in the shrinkage step layer by layer. Simulations were performed and demonstrated the effectiveness of our approach, showing that HyperLISTA-ABT achieves superior computational efficiency and with no significant performance degradation compared to state-of-the-art methods. Real data experiments showed that a high-quality 4D point cloud could be reconstructed over a large area by the proposed HyperLISTA-ABT with affordable computational resources and in a fast time

EarthNets: Empowering AI in Earth Observation

Earth observation (EO), aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment. With a growing number of satellites in orbit, an increasing number of datasets with diverse sensors and research domains are being published to facilitate the research of the remote sensing community. This paper presents a comprehensive review of more than 500 publicly published datasets, including research domains like agriculture, land use and land cover, disaster monitoring, scene understanding, vision-language models, foundation models, climate change, and weather forecasting. We systematically analyze these EO datasets from four aspects: volume, resolution distributions, research domains, and the correlation between datasets. Based on the dataset attributes, we propose to measure, rank, and select datasets to build a new benchmark for model evaluation. Furthermore, a new platform for EO, termed EarthNets, is released to achieve a fair and consistent evaluation of deep learning methods on remote sensing data. EarthNets supports standard dataset libraries and cutting-edge deep learning models to bridge the gap between the remote sensing and machine learning communities. Based on this platform, extensive deep-learning methods are evaluated on the new benchmark. The insightful results are beneficial to future research. The platform and dataset collections are publicly available at https://earthnets.github.io.Comment: 30 page

Randomness invalidates criminal smart contracts

A smart contract enforces specific performance on anonymous users without centralization. It facilitates payment equity in commerce by providing irreversible transactions. Smart contracts are also used for illegal activities such as money laundering and ransomware. Such contracts include criminal smart contracts (CSCs), proposed in CCS’16, that can be efficiently implemented in existing scripting languages. This aggravates concerns about the dangers of CSCs. However, PublicLeaks, a CSC for leaking private data, is conditionally implemented as it is influenced by various factors. For example, PublicLeaks does not necessarily reach a desirable terminal state for a criminal leaking private information, and other possible terminal states may invalidate the CSC. In this study, we propose a CSC based on PublicLeaks by formulating random factors such as the donation ratio. Our contract forks into five terminal states, including a unique one in PublicLeaks due to randomness. We simulated the maximal probabilities of these terminal states and found that the desirable terminal state in PublicLeaks is reachable with low probabilities (lower than 25%). The terminal state where the criminal fails to leak private information is attained with relatively high probabilities (over 65%). Therefore, our simulations show that CSCs are not always as powerful as expected, and the risk posed by them can be mitigated

Diversifying the Brooklyn Park Police Department

Report completed by students enrolled in PSY 5701: Staffing and Personnel Selection, taught by Deniz Ones in spring 2017.This project was completed as part of the 2016-2017 Resilient Communities Project (rcp.umn.edu) partnership with the City of Brooklyn Park. The Brooklyn Park Police Department does not reflect the diversity of the community, with few officers of color, particularly African American officers. Drawing on a review of literature, an assessment of current recruitment efforts and departmental values in the Brooklyn Park Police Department, and an analysis of characteristics that define successful police candidates, students in Dr. Deniz Ones’ Staffing and Personnel Selection class identified strategies to increase officer diversity within the department. The students’ final report is available.This project was supported by the Resilient Communities Project (RCP), a program at the University of Minnesota whose mission is to connect communities in Minnesota with U of MN faculty and students to advance local sustainability and resilience through collaborative, course-based projects. RCP is a program of the Center for Urban and Regional Affairs (CURA). More information at http://www.rcp.umn.edu

Scan2Drawing: Use of Deep Learning for As-Built Model Landscape Architecture

This paper presents an innovative and fully automatic solution of generating as-built computer-aided design (CAD) drawings for landscape architecture (LA) with three dimensional (3D) reality data scanned via drone, camera, and LiDAR. To start with the full pipeline, 2D feature images of ortho-image and elevation-map are converted from the reality data. A deep learning-based light convolutional encoder–decoder was developed, and compared with U-Net (a binary segmentation model), for image pixelwise segmentation to realize automatic site surface classification, object detection, and ground control point identification. Then, the proposed elevation clustering and segmentation algorithms can automatically extract contours for each instance from each surface or object category. Experimental results showed that the developed light model achieved comparable results with U-Net in landing pad segmentation with average intersection over union (IoU) of 0.900 versus 0.969. With the proposed data augmentation strategy, the light model had a testing pixel accuracy of 0.9764 and mean IoU of 0.8922 in the six-class segmentation testing task. Additionally, for surfaces with continuous elevation changes (i.e., ground), the developed algorithm created contours only have an average elevation difference of 1.68 cm compared to dense point clouds using drones and image-based reality data. For objects with discrete elevation changes (i.e., stair treads), the generated contours accurately represent objects’ elevations with zero difference using light detection and ranging (LiDAR) data. The contribution of this research is to develop algorithms that automatically transfer the scanned LA scenes to contours with real-world coordinates to create as-built computer-aided design (CAD) drawings, which can further assist building information modeling (BIM) model creation and inspect the scanned LA scenes with augmented reality. The optimized parameters for the developed algorithms are analyzed and recommended for future applications

KARYOLOGICAL STUDIES OF THE HYBRID LARVAE OF HALIOTIS DISVERSICOLOR SUPERTEXTA FEMALE AND HALIOTIS DISCUS DISCUS MALE

To determine the genomic composition of the interspecific hybrid between Haliotis diversicolor supertexta female and H. discus discus male at an early developmental stage, veliger larvae produced from hybrid (SJ-5 and SJ-50) and pure species crosses (SS and JJ) were sampled and analyzed using standard karyological methods and genomic in situ hybridization. In hybrid metaphase spreads, chromosomes from both parents were detected, except one metaphase, which showed the H. diversicolor supertexta haploid karyotype. The genomic composition of the hybrid was also confirmed through preliminary genomic in situ hybridization results. Many more aneuploids and chromosome fragments were found in the hybrids than those in the control pure species crosses, indicating genome instability and chromosome loss in the hybrids. In the hybrid hypodiploid metaphase spreads, two intact sets of H. diversicolor supertexta chromosomes and several H. discus discus chromosomes were detected by pairing. Spontaneous diploidization of the maternal chromosome set was shown to occur in hybrid larvae, as 2.2% heterogeneous triploid and 17.9% hypodiploids with two intact H. diversicolor supertexta chromosome sets for SJ-5. The current findings suggest that uniparental chromosome elimination along with spontaneous diploidization of maternal chromosome sets may be the reason for allogynogenesis production in H. diversicolor supertexta X H. discus discus hybridization