OpenRaiser/Intern-Atlas huggingface dataset:
Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots
Venue: arXiv cs.RO 2021
Year: 2021

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices -- making it especially suited for resource-constrained multi-robot applications. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. In this letter, we address these issues by proposing a robust UWB TDOA localization framework comprising of (i) learning-based bias correction and (ii) M-estimation-based robust filtering to handle outliers. The key properties of our approach are that (i) the learned biases generalize to different UWB anchor setups and (ii) the approach is computationally efficient enough to run on resource-constrained hardware. We demonstrate our approach on a Crazyflie nano-quadcopter. Experimental results show that the proposed localization framework, relying only on the onboard IMU and UWB, provides an average of 42.08 percent localization error reduction (in three different anchor setups) compared to the baseline approach without bias compensation. {We also show autonomous trajectory tracking on a quadcopter using our UWB TDOA localization approach.}
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised learning tasks, relying on the ground-truth data label, a targeted objective, or supervision from a trained classifier. In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. Our framework exploits a mutual information neural estimator as an information-theoretic similarity measure to generate adversarial examples without supervision. We propose a new MinMax algorithm with provable convergence guarantees for efficient generation of unsupervised adversarial examples. Our framework can also be extended to supervised adversarial examples. When using unsupervised adversarial examples as a simple plug-in data augmentation tool for model retraining, significant improvements are consistently observed across different unsupervised tasks and datasets, including data reconstruction, representation learning, and contrastive learning. Our results show novel methods and considerable advantages in studying and improving unsupervised machine learning via adversarial examples.
Stream Distributed Coded Computing
Venue: arXiv cs.IT 2021
Year: 2021

The emerging large-scale and data-hungry algorithms require the computations to be delegated from a central server to several worker nodes. One major challenge in the distributed computations is to tackle delays and failures caused by the stragglers. To address this challenge, introducing efficient amount of redundant computations via distributed coded computation has received significant attention. Recent approaches in this area have mainly focused on introducing minimum computational redundancies to tolerate certain number of stragglers. To the best of our knowledge, the current literature lacks a unified end-to-end design in a heterogeneous setting where the workers can vary in their computation and communication capabilities. The contribution of this paper is to devise a novel framework for joint scheduling-coding, in a setting where the workers and the arrival of stream computational jobs are based on stochastic models. In our initial joint scheme, we propose a systematic framework that illustrates how to select a set of workers and how to split the computational load among the selected workers based on their differences in order to minimize the average in-order job execution delay. Through simulations, we demonstrate that the performance of our framework is dramatically better than the performance of naive method that splits the computational load uniformly among the workers, and it is close to the ideal performance.
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as $\mathcal{O}(1/K^{1/2})$, where $K$ is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by join incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as $\mathcal{O}(1/K^{3/4})$. In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are $(a)$ known, or $(b)$ unknown at the parameter server.
Technical Report on Data Integration and Preparation
Venue: arXiv cs.DB 2021
Year: 2021

AI application developers typically begin with a dataset of interest and a vision of the end analytic or insight they wish to gain from the data at hand. Although these are two very important components of an AI workflow, one often spends the first few weeks (sometimes months) in the phase we refer to as data conditioning. This step typically includes tasks such as figuring out how to prepare data for analytics, dealing with inconsistencies in the dataset, and determining which algorithm (or set of algorithms) will be best suited for the application. Larger, faster, and messier datasets such as those from Internet of Things sensors, medical devices or autonomous vehicles only amplify these issues. These challenges, often referred to as the three Vs (volume, velocity, variety) of Big Data, require low-level tools for data management, preparation and integration. In most applications, data can come from structured and/or unstructured sources and often includes inconsistencies, formatting differences, and a lack of ground-truth labels. In this report, we highlight a number of tools that can be used to simplify data integration and preparation steps. Specifically, we focus on data integration tools and techniques, a deep dive into an exemplar data integration tool, and a deep-dive in the evolving field of knowledge graphs. Finally, we provide readers with a list of practical steps and considerations that they can use to simplify the data integration challenge. The goal of this report is to provide readers with a view of state-of-the-art as well as practical tips that can be used by data creators that make data integration more seamless.
DeepFake-o-meter: An Open Platform for DeepFake Detection
Venue: arXiv cs.CV 2021
Year: 2021

In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes. The availability of open-source tools to create DeepFakes poses as a threat to the trustworthiness of the online media. In this work, we develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users. We describe the design and function of DeepFake-o-meter in this work.
Spam Prevention Using zk-SNARKs for Anonymous Peer-to-Peer Content Sharing Systems
Venue: arXiv cs.CR 2021
Year: 2021

Decentralized unpermissioned peer-to-peer networks are inherently vulnerable to spam when they allow arbitrary participants to submit content to a common public index or registry; preventing this is difficult due to the absence of a central arbitrator who can act as a gate-keeper. For this reason indexing of new content even in otherwise decentralized networks (e.g. Bittorrent with DHT, IPFS) has generally been left to centralized services such as torrent sites. Decentralized methods for spam prevention, such as Web of Trust, already exist[1][2] but they require submitters to assume pseudonymous identities and establish trust over time. In this paper we present a method of spam prevention that works under the assumption that the participants are fully anonymous and do not want different submissions of theirs to be linked to each other. By spam we do not specifically mean unsolicited advertising; rather it is the practice of adding a large amount of content in a short time, saturating the capacity of the network, and causing denial of service to peers. The purpose of our solution is to prevent users from saturating the system, and can be described as rate-limiting. The system should be censorship resistant: it should not be possible for submissions to be excluded because of the content itself, or for users to be excluded based on what they submit. We first discuss a solution based on a single, centralized rate-limiter that is censorship resistant and anonymous, then we extend this to a fully decentralized blockchain-based system and present methods to make it economical and scalable.
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
Venue: arXiv cs.LG 2021
Year: 2021

Data poisoning and backdoor attacks manipulate training data to induce security breaches in a victim model. These attacks can be provably deflected using differentially private (DP) training methods, although this comes with a sharp decrease in model performance. The InstaHide method has recently been proposed as an alternative to DP training that leverages supposed privacy properties of the mixup augmentation, although without rigorous guarantees. In this work, we show that strong data augmentations, such as mixup and random additive noise, nullify poison attacks while enduring only a small accuracy trade-off. To explain these finding, we propose a training method, DP-InstaHide, which combines the mixup regularizer with additive noise. A rigorous analysis of DP-InstaHide shows that mixup does indeed have privacy advantages, and that training with k-way mixup provably yields at least k times stronger DP guarantees than a naive DP mechanism. Because mixup (as opposed to noise) is beneficial to model performance, DP-InstaHide provides a mechanism for achieving stronger empirical performance against poisoning attacks than other known DP methods.
Design and Analysis of Delayed Bit-Interleaved Coded Modulation with LDPC Codes
Venue: arXiv cs.IT 2021
Year: 2021

This paper investigates the design and performance of delayed bit-interleaved coded modulation (DBICM) with low-density parity-check (LDPC) codes. For Gray labeled square $M$-ary quadrature amplitude modulation (QAM) constellations, we investigate the optimal delay scheme with the largest spectrum efficiency of DBICM for a fixed maximum number of delayed time slots and a given signal-to-noise ratio. When analyzing the capacity of DBICM, we find two important properties: the capacity improvement due to delayed coded bits being mapped to the real and imaginary parts of the transmitted symbols are independent of each other; a pair of delay schemes with delayed coded bits having identical bit-channel capacity lead to equivalent DBICM capacity. Using these two properties, we efficiently optimize the delay scheme for any uniform Gray-QAM systems. Furthermore, these two properties enable efficient LDPC code designs regarding unequal error protection via bit-channel type classifications. Moreover, we use protograph-based extrinsic information transfer charts to jointly optimize degree distributions and channel assignments of LDPC codes and propose a constrained progressive edge growth like algorithm to jointly construct LDPC codes and bit-interleavers for DBICM, taking distinctive bit-channel's capacity into account. Simulation results demonstrate that the designed LDPC coded DBICM systems significantly outperform LDPC coded BICM systems.
Energy and Cost Efficient Resource Allocation for Blockchain-Enabled NFV
Venue: arXiv cs.NI 2021
Year: 2021

Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators' OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users' service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users' SFCs without the need for an NFVO based on BC technology. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously.
Predicting Driver Fatigue in Automated Driving with Explainability
Venue: arXiv cs.LG 2021
Year: 2021

Research indicates that monotonous automated driving increases the incidence of fatigued driving. Although many prediction models based on advanced machine learning techniques were proposed to monitor driver fatigue, especially in manual driving, little is known about how these black-box machine learning models work. In this paper, we proposed a combination of eXtreme Gradient Boosting (XGBoost) and SHAP (SHapley Additive exPlanations) to predict driver fatigue with explanations due to their efficiency and accuracy. First, in order to obtain the ground truth of driver fatigue, PERCLOS (percentage of eyelid closure over the pupil over time) between 0 and 100 was used as the response variable. Second, we built a driver fatigue regression model using both physiological and behavioral measures with XGBoost and it outperformed other selected machine learning models with 3.847 root-mean-squared error (RMSE), 1.768 mean absolute error (MAE) and 0.996 adjusted $R^2$. Third, we employed SHAP to identify the most important predictor variables and uncovered the black-box XGBoost model by showing the main effects of most important predictor variables globally and explaining individual predictions locally. Such an explainable driver fatigue prediction model offered insights into how to intervene in automated driving when necessary, such as during the takeover transition period from automated driving to manual driving.
On Understanding the Relation of Knowledge and Confidence to Requirements Quality
Venue: arXiv cs.SE 2021
Year: 2021

Context and Motivation: Software requirements are affected by the knowledge and confidence of software engineers. Analyzing the interrelated impact of these factors is difficult because of the challenges of assessing knowledge and confidence. Question/Problem: This research aims to draw attention to the need for considering the interrelated effects of confidence and knowledge on requirements quality, which has not been addressed by previous publications. Principal ideas/results: For this purpose, the following steps have been taken: 1) requirements quality was defined based on the instructions provided by the ISO29148:2011 standard, 2) we selected the symptoms of low qualified requirements based on ISO29148:2011, 3) we analyzed five Software Requirements Specification (SRS) documents to find these symptoms, 3) people who have prepared the documents were categorized in four classes to specify the more/less knowledge and confidence they have regarding the symptoms, and 4) finally, the relation of lack of enough knowledge and confidence to symptoms of low quality was investigated. The results revealed that the simultaneous deficiency of confidence and knowledge has more negative effects in comparison with a deficiency of knowledge or confidence. Contribution: In brief, this study has achieved these results: 1) the realization that a combined lack of knowledge and confidence has a larger effect on requirements quality than only one of the two factors, 2) the relation between low qualified requirements and requirements engineers' needs for knowledge and confidence, and 3) variety of requirements engineers' needs for knowledge based on their abilities to make discriminative and consistent decisions.
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Venue: arXiv cs.CL 2021
Year: 2021

Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-stage process can yield substantial further gains and can be applied without modifying the model or learning objective.
Talaria: A Framework for Simulation of Permissioned Blockchains for Logistics and Beyond
Venue: arXiv cs.CR 2021
Year: 2021

In this paper, we present Talaria, a novel permissioned blockchain simulator that supports numerous protocols and use cases, most notably in supply chain management. Talaria extends the capability of BlockSim, an existing blockchain simulator, to include permissioned blockchains and serves as a foundation for further private blockchain assessment. Talaria is designed with both practical Byzantine Fault Tolerance (pBFT) and simplified version of Proof-of-Authority consensus protocols, but can be revised to include other permissioned protocols within its modular framework. Moreover, Talaria is able to simulate different types of malicious authorities and a variable daily transaction load at each node. In using Talaria, business practitioners and policy planners have an opportunity to measure, evaluate, and adapt a range of blockchain solutions for commercial operations.
Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system. We introduce a Markovian probability model to characterize the intrinsic temporal structure of the model aggregation series. With this temporal probability model, we formulate the model aggregation problem as to infer the desired aggregated update given all the past observations from a Bayesian perspective. We develop a message passing based algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to fulfil this estimation task with low complexity and near-optimal performance. We further establish the state evolution (SE) analysis to characterize the behaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the expected loss reduction of the FEEL system under certain standard regularity conditions. In addition, we develop an expectation maximization (EM) strategy to learn the unknown parameters in the Markovian model. We show that the proposed TSAGA algorithm significantly outperforms the state-of-the-art, and is able to achieve comparable learning performance as the error-free benchmark in terms of both convergence rate and final test accuracy.
Who Can Find My Devices? Security and Privacy of Apple's Crowd-Sourced Bluetooth Location Tracking System
Venue: arXiv cs.CR 2021
Year: 2021

Overnight, Apple has turned its hundreds-of-million-device ecosystem into the world's largest crowd-sourced location tracking network called offline finding (OF). OF leverages online finder devices to detect the presence of missing offline devices using Bluetooth and report an approximate location back to the owner via the Internet. While OF is not the first system of its kind, it is the first to commit to strong privacy goals. In particular, OF aims to ensure finder anonymity, untrackability of owner devices, and confidentiality of location reports. This paper presents the first comprehensive security and privacy analysis of OF. To this end, we recover the specifications of the closed-source OF protocols by means of reverse engineering. We experimentally show that unauthorized access to the location reports allows for accurate device tracking and retrieving a user's top locations with an error in the order of 10 meters in urban areas. While we find that OF's design achieves its privacy goals, we discover two distinct design and implementation flaws that can lead to a location correlation attack and unauthorized access to the location history of the past seven days, which could deanonymize users. Apple has partially addressed the issues following our responsible disclosure. Finally, we make our research artifacts publicly available.
Cooking Object's State Identification Without Using Pretrained Model
Venue: arXiv cs.CV 2021
Year: 2021

Recently, Robotic Cooking has been a very promising field. To execute a recipe, a robot has to recognize different objects and their states. Contrary to object recognition, state identification has not been explored that much. But it is very important because different recipe might require different state of an object. Moreover, robotic grasping depends on the state. Pretrained model usually perform very well in this type of tests. Our challenge was to handle this problem without using any pretrained model. In this paper, we have proposed a CNN and trained it from scratch. The model is trained and tested on the dataset from cooking state recognition challenge. We have also evaluated the performance of our network from various perspective. Our model achieves 65.8% accuracy on the unseen test dataset.