OpenRaiser/Intern-Atlas huggingface dataset:
Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics
Venue: arXiv cs.CR 2021
Year: 2021

Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of solutions are being explored but have not yet won the public's trust and full backing of mobile platforms. In this work, we propose numerical aggregation protocols that empirically improve upon prior art, while providing comparable local differential privacy guarantees. Sharing a single private bit per value supports privacy metering that enable privacy controls and guarantees that are not covered by differential privacy. We put emphasis on the ease of implementation, compatibility with existing methods, and compelling empirical performance.
Sexing Caucasian 2D footprints using convolutional neural networks
Venue: arXiv cs.CV 2021
Year: 2021

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N=196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N=2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N=267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint.
Hardware-aware Design of Multiplierless Second-Order IIR Filters with Minimum Adders
Venue: arXiv cs.AR 2021
Year: 2021

In this work, we optimally solve the problem of multiplierless design of second-order Infinite Impulse Response filters with minimum number of adders. Given a frequency specification, we design a stable direct form filter with hardware-aware fixed-point coefficients that yielding minimal number of adders when replacing all the multiplications by bit shifts and additions. The coefficient design, quantization and implementation, typically conducted independently, are now gathered into one global optimization problem, modeled through integer linear programming and efficiently solved using generic solvers. We guarantee the frequency-domain specifications and stability, which together with optimal number of adders will significantly simplify design-space exploration for filter designers. The optimal filters are implemented within the FloPoCo IP core generator and synthesized for Field Programmable Gate Arrays. With respect to state-of-the-art three-step filter design methods, our one-step design approach achieves, on average, 42% reduction in the number of lookup tables and 21% improvement in delay.
ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference
Venue: arXiv cs.CL 2021
Year: 2021

Neural language representation models such as BERT, pre-trained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasoning and inference. Natural Language Inference (NLI) is a challenging reasoning task that relies on common human understanding of language and real-world commonsense knowledge. We introduce a new model for NLI called External Knowledge Enhanced BERT (ExBERT), to enrich the contextual representation with real-world commonsense knowledge from external knowledge sources and enhance BERT's language understanding and reasoning capabilities. ExBERT takes full advantage of contextual word representations obtained from BERT and employs them to retrieve relevant external knowledge from knowledge graphs and to encode the retrieved external knowledge. Our model adaptively incorporates the external knowledge context required for reasoning over the inputs. Extensive experiments on the challenging SciTail and SNLI benchmarks demonstrate the effectiveness of ExBERT: in comparison to the previous state-of-the-art, we obtain an accuracy of 95.9% on SciTail and 91.5% on SNLI.
The history and future prospects of open data and open source software
Venue: arXiv cs.SI 2021
Year: 2021

Open data for all New Yorkers is the tagline on New York City's open data website. Open government is being promoted at most countries of the western world. Government transparency levels are being measured by the amount of data they share through their online public repositories. Additionally, open source software is promoted at governments, academia, and the industry. This is the new digital story of this century, and the new testament between the Gods of technology and there users. Data and software openness will redefine the path forward and aim to rekindle our collective intelligence. Data and software openness can redefine Data Democracy and be the catalyst for its progress. This chapter provides a historical insight into data and software openness, the beginnings, the heroes, prospects for the future, and all things we cannot afford to negotiate or lose.
Full-Text and URL Search Over Web Archives
Venue: arXiv cs.DL 2021
Year: 2021

Web archives are a historically valuable source of information. In some respects, web archives are the only record of the evolution of human society in the last two decades. They preserve a mix of personal and collective memories, the importance of which tends to grow as they age. However, the value of web archives depends on their users being able to search and access the information they require in efficient and effective ways. Without the possibility of exploring and exploiting the archived contents, web archives are useless. Web archive access functionalities range from basic browsing to advanced search and analytical services, accessed through user-friendly interfaces. Full-text and URL search have become the predominant and preferred forms of information discovery in web archives, fulfilling user needs and supporting search APIs that feed complex applications. Both full-text and URL search are based on the technology developed for modern web search engines, since the Web is the main resource targeted by both systems. However, while web search engines enable searching over the most recent web snapshot, web archives enable searching over multiple snapshots from the past. This means that web archives have to deal with a temporal dimension that is the cause of new challenges and opportunities, discussed throughout this chapter.
Predicting Zip Code-Level Vaccine Hesitancy in US Metropolitan Areas Using Machine Learning Models on Public Tweets
Venue: arXiv cs.SI 2021
Year: 2021

Although the recent rise and uptake of COVID-19 vaccines in the United States has been encouraging, there continues to be significant vaccine hesitancy in various geographic and demographic clusters of the adult population. Surveys, such as the one conducted by Gallup over the past year, can be useful in determining vaccine hesitancy, but can be expensive to conduct and do not provide real-time data. At the same time, the advent of social media suggests that it may be possible to get vaccine hesitancy signals at an aggregate level (such as at the level of zip codes) by using machine learning models and socioeconomic (and other) features from publicly available sources. It is an open question at present whether such an endeavor is feasible, and how it compares to baselines that only use constant priors. To our knowledge, a proper methodology and evaluation results using real data has also not been presented. In this article, we present such a methodology and experimental study, using publicly available Twitter data collected over the last year. Our goal is not to devise novel machine learning algorithms, but to evaluate existing and established models in a comparative framework. We show that the best models significantly outperform constant priors, and can be set up using open-source tools.
Categorical EHR Imputation with Generative Adversarial Nets
Venue: arXiv cs.LG 2021
Year: 2021

Electronic Health Records often suffer from missing data, which poses a major problem in clinical practice and clinical studies. A novel approach for dealing with missing data are Generative Adversarial Nets (GANs), which have been generating huge research interest in image generation and transformation. Recently, researchers have attempted to apply GANs to missing data generation and imputation for EHR data: a major challenge here is the categorical nature of the data. State-of-the-art solutions to the GAN-based generation of categorical data involve either reinforcement learning, or learning a bidirectional mapping between the categorical and the real latent feature space, so that the GANs only need to generate real-valued features. However, these methods are designed to generate complete feature vectors instead of imputing only the subsets of missing features. In this paper we propose a simple and yet effective approach that is based on previous work on GANs for data imputation. We first motivate our solution by discussing the reason why adversarial training often fails in case of categorical features. Then we derive a novel way to re-code the categorical features to stabilize the adversarial training. Based on experiments on two real-world EHR data with multiple settings, we show that our imputation approach largely improves the prediction accuracy, compared to more traditional data imputation approaches.
An Empirical Evaluation of End-to-End Polyphonic Optical Music Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Previous work has shown that neural architectures are able to perform optical music recognition (OMR) on monophonic and homophonic music with high accuracy. However, piano and orchestral scores frequently exhibit polyphonic passages, which add a second dimension to the task. Monophonic and homophonic music can be described as homorhythmic, or having a single musical rhythm. Polyphonic music, on the other hand, can be seen as having multiple rhythmic sequences, or voices, concurrently. We first introduce a workflow for creating large-scale polyphonic datasets suitable for end-to-end recognition from sheet music publicly available on the MuseScore forum. We then propose two novel formulations for end-to-end polyphonic OMR -- one treating the problem as a type of multi-task binary classification, and the other treating it as multi-sequence detection. Building upon the encoder-decoder architecture and an image encoder proposed in past work on end-to-end OMR, we propose two novel decoder models -- FlagDecoder and RNNDecoder -- that correspond to the two formulations. Finally, we compare the empirical performance of these end-to-end approaches to polyphonic OMR and observe a new state-of-the-art performance with our multi-sequence detection decoder, RNNDecoder.
Goldilocks: Consistent Crowdsourced Scalar Annotations with Relative Uncertainty
Venue: arXiv cs.HC 2021
Year: 2021

Human ratings have become a crucial resource for training and evaluating machine learning systems. However, traditional elicitation methods for absolute and comparative rating suffer from issues with consistency and often do not distinguish between uncertainty due to disagreement between annotators and ambiguity inherent to the item being rated. In this work, we present Goldilocks, a novel crowd rating elicitation technique for collecting calibrated scalar annotations that also distinguishes inherent ambiguity from inter-annotator disagreement. We introduce two main ideas: grounding absolute rating scales with examples and using a two-step bounding process to establish a range for an item's placement. We test our designs in three domains: judging toxicity of online comments, estimating satiety of food depicted in images, and estimating age based on portraits. We show that (1) Goldilocks can improve consistency in domains where interpretation of the scale is not universal, and that (2) representing items with ranges lets us simultaneously capture different sources of uncertainty leading to better estimates of pairwise relationship distributions.
Deep Learning Chromatic and Clique Numbers of Graphs
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks have been applied to a wide range of problems across different application domains with great success. Recently, research into combinatorial optimization problems in particular has generated much interest in the machine learning community. In this work, we develop deep learning models to predict the chromatic number and maximum clique size of graphs, both of which represent classical NP-complete combinatorial optimization problems encountered in graph theory. The neural networks are trained using the most basic representation of the graph, the adjacency matrix, as opposed to undergoing complex domain-specific feature engineering. The experimental results show that deep neural networks, and in particular convolutional neural networks, obtain strong performance on this problem.
Lachesis: Scalable Asynchronous BFT on DAG Streams
Venue: arXiv cs.DC 2021
Year: 2021

This paper consolidates the core technologies and key concepts of our novel Lachesis consensus protocol and Fantom Opera platform, which is permissionless, leaderless and EVM compatible. We introduce our new protocol, so-called Lachesis, for distributed networks achieving Byzantine fault tolerance (BFT)~\cite{lachesis01}. Each node in Lachesis protocol operates on a local block DAG, namely \emph{OPERA DAG}. Aiming for a low time to finality (TTF) for transactions, our general model considers DAG streams of high speed but asynchronous events. We integrate Proof-of-Stake (PoS) into a DAG model in Lachesis protocol to improve performance and security. Our general model of trustless system leverages participants' stake as their validating power~\cite{stakedag}. Lachesis's consensus algorithm uses Lamport timestamps, graph layering and concurrent common knowledge to guarantee a consistent total ordering of event blocks and transactions. In addition, Lachesis protocol allows dynamic participation of new nodes into Opera network. Lachesis optimizes DAG storage and processing time by splitting local history into checkpoints (so-called epochs). We also propose a model to improve stake decentralization, and network safety and liveness ~\cite{stairdag}. Built on our novel Lachesis protocol, Fantom's Opera platform is a public, leaderless, asynchronous BFT, layer-1 blockchain, with guaranteed deterministic finality. Hence, Lachesis protocol is suitable for distributed ledgers by leveraging asynchronous partially ordered sets with logical time ordering instead of blockchains. We also present our proofs into a model that can be applied to abstract asynchronous distributed system.
Deterministic Logarithmic Completeness in the Distributed Sleeping Model
Venue: arXiv cs.DC 2021
Year: 2021

We provide a deterministic scheme for solving any decidable problem in the distributed {sleeping model}. The sleeping model is a generalization of the standard message-passing model, with an additional capability of network nodes to enter a sleeping state occasionally. As long as a vertex is in the awake state, it is similar to the standard message-passing setting. However, when a vertex is asleep it cannot receive or send messages in the network nor can it perform internal computations. On the other hand, sleeping rounds do not count towards {\awake complexity.} Awake complexity is the main complexity measurement in this setting, which is the number of awake rounds a vertex spends during an execution. In this paper we devise algorithms with worst-case guarantees on the awake complexity. We devise a deterministic scheme with awake complexity of $O(\log n)$ for solving any decidable problem in this model by constructing a structure we call { Distributed Layered Tree}. This structure turns out to be very powerful in the sleeping model, since it allows one to collect the entire graph information within a constant number of awake rounds. Moreover, we prove that our general technique cannot be improved in this model, by showing that the construction of distributed layered trees itself requires $Ω(\log n)$ awake rounds. Another result we obtain in this work is a deterministic scheme for solving any problem from a class of problems, denoted O-LOCAL, in $O(\log Δ+ \log^*n)$ awake rounds. This class contains various well-studied problems, such as MIS and $(Δ+1)$-vertex-coloring.
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

The popularity and promotion of depth maps have brought new vigor and vitality into salient object detection (SOD), and a mass of RGB-D SOD algorithms have been proposed, mainly concentrating on how to better integrate cross-modality features from RGB image and depth map. For the cross-modality interaction in feature encoder, existing methods either indiscriminately treat RGB and depth modalities, or only habitually utilize depth cues as auxiliary information of the RGB branch. Different from them, we reconsider the status of two modalities and propose a novel Cross-modality Discrepant Interaction Network (CDINet) for RGB-D SOD, which differentially models the dependence of two modalities according to the feature representations of different layers. To this end, two components are designed to implement the effective cross-modality interaction: 1) the RGB-induced Detail Enhancement (RDE) module leverages RGB modality to enhance the details of the depth features in low-level encoder stage. 2) the Depth-induced Semantic Enhancement (DSE) module transfers the object positioning and internal consistency of depth features to the RGB branch in high-level encoder stage. Furthermore, we also design a Dense Decoding Reconstruction (DDR) structure, which constructs a semantic block by combining multi-level encoder features to upgrade the skip connection in the feature decoding. Extensive experiments on five benchmark datasets demonstrate that our network outperforms $15$ state-of-the-art methods both quantitatively and qualitatively. Our code is publicly available at: https://rmcong.github.io/proj_CDINet.html.
Self-Supervised Learning of Depth and Ego-Motion from Video by Alternative Training and Geometric Constraints from 3D to 2D
Venue: arXiv cs.CV 2021
Year: 2021

Self-supervised learning of depth and ego-motion from unlabeled monocular video has acquired promising results and drawn extensive attention. Most existing methods jointly train the depth and pose networks by photometric consistency of adjacent frames based on the principle of structure-from-motion (SFM). However, the coupling relationship of the depth and pose networks seriously influences the learning performance, and the re-projection relations is sensitive to scale ambiguity, especially for pose learning. In this paper, we aim to improve the depth-pose learning performance without the auxiliary tasks and address the above issues by alternative training each task and incorporating the epipolar geometric constraints into the Iterative Closest Point (ICP) based point clouds match process. Distinct from jointly training the depth and pose networks, our key idea is to better utilize the mutual dependency of these two tasks by alternatively training each network with respective losses while fixing the other. We also design a log-scale 3D structural consistency loss to put more emphasis on the smaller depth values during training. To makes the optimization easier, we further incorporate the epipolar geometry into the ICP based learning process for pose learning. Extensive experiments on various benchmarks datasets indicate the superiority of our algorithm over the state-of-the-art self-supervised methods.
Exact and Heuristic Approaches to Drone Delivery Problems
Venue: arXiv cs.AI 2021
Year: 2021

The Flying Sidekick Traveling Salesman Problem (FSTSP) considers a delivery system composed by a truck and a drone. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel Mixed Integer Programming (MIP) formulation and a heuristic approach to address the problem. The proposedMIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.
High-Performance Level-1 and Level-2 BLAS
Venue: arXiv cs.MS 2021
Year: 2021

The introduction of the Basic Linear Algebra Subroutine (BLAS) in the 1970s paved the way for different libraries to solve the same problem with an improved approach and hardware. The new BLAS implementation led to High-Performance Computing (HPC) innovation. All the love went to the level 3 BLAS due to its humongous application in different fields, not bounded by computer science. However, level 1 and level 2 got neglected; we tried to solve the problem by introducing the new algorithm for the Vector-Vector dot product, Vector-Vector outer product and Matrix-Vector product, which improves the performance of these operations in a significant way. We are not introducing any library but algorithms, which improves upon the current state of art algorithms. Also, we rely on the FMA instruction, OpenMP, and the compiler to optimize the code rather than implementing the algorithm in assembly. Therefore, our current implementation is machine oblivious and depends on the compilers ability to optimize the code.
The MIT Supercloud Dataset
Venue: arXiv cs.DC 2021
Year: 2021

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial cloud systems. This has led to changes in deployment approaches of HPC clusters and the commercial cloud, as well as a new focus on approaches to optimized resource usage, allocations and deployment of new AI frame- works, and capabilities such as Jupyter notebooks to enable rapid prototyping and deployment. With these changes, there is a need to better understand cluster/datacenter operations with the goal of developing improved scheduling policies, identifying inefficiencies in resource utilization, energy/power consumption, failure prediction, and identifying policy violations. In this paper we introduce the MIT Supercloud Dataset which aims to foster innovative AI/ML approaches to the analysis of large scale HPC and datacenter/cloud operations. We provide detailed monitoring logs from the MIT Supercloud system, which include CPU and GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This paper discusses the details of the dataset, collection methodology, data availability, and discusses potential challenge problems being developed using this data. Datasets and future challenge announcements will be available via https://dcc.mit.edu.