OpenRaiser/Intern-Atlas huggingface dataset:
Transferability of Adversarial Examples to Attack Cloud-based Image Classifier Service
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, Deep Learning(DL) techniques have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. While many recent works demonstrated that DL models are vulnerable to adversarial examples. Fortunately, generating adversarial examples usually requires white-box access to the victim model, and real-world cloud-based image classification services are more complex than white-box classifier,the architecture and parameters of DL models on cloud platforms cannot be obtained by the attacker. The attacker can only access the APIs opened by cloud platforms. Thus, keeping models in the cloud can usually give a (false) sense of security. In this paper, we mainly focus on studying the security of real-world cloud-based image classification services. Specifically, (1) We propose a novel attack method, Fast Featuremap Loss PGD (FFL-PGD) attack based on Substitution model, which achieves a high bypass rate with a very limited number of queries. Instead of millions of queries in previous studies, our method finds the adversarial examples using only two queries per image; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based classification services. Through evaluations on four popular cloud platforms including Amazon, Google, Microsoft, Clarifai, we demonstrate that FFL-PGD attack has a success rate over 90\% among different classification services. (3) We discuss the possible defenses to address these security challenges in cloud-based classification services. Our defense technology is mainly divided into model training stage and image preprocessing stage.
Ethics of Technology needs more Political Philosophy
Venue: arXiv cs.CY 2020
Year: 2020

The ongoing debate on the ethics of self-driving cars typically focuses on two approaches to answering ethical questions: moral philosophy and social science. I argue that these two approaches are both lacking. We should neither deduce answers from individual moral theories nor should we expect social science to give us complete answers. To supplement these approaches, we should turn to political philosophy. The issues we face are collective decisions that we make together rather than individual decisions we make in light of what we each have reason to value. Political philosophy adds three basic concerns to our conceptual toolkit: reasonable pluralism, human agency, and legitimacy. These three concerns have so far been largely overlooked in the debate on the ethics of self-driving cars.
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs
Venue: arXiv cs.DC 2020
Year: 2020

Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growing demand for DNN chips. However, designing DNN chips is non-trivial because: (1) mainstream DNNs have millions of parameters and operations; (2) the large design space due to the numerous design choices of dataflows, processing elements, memory hierarchy, etc.; and (3) an algorithm/hardware co-design is needed to allow the same DNN functionality to have a different decomposition, which would require different hardware IPs to meet the application specifications. Therefore, DNN chips take a long time to design and require cross-disciplinary experts. To enable fast and effective DNN chip design, we propose AutoDNNchip - a DNN chip generator that can automatically generate both FPGA- and ASIC-based DNN chip implementation given DNNs from machine learning frameworks (e.g., PyTorch) for a designated application and dataset. Specifically, AutoDNNchip consists of two integrated enablers: (1) a Chip Predictor, built on top of a graph-based accelerator representation, which can accurately and efficiently predict a DNN accelerator's energy, throughput, and area based on the DNN model parameters, hardware configuration, technology-based IPs, and platform constraints; and (2) a Chip Builder, which can automatically explore the design space of DNN chips (including IP selection, block configuration, resource balancing, etc.), optimize chip design via the Chip Predictor, and then generate optimized synthesizable RTL to achieve the target design metrics. Experimental results show that our Chip Predictor's predicted performance differs from real-measured ones by < 10% when validated using 15 DNN models and 4 platforms (edge-FPGA/TPU/GPU and ASIC). Furthermore, accelerators generated by our AutoDNNchip can achieve better (up to 3.86X improvement) performance than that of expert-crafted state-of-the-art accelerators.
Accelerating Forward and Backward Private Searchable Encryption Using Trusted Execution
Venue: arXiv cs.CR 2020
Year: 2020

Searchable encryption (SE) is one of the key enablers for building encrypted databases. It allows a cloud server to search over encrypted data without decryption. Dynamic SE additionally includes data addition and deletion operations to enrich the functions of encrypted databases. Recent attacks exploiting the leakage in dynamic operations drive rapid development of new SE schemes revealing less information while performing updates; they are also known as forward and backward private SE. Newly added data is no longer linkable to queries issued before, and deleted data is no longer searchable in queries issued later. However, those advanced SE schemes reduce the efficiency of SE, especially in the communication cost between the client and server. In this paper, we resort to the hardware-assisted solution, aka Intel SGX, to ease the above bottleneck. Our key idea is to leverage SGX to take over the most tasks of the client, i.e., tracking keyword states along with data addition and caching deleted data. However, handling large datasets is non-trivial due to the I/O and memory constraints of the SGX enclave. We further develop batch data processing and state compression technique to reduce the communication overhead between the SGX and untrusted server, and minimise the memory footprint in the enclave. We conduct a comprehensive set of evaluations on both synthetic and real-world datasets, which confirm that our designs outperform the prior art.
Reward Engineering for Object Pick and Place Training
Venue: arXiv cs.AI 2020
Year: 2020

Robotic grasping is a crucial area of research as it can result in the acceleration of the automation of several Industries utilizing robots ranging from manufacturing to healthcare. Reinforcement learning is the field of study where an agent learns a policy to execute an action by exploring and exploiting rewards from an environment. Reinforcement learning can thus be used by the agent to learn how to execute a certain task, in our case grasping an object. We have used the Pick and Place environment provided by OpenAI's Gym to engineer rewards. Hindsight Experience Replay (HER) has shown promising results with problems having a sparse reward. In the default configuration of the OpenAI baseline and environment the reward function is calculated using the distance between the target location and the robot end-effector. By weighting the cost based on the distance of the end-effector from the goal in the x,y and z-axes we were able to almost halve the learning time compared to the baselines provided by OpenAI, an intuitive strategy that further reduced learning time. In this project, we were also able to introduce certain user desired trajectories in the learnt policies (city-block / Manhattan trajectories). This helps us understand that by engineering the rewards we can tune the agent to learn policies in a certain way even if it might not be the most optimal but is the desired manner.
Grundy Coloring & friends, Half-Graphs, Bicliques
Venue: arXiv cs.CC 2020
Year: 2020

The first-fit coloring is a heuristic that assigns to each vertex, arriving in a specified order $σ$, the smallest available color. The problem Grundy Coloring asks how many colors are needed for the most adversarial vertex ordering $σ$, i.e., the maximum number of colors that the first-fit coloring requires over all possible vertex orderings. Since its inception by Grundy in 1939, Grundy Coloring has been examined for its structural and algorithmic aspects. A brute-force $f(k)n^{2^{k-1}}$-time algorithm for Grundy Coloring on general graphs is not difficult to obtain, where $k$ is the number of colors required by the most adversarial vertex ordering. It was asked several times whether the dependency on $k$ in the exponent of $n$ can be avoided or reduced, and its answer seemed elusive until now. We prove that Grundy Coloring is W[1]-hard and the brute-force algorithm is essentially optimal under the Exponential Time Hypothesis, thus settling this question by the negative. The key ingredient in our W[1]-hardness proof is to use so-called half-graphs as a building block to transmit a color from one vertex to another. Leveraging the half-graphs, we also prove that b-Chromatic Core is W[1]-hard, whose parameterized complexity was posed as an open question by Panolan et al. [JCSS '17]. A natural follow-up question is, how the parameterized complexity changes in the absence of (large) half-graphs. We establish fixed-parameter tractability on $K_{t,t}$-free graphs for b-Chromatic Core and Partial Grundy Coloring, making a step toward answering this question. The key combinatorial lemma underlying the tractability result might be of independent interest.
Optimizing the Write Fidelity of MRAMs
Venue: arXiv cs.IT 2020
Year: 2020

Magnetic random-access memory (MRAM) is a promising memory technology due to its high density, non-volatility, and high endurance. However, achieving high memory fidelity incurs significant write-energy costs, which should be reduced for large-scale deployment of MRAMs. In this paper, we formulate an optimization problem for maximizing the memory fidelity given energy constraints, and propose a biconvex optimization approach to solve it. The basic idea is to allocate non-uniform write pulses depending on the importance of each bit position. The fidelity measure we consider is minimum mean squared error (MSE), for which we propose an iterative water-filling algorithm. Although the iterative algorithm does not guarantee global optimality, we can choose a proper starting point that decreases the MSE exponentially and guarantees fast convergence. For an 8-bit accessed word, the proposed algorithm reduces the MSE by a factor of 21.
Functional Error Correction for Robust Neural Networks
Venue: arXiv cs.IT 2020
Year: 2020

When neural networks (NeuralNets) are implemented in hardware, their weights need to be stored in memory devices. As noise accumulates in the stored weights, the NeuralNet's performance will degrade. This paper studies how to use error correcting codes (ECCs) to protect the weights. Different from classic error correction in data storage, the optimization objective is to optimize the NeuralNet's performance after error correction, instead of minimizing the Uncorrectable Bit Error Rate in the protected bits. That is, by seeing the NeuralNet as a function of its input, the error correction scheme is function-oriented. A main challenge is that a deep NeuralNet often has millions to hundreds of millions of weights, causing a large redundancy overhead for ECCs, and the relationship between the weights and its NeuralNet's performance can be highly complex. To address the challenge, we propose a Selective Protection (SP) scheme, which chooses only a subset of important bits for ECC protection. To find such bits and achieve an optimized tradeoff between ECC's redundancy and NeuralNet's performance, we present an algorithm based on deep reinforcement learning. Experimental results verify that compared to the natural baseline scheme, the proposed algorithm achieves substantially better performance for the functional error correction task.
Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing
Venue: arXiv cs.DC 2020
Year: 2020

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation.
On the challenges ahead of spatial scientometrics focusing on the city level
Venue: arXiv cs.DL 2020
Year: 2020

Since the mid-1970s, it has become highly acknowledged to measure and evaluate changes in international research collaborations and the scientific performance of institutions and countries through the prism of bibliometric and scientometric data. Spatial bibliometrics and scientometrics (henceforward spatial scientometrics) have traditionally focused on examining both country and regional levels; however, in recent years, numerous spatial analyses on the city level have been carried out. While city-level scientometric analyses have gained popularity among policymakers and statistical/economic research organizations, researchers in the field of bibliometrics are divided regarding whether it is possible to observe the spatial unit 'city' through bibliometric and scientometric tools. After systematically scrutinizing relevant studies in the field, three major problems have been identified: 1) there is no standardized method of how cities should be defined and how metropolitan areas should be delineated, 2) there is no standardized method of how bibliometric and scientometric data on the city level should be collected and processed and 3) it is not clearly defined how cities can profit from the results of bibliometric and scientometric analysis focusing on them. This paper investigates major challenges ahead of spatial scientometrics, focusing on the city level and presents some possible solutions.
Modelling Orebody Structures: Block Merging Algorithms and Block Model Spatial Restructuring Strategies Given Mesh Surfaces of Geological Boundaries
Venue: arXiv cs.CE 2020
Year: 2020

This paper describes a framework for capturing geological structures in a 3D block model and improving its spatial fidelity given new mesh surfaces. Using surfaces that represent geological boundaries, the objectives are to identify areas where refinement is needed, increase spatial resolution to minimize surface approximation error, reduce redundancy to increase the compactness of the model and identify the geological domain on a block-by-block basis. These objectives are fulfilled by four system components which perform block-surface overlap detection, spatial structure decomposition, sub-blocks consolidation and block tagging, respectively. The main contributions are a coordinate-ascent merging algorithm and a flexible architecture for updating the spatial structure of a block model when given multiple surfaces, which emphasizes the ability to selectively retain or modify previously assigned block labels. The techniques employed include block-surface intersection analysis based on the separable axis theorem and ray-tracing for establishing the location of blocks relative to surfaces. To demonstrate the robustness and applicability of the proposed block merging strategy in a more narrow setting, it is used to reduce block fragmentation in an existing model where surfaces are not given and the minimum block size is fixed. To obtain further insight, a systematic comparison with octree subblocking subsequently illustrates the inherent constraints of dyadic hierarchical decomposition and the importance of inter-scale merging. The results show the proposed method produces merged blocks with less extreme aspect ratios and is highly amenable to parallel processing. The overall framework is applicable to orebody modelling given geological boundaries, and 3D segmentation more generally, where there is a need to delineate spatial regions using mesh surfaces within a block model.
Universal Successor Features for Transfer Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et al., 2015), which generalizes over goals and states, has previously been shown to be useful for transfer. However, successor features are believed to be more suitable than values for transfer (Dayan, 1993; Barreto et al.,2017), even though they cannot directly generalize to new goals. In this paper, we propose (1) Universal Successor Features (USFs) to capture the underlying dynamics of the environment while allowing generalization to unseen goals and (2) a flexible end-to-end model of USFs that can be trained by interacting with the environment. We show that learning USFs is compatible with any RL algorithm that learns state values using a temporal difference method. Our experiments in a simple gridworld and with two MuJoCo environments show that USFs can greatly accelerate training when learning multiple tasks and can effectively transfer knowledge to new tasks.
Exploiting Unlabeled Data in Smart Cities using Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data brings threatening problems to people's lives. Federated learning has emerged as an effective technique to avoid privacy infringement as well as increase the utilization of the data. However, there is a scarcity in the amount of labeled data and an abundance of unlabeled data collected in smart cities, hence there is a need to use semi-supervised learning. We propose a semi-supervised federated learning method called FedSem that exploits unlabeled data. The algorithm is divided into two phases where the first phase trains a global model based on the labeled data. In the second phase, we use semi-supervised learning based on the pseudo labeling technique to improve the model. We conducted several experiments using traffic signs dataset to show that FedSem can improve accuracy up to 8% by utilizing the unlabeled data in the learning process.
FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile
Venue: arXiv cs.NI 2020
Year: 2020

Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices.To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, and Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.
An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs
Venue: arXiv cs.LG 2020
Year: 2020

While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same hospital, but perform significantly worse when they are tested in different hospital systems. Furthermore, even within a given hospital system, deep learning models have been shown to depend on hospital- and patient-level confounders rather than meaningful pathology to make classifications. In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classification, and that they will work well even when tested on images from hospitals that were not included in the training data. We attempt to address this problem in the context of pneumonia classification from chest radiographs. We propose an approach based on adversarial optimization, which allows us to learn more robust models that do not depend on confounders. Specifically, we demonstrate improved out-of-hospital generalization performance of a pneumonia classifier by training a model that is invariant to the view position of chest radiographs (anterior-posterior vs. posterior-anterior). Our approach leads to better predictive performance on external hospital data than both a standard baseline and previously proposed methods to handle confounding, and also suggests a method for identifying models that may rely on confounders. Code available at https://github.com/suinleelab/cxr_adv.
Upper Bound Scalability on Achievable Rates of Batched Codes for Line Networks
Venue: arXiv cs.IT 2020
Year: 2020

The capacity of line networks with buffer size constraints is an open, but practically important problem. In this paper, the upper bound on the achievable rate of a class of codes, called batched codes, is studied for line networks. Batched codes enable a range of buffer size constraints, and are general enough to include special coding schemes studied in the literature for line networks. Existing works have characterized the achievable rates of batched codes for several classes of parameter sets, but leave the cut-set bound as the best existing general upper bound. In this paper, we provide upper bounds on the achievable rates of batched codes as functions of line network length for these parameter sets. Our upper bounds are tight in order of the network length compared with the existing achievability results.