Dense Piecewise Planar RGB-D SLAM for Indoor Environments

The paper exploits weak Manhattan constraints to parse the structure of indoor environments from RGB-D video sequences in an online setting. We extend the previous approach for single view parsing of indoor scenes to video sequences and formulate the problem of recovering the floor plan of the environment as an optimal labeling problem solved using dynamic programming. The temporal continuity is enforced in a recursive setting, where labeling from previous frames is used as a prior term in the objective function. In addition to recovery of piecewise planar weak Manhattan structure of the extended environment, the orthogonality constraints are also exploited by visual odometry and pose graph optimization. This yields reliable estimates in the presence of large motions and absence of distinctive features to track. We evaluate our method on several challenging indoors sequences demonstrating accurate SLAM and dense mapping of low texture environments. On existing TUM benchmark we achieve competitive results with the alternative approaches which fail in our environments.Comment: International Conference on Intelligent Robots and Systems (IROS) 201

Fully Secure PSI via MPC-in-the-Head

We design several new protocols for private set intersection (PSI) with active security: one for the two party setting, and two protocols for the multi-party setting. In recent years, the state-of-the-art protocols for two party PSI have all been built from OT-extension. This has led to extremely efficient protocols that provide correct output to one party;~seemingly inherent to the approach, however, is that there is no efficient way to relay the result to the other party with a provable correctness guarantee. Furthermore, there is no natural way to extend this line of works to more parties. We consider a new instantiation of an older approach. Using the MPC-in-the-head paradigm of Ishai et al [IPS08], we construct a polynomial with roots that encode the intersection, without revealing the inputs. Our reliance on this paradigm allows us to base our protocol on passively secure Oblivious Linear Evaluation (OLE) (requiring 4 such amortized calls per input element). Unlike state-of-the-art prior work, our protocols provide correct output to all parties. We have implemented our protocols, providing the first benchmarks for PSI that provides correct output to all parties. Additionally, we present a variant of our multi-party protocol that provides output only to a central server

Linear Communication in Malicious Majority MPC

The SPDZ multiparty computation protocol allows $n$ parties to securely compute arithmetic circuits over a finite field, while tolerating up to $n − 1$ active corruptions. A line of work building upon SPDZ have made considerable improvements to the protocol’s performance, typically focusing on concrete efficiency. However, the communication complexity of each of these protocols is $\Omega(n^2 |C|)$. In this paper, we present a protocol that achieves $O(n|C|)$ communication. Our construction is very similar to those in the SPDZ family of protocols, but for one modular sub-routine for computing a verified sum. There are a handful of times in the SPDZ protocols in which the $n$ parties wish to sum $n$ public values. Rather than requiring each party to broadcast their input to all other parties, clearly it is cheaper to use some designated dealer to compute and broadcast the sum. In prior work, it was assumed that the cost of verifying the correctness of these sums is $O(n^2 )$, erasing the benefit of using a dealer. We show how to amortize this cost over the computation of multiple sums, resulting in linear communication complexity whenever the circuit size is $|C| > n$

Secure Poisson Regression

We introduce the first construction for secure two-party computation of Poisson regression, which enables two parties who hold shares of the input samples to learn only the resulting Poisson model while protecting the privacy of the inputs. Our construction relies on new protocols for secure fixed-point exponentiation and correlated matrix multiplications. Our secure exponentiation construction avoids expensive bit decomposition and achieves orders of magnitude improvement in both online and offline costs over state of the art works. As a result, the dominant cost for our secure Poisson regression are matrix multiplications with one fixed matrix. We introduce a new technique, called correlated Beaver triples, which enables many such multiplications at the cost of roughly one matrix multiplication. This further brings down the cost of secure Poisson regression. We implement our constructions and show their extreme efficiency. In a LAN setting, our secure exponentiation for 20-bit fractional precision takes less than 0.07ms with a batch-size of 100,000. One iteration of secure Poisson regression on a dataset with 10,000 samples with 1000 binary features needs about 65.82s in the offline phase, 55.14s in the online phase and 17MB total communication. For several real datasets this translates into training that takes seconds and only a couple of MB communication

More Efficient (Reusable) Private Set Union

We study the problem of private set union in the two-party setting, providing several new constructions. We consider the case where one party is designated to receive output. In the semi-honest setting, we provide two protocols. Our four-round protocol out-performs the state-ofthe-art in both communication and computation, and has a runtime that is 1.3X-2X faster than existing protocols. Our two-round protocol is only slightly more expensive, but it is still faster than existing protocols and has the property that the receiver message can be re-used across multiple executions. All our semi-honest protocols are post-quantum secure. In the setting where the sender is malicious, we provide the first protocols that avoid the use of expensive zero-knowledge proofs. We estimate (conservatively) that our constructions are less than 2X more expensive than the best known semi-honest constructions. As in the semi-honest setting, we describe two protocols: a faster one that requires four rounds of communication, and a slightly more expensive protocol that allows the receiver message to be re-used. Our work draws on several techniques from the literature on private set intersection, and helps clarify how these techniques generalize (and don’t generalize) to the problem of PSU

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the pills' names in the prescription. We then propose PIMA, a novel approach using Graph Neural Network (GNN) and contrastive learning to address the targeted problem. In particular, GNN is used to learn the spatial correlation between the text boxes in the prescription and thereby highlight the text boxes carrying the pill names. In addition, contrastive learning is employed to facilitate the modeling of cross-modal similarity between textual representations of pill names and visual representations of pill images. We conducted extensive experiments and demonstrated that PIMA outperforms baseline models on a real-world dataset of pill and prescription images that we constructed. Specifically, PIMA improves the accuracy from 19.09% to 46.95% compared to other baselines. We believe our work can open up new opportunities to build new clinical applications and improve medication safety and patient care.Comment: Accepted for publication and presentation at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022

gOTzilla: Efficient Disjunctive Zero-Knowledge Proofs from MPC in the Head, with Application to Proofs of Assets in Cryptocurrencies

We present gOTzilla, a protocol for interactive zero-knowledge proofs for very large disjunctive statements of the following format: given publicly known circuit $C$, and set of values $Y = \{y_1, \ldots, y_n\}$, prove knowledge of a witness $x$ such that $C(x) = y_1 \lor C(x) = y_2 \lor \cdots \lor C(x) = y_n$. These type of statements are extremely important for the proof of assets (PoA) problem in cryptocurrencies where a prover wants to prove the knowledge of a secret key $sk$ that associates with the hash of a public key $H(pk)$ posted on the ledger. We note that the size of $n$ in popular cryptocurrencies, such as Bitcoin, is estimated to 80 million. For the construction of gOTzilla, we start by observing that if we restructure the proof statement to an equivalent of proving knowledge of $(x,y)$ such that $(C(x) = y) \land (y = y_1 \lor \cdots \lor y = y_n))$, then we can reduce the disjunction of equalities to 1-out-of-N oblivious transfer (OT). Our overall protocol is based on the MPC in the head (MPCitH) paradigm. We additionally provide a concrete, efficient extension of our protocol for the case where $C$ combines algebraic and non-algebraic statements (which is the case in the PoA application). We achieve an asymptotic communication cost of $O(\log n)$ plus the proof size of the underlying MPCitH protocol. While related work has similar asymptotic complexity, our approach results in concrete performance improvements. We implement our protocol and provide benchmarks. Concretely, for a set of size 1 million entries, the total run-time of our protocol is 14.89 seconds using 48 threads, with 6.18 MB total communication, which is about 4x faster compared to the state of the art when considering a disjunctive statement with algebraic and non-algebraic elements

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.Comment: Technical report for accepted paper at WSDM 202

An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane

The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.Comment: Accepted for presentation at the 51st International Conference on Parallel Processin