OpenRaiser/Intern-Atlas huggingface dataset:
A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation
Venue: arXiv cs.RO 2021
Year: 2021

Specifying tasks with videos is a powerful technique towards acquiring novel and general robot skills. However, reasoning over mechanics and dexterous interactions can make it challenging to scale learning contact-rich manipulation. In this work, we focus on the problem of visual non-prehensile planar manipulation: given a video of an object in planar motion, find contact-aware robot actions that reproduce the same object motion. We propose a novel architecture, Differentiable Learning for Manipulation (\ours), that combines video decoding neural models with priors from contact mechanics by leveraging differentiable optimization and finite difference based simulation. Through extensive simulated experiments, we investigate the interplay between traditional model-based techniques and modern deep learning approaches. We find that our modular and fully differentiable architecture performs better than learning-only methods on unseen objects and motions. \url{https://github.com/baceituno/dlm}.
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution
Venue: arXiv cs.LG 2021
Year: 2021

The rapidly growing size of deep neural network (DNN) models and datasets has given rise to a variety of distribution strategies such as data, tensor-model, pipeline parallelism, and hybrid combinations thereof. Each of these strategies offers its own trade-offs and exhibits optimal performance across different models and hardware topologies. Selecting the best set of strategies for a given setup is challenging because the search space grows combinatorially, and debugging and testing on clusters is expensive. In this work we propose DistIR, an expressive intermediate representation for distributed DNN computation that is tailored for efficient analyses, such as simulation. This enables automatically identifying the top-performing strategies without having to execute on physical hardware. Unlike prior work, DistIR can naturally express many distribution strategies including pipeline parallelism with arbitrary schedules. Our evaluation on MLP training and GPT-2 inference models demonstrates how DistIR and its simulator enable fast grid searches over complex distribution spaces spanning up to 1000+ configurations, reducing optimization time by an order of magnitude for certain regimes.
Intrinsic PUF Instance on Non-Volatile NAND Flash Memory
Venue: arXiv cs.CR 2021
Year: 2021

Embedded systems or micro controller based modules have become increasingly prevalent in our daily lives. However, the security of embedded devices as well as the authenticity of hardware has become an increasing concern within the growing Internet of Things (IoT) space. In this paper we setup an experiment environment where SLC flash program disturbance is observed. We discovered that intra-page disturbance is easier to be produced than inter-page disturbance. We also observed that adjacent pages are paired in (2n, 2n+1) manner, and disturbance only occurs within a pair. Lastly, we found that as page number increases from 0 to 63, it becomes more difficult to observe the first bit flip within a page, and thus more difficult to achieve the disturbance stable state.
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security
Venue: arXiv cs.LG 2021
Year: 2021

Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, Ensemble CorrDet with Adaptive Statistics strategy and other machine learning classification-based detection schemes.
OPlaceRAN -- a Placement Orchestrator for Virtualized Next-Generation of Radio Access Network
Venue: arXiv cs.NI 2021
Year: 2021

The fifth-generation mobile evolution enables transformations on Next-Generation Radio Access Networks (NG-RAN). The RAN protocol stack is split into eight disaggregated options combined in three network units, i.e., Central, Distributed, and Radio. Besides that, further advances allow the RAN functions to be virtualized on top of general-purpose hardware, using the concept of virtualized RAN (vRAN). The Combination of NG-RAN and vRAN results in vNG-RAN, which enables the management of the disaggregated units and protocols as a set of radio functions. However, the orchestration-based placement of these radio functions is a challenging issue since the best decision can be determined by multiple constraints involving RAN disaggregation, crosshaul networks requirements, availability of computational resources, etc. This article proposes OPlaceRAN, a vNG-RAN deployment orchestrator framed within the NFV reference architecture and aligned with the Open RAN initiative. OPlaceRAN supports the dynamic placement of radio functions focusing on vNG-RAN planning and is designed to be agnostic to the placement optimization model. To validate OPlaceRAN, we developed a prototype based on up-to-date cloud-native tools to deploy RAN using containerized virtualization using the OpenAirInterface emulator and considering two distinct functional splits (options 2 and 6). The evaluation is tested as proofs-of-concept in a real computing infrastructure using two different placement solutions. Our results reveal that OPlaceRAN is an effective cloud-native solution for containerized network functions placement and agnostic to the optimization model. Additionally, OPlaceRAN is up-to-date with the most advanced vNG-RAN design and development approaches, contributing to the evolution of the fifth-generation of mobile networks.
Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique
Venue: arXiv cs.CV 2021
Year: 2021

The project comes with the technique of OCR (Optical Character Recognition) which includes various research sides of computer science. The project is to take a picture of a character and process it up to recognize the image of that character like a human brain recognize the various digits. The project contains the deep idea of the Image Processing techniques and the big research area of machine learning and the building block of the machine learning called Neural Network. There are two different parts of the project. Training part comes with the idea of to train a child by giving various sets of similar characters but not the totally same and to say them the output of this is this. Like this idea one has to train the newly built neural network with so many characters. This part contains some new algorithm which is self-created and upgraded as the project need. The testing part contains the testing of a new dataset .This part always comes after the part of the training .At first one has to teach the child how to recognize the character .Then one has to take the test whether he has given right answer or not. If not, one has to train him harder by giving new dataset and new entries. Just like that one has to test the algorithm also. There are many parts of statistical modeling and optimization techniques which come into the project requiring a lot of modeling concept of statistics like optimizer technique and filtering process, that how the mathematics and prediction behind that filtering or the algorithms comes after or which result one actually needs to and ultimately for the prediction of a predictive model creation. Machine learning algorithm is built by concepts of prediction and programming.
Inclusive Speaker Verification with Adaptive thresholding
Venue: arXiv cs.SD 2021
Year: 2021

While using a speaker verification (SV) based system in a commercial application, it is important that customers have an inclusive experience irrespective of their gender, age, or ethnicity. In this paper, we analyze the impact of gender and age on SV and find that for a desired common False Acceptance Rate (FAR) across different gender and age groups, the False Rejection Rate (FRR) is different for different gender and age groups. To optimize FRR for all users for a desired FAR, we propose a context (e.g. gender, age) adaptive thresholding framework for SV. The context can be available as prior information for many practical applications. We also propose a concatenated gender/age detection model to algorithmically derive the context in absence of such prior information. We experimentally show that our context-adaptive thresholding method is effective in building a more efficient inclusive SV system. Specifically, we show that we can reduce FRR for specific gender for a desired FAR on the voxceleb1 test set by using gender-specific thresholds. Similar analysis on OGI kids' speech corpus shows that by using an age-specific threshold, we can significantly reduce FRR for certain age groups for desired FAR.
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) is a burgeoning distributed machine learning framework where a central parameter server (PS) coordinates many local users to train a globally consistent model. Conventional federated learning inevitably relies on a centralized topology with a PS. As a result, it will paralyze once the PS fails. To alleviate such a single point failure, especially on the PS, some existing work has provided decentralized FL (DFL) implementations like CDSGD and D-PSGD to facilitate FL in a decentralized topology. However, there are still some problems with these methods, e.g., significant divergence between users' final models in CDSGD and a network-wide model average necessity in D-PSGD. In order to solve these deficiency, this paper devises a new DFL implementation coined as DACFL, where each user trains its model using its own training data and exchanges the intermediate models with its neighbors through a symmetric and doubly stochastic matrix. The DACFL treats the progress of each user's local training as a discrete-time process and employs a first order dynamic average consensus (FODAC) method to track the \textit{average model} in the absence of the PS. In this paper, we also provide a theoretical convergence analysis of DACFL on the premise of i.i.d data to strengthen its rationality. The experimental results on MNIST, Fashion-MNIST and CIFAR-10 validate the feasibility of our solution in both time-invariant and time-varying network topologies, and declare that DACFL outperforms D-PSGD and CDSGD in most cases.
Space-Time Memory Network for Sounding Object Localization in Videos
Venue: arXiv cs.CV 2021
Year: 2021

Leveraging temporal synchronization and association within sight and sound is an essential step towards robust localization of sounding objects. To this end, we propose a space-time memory network for sounding object localization in videos. It can simultaneously learn spatio-temporal attention over both uni-modal and cross-modal representations from audio and visual modalities. We show and analyze both quantitatively and qualitatively the effectiveness of incorporating spatio-temporal learning in localizing audio-visual objects. We demonstrate that our approach generalizes over various complex audio-visual scenes and outperforms recent state-of-the-art methods.
On Designing a Two-stage Auction for Online Advertising
Venue: arXiv cs.GT 2021
Year: 2021

For the scalability of industrial online advertising systems, a two-stage auction architecture is widely used to enable efficient ad allocation on a large set of corpus within a limited response time. The current deployed two-stage ad auction usually retrieves an ad subset by a coarse ad quality metric in a pre-auction stage, and then determines the auction outcome by a refined metric in the subsequent stage. However, this simple and greedy solution suffers from serious performance degradation, as it regards the decision in each stage separately, leading to an improper ad selection metric for the pre-auction stage. In this work, we explicitly investigate the relation between the coarse and refined ad quality metrics, and design a two-stage ad auction by taking the decision interaction between the two stages into account. We decouple the design of the two-stage auction by solving a stochastic subset selection problem in the pre-auction stage and conducting a general second price (GSP) auction in the second stage. We demonstrate that this decouple still preserves the incentive compatibility of the auction mechanism. As the proposed formulation of the pre-auction stage is an NP-hard problem, we propose a scalable approximation solution by defining a new subset selection metric, namely Pre-Auction Score (PAS). Experiment results on both public and industrial dataset demonstrate the significant improvement on social welfare and revenue of the proposed two-stage ad auction, than the intuitive greedy two-stage auction and other baselines.
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes
Venue: arXiv cs.LG 2021
Year: 2021

This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free optimization schemes and multi-fidelity Gaussian processes within a Bayesian optimization framework. The proposed scheme uses two Gaussian processes for the stochastic system, one emulates the (known) process model, and another, the true system through measurements. In this way, low fidelity samples can be obtained via a model, while high fidelity samples are obtained through measurements of the system. This framework captures the system's behavior in a non-parametric fashion while driving exploration through acquisition functions. The benefit of using a Gaussian process to represent the system is the ability to perform uncertainty quantification in real-time and allow for chance constraints to be satisfied with high confidence. This results in a practical approach that is illustrated in numerical case studies, including a semi-batch photobioreactor optimization problem.
The Wind in Our Sails: Developing a Reusable and Maintainable Dutch Maritime History Knowledge Graph
Venue: arXiv cs.DL 2021
Year: 2021

Digital sources are more prevalent than ever but effectively using them can be challenging. One core challenge is that digitized sources are often distributed, thus forcing researchers to spend time collecting, interpreting, and aligning different sources. A knowledge graph can accelerate research by providing a single connected source of truth that humans and machines can query. During two design-test cycles, we convert four data sets from the historical maritime domain into a knowledge graph. The focus during these cycles is on creating a sustainable and usable approach that can be adopted in other linked data conversion efforts. Furthermore, our knowledge graph is available for maritime historians and other interested users to investigate the daily business of the Dutch East India Company through a unified portal.
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, multi-agent reinforcement learning (MARL) has presented impressive performance in various applications. However, physical limitations, budget restrictions, and many other factors usually impose \textit{constraints} on a multi-agent system (MAS), which cannot be handled by traditional MARL frameworks. Specifically, this paper focuses on constrained MASes where agents work \textit{cooperatively} to maximize the expected team-average return under various constraints on expected team-average costs, and develops a \textit{constrained cooperative MARL} framework, named DeCOM, for such MASes. In particular, DeCOM decomposes the policy of each agent into two modules, which empowers information sharing among agents to achieve better cooperation. In addition, with such modularization, the training algorithm of DeCOM separates the original constrained optimization into an unconstrained optimization on reward and a constraints satisfaction problem on costs. DeCOM then iteratively solves these problems in a computationally efficient manner, which makes DeCOM highly scalable. We also provide theoretical guarantees on the convergence of DeCOM's policy update algorithm. Finally, we validate the effectiveness of DeCOM with various types of costs in both toy and large-scale (with 500 agents) environments.
Effects of Design and Hydrodynamic Parameters on Optimized Swimming for Simulated, Fish-inspired Robots
Venue: arXiv cs.RO 2021
Year: 2021

In this work we developed a mathematical model and a simulation platform for a fish-inspired robotic template, namely Magnetic, Modular, Undulatory Robotics ($μ$Bots). Through this platform, we systematically explored the effects of design and fluid parameters on the swimming performance via reinforcement learning. The mathematical model was composed of two interacting subsystems, the robot dynamics and the hydrodynamics, and the hydrodynamic model consisted of reactive components (added-mass and pressure forces) and resistive components (drag and friction forces), which were then nondimensionalized for deriving key "control parameters" of robot-fluid interaction. The $μ$Bot was actuated via magnetic actuators controlled with harmonic voltage signals, which were optimized via EM-based Policy Hyper Parameter Exploration (EPHE) to maximize swimming speed. By varying the control parameters, total 36 cases with different robot template variations (Number of Actuation (NoA) and stiffness) and hydrodynamic parameters were simulated and optimized via EPHE. Results showed that wavelength of optimized gaits (i.e., traveling wave along body) was independent of template variations and hydrodynamic parameters. Higher NoA yielded higher speed but lower speed per body length however with diminishing gain and lower speed per body length. Body and caudal-fin gait dynamics were dominated by the interaction among fluid added-mass, spring, and actuation torque, with negligible contribution from fluid resistive drag. In contrast, thrust generation was dominated by pressure force acting on caudal fin, as steady swimming resulted from a balance between resistive force and pressure force, with minor contributions from added-mass and body drag forces. Therefore, added-mass force only indirectly affected the thrust generation and swimming speed via the caudal fin dynamics.
STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactions
Venue: arXiv cs.LG 2021
Year: 2021

Motivation: Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore that the action of a drug is mainly caused by its chemical substructures. In addition, their interpretability is still weak. Results: In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (sub-structures) and their DDI types are determined by the linkages between different substructure sets, we design a novel Substructure-ware Tensor Neural Network model for DDI prediction (STNN-DDI). The proposed model learns a 3-D tensor of (substructure, in-teraction type, substructure) triplets, which characterizes a substructure-substructure interaction (SSI) space. According to a list of predefined substructures with specific chemical meanings, the mapping of drugs into this SSI space enables STNN-DDI to perform the multiple-type DDI prediction in both transductive and inductive scenarios in a unified form with an explicable manner. The compar-ison with deep learning-based state-of-the-art baselines demonstrates the superiority of STNN-DDI with the significant improvement of AUC, AUPR, Accuracy, and Precision. More importantly, case studies illustrate its interpretability by both revealing a crucial sub-structure pair across drugs regarding a DDI type of interest and uncovering interaction type-specific substructure pairs in a given DDI. In summary, STNN-DDI provides an effective approach to predicting DDIs as well as explaining the interaction mechanisms among drugs.
Towards More Reliable Automated Program Repair by Integrating Static Analysis Techniques
Venue: arXiv cs.SE 2021
Year: 2021

A long-standing open challenge for automated program repair is the overfitting problem, which is caused by having insufficient or incomplete specifications to validate whether a generated patch is correct or not. Most available repair systems rely on weak specifications (i.e., specifications that are synthesized from test cases) which limits the quality of generated repairs. To strengthen specifications and improve the quality of repairs, we propose to closer integrate static bug detection techniques with automated program repair. The integration combines automated program repair with static analysis techniques in such a way that bug detection patterns can be synthesized into specifications that the repair system can use. We explore the feasibility of such integration using two types of bugs: arithmetic bugs, such as integer overflow, and logical bugs, such as termination bugs. As part of our analysis, we make several observations that help to improve patch generation for these classes of bugs. Moreover, these observations assist with narrowing down the candidate patch search space, and inferring an effective search order.
Preventing Handheld Phone Distraction for Drivers by Sensing the Gripping Hand
Venue: arXiv cs.HC 2021
Year: 2021

Handheld phone distraction is the leading cause of traffic accidents. However, few efforts have been devoted to detecting when the phone distraction happens, which is a critical input for taking immediate safety measures. This work proposes a phone-use monitoring system, which detects the start of the driver's handheld phone use and eliminates the distraction at once. Specifically, the proposed system emits periodic ultrasonic pulses to sense if the phone is being held in hand or placed on support surfaces (e.g., seat and cup holder) by capturing the unique signal interference resulted from the contact object's damping, reflection and refraction. We derive the short-time Fourier transform from the microphone data to describe such impacts and develop a CNN-based binary classifier to discriminate the phone use between the handheld and the handsfree status. Additionally, we design an adaptive window-based filter to correct the classification errors and identify each handheld phone distraction instance, including its start, end, and duration. Extensive experiments with fourteen people, three phones and two car models show that our system achieves 99% accuracy of recognizing handheld phone-use instances and 0.76-second median error to estimate the distraction's start time.