OpenRaiser/Intern-Atlas huggingface dataset:
Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan
Venue: arXiv cs.IR 2020
Year: 2020

[Background] Two clusters of coronavirus disease 2019 (COVID-19) were confirmed in Hokkaido, Japan in February 2020. To capture the clusters, this study employs Web search query logs and user location information from smartphones. [Material and Methods] First, we anonymously identified smartphone users who used a Web search engine (Yahoo! JAPAN Search) for the COVID-19 or its symptoms via its companion application for smartphones (Yahoo Japan App). We regard these searchers as Web searchers who are suspicious of their own COVID-19 infection (WSSCI). Second, we extracted the location of the WSSCI via the smartphone application. The spatio-temporal distribution of the number of WSSCI are compared with the actual location of the known two clusters. [Result and Discussion] Before the early stage of the cluster development, we could confirm several WSSCI, which demonstrated the basic feasibility of our WSSCI-based approach. However, it is accurate only in the early stage, and it was biased after the public announcement of the cluster development. For the case where the other cluster-related resources, such as fine-grained population statistics, are not available, the proposed metric would be helpful to catch the hint of emerging clusters.
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments. Despite this potential, most of the robot learning systems today are deployed as a fixed policy and they are not being adapted after their deployment. Can we efficiently adapt previously learned behaviors to new environments, objects and percepts in the real world? In this paper, we present a method and empirical evidence towards a robot learning framework that facilitates continuous adaption. In particular, we demonstrate how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning, including changes in background, object shape and appearance, lighting conditions, and robot morphology. Further, this adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning, and that pre-training via RL is essential: training from scratch or adapting from supervised ImageNet features are both unsuccessful with such small amounts of data. We also find that these positive results hold in a limited continual learning setting, in which we repeatedly fine-tune a single lineage of policies using data from a succession of new tasks. Our empirical conclusions are consistently supported by experiments on simulated manipulation tasks, and by 52 unique fine-tuning experiments on a real robotic grasping system pre-trained on 580,000 grasps.
On-the-Fly Joint Feature Selection and Classification
Venue: arXiv cs.LG 2020
Year: 2020

Joint feature selection and classification in an online setting is essential for time-sensitive decision making. However, most existing methods treat this coupled problem independently. Specifically, online feature selection methods can handle either streaming features or data instances offline to produce a fixed set of features for classification, while online classification methods classify incoming instances using full knowledge about the feature space. Nevertheless, all existing methods utilize a set of features, common for all data instances, for classification. Instead, we propose a framework to perform joint feature selection and classification on-the-fly, so as to minimize the number of features evaluated for every data instance and maximize classification accuracy. We derive the optimum solution of the associated optimization problem and analyze its structure. Two algorithms are proposed, ETANA and F-ETANA, which are based on the optimum solution and its properties. We evaluate the performance of the proposed algorithms on several public datasets, demonstrating (i) the dominance of the proposed algorithms over the state-of-the-art, and (ii) its applicability to broad range of application domains including clinical research and natural language processing.
Decomposed Adversarial Learned Inference
Venue: arXiv cs.LG 2020
Year: 2020

Effective inference for a generative adversarial model remains an important and challenging problem. We propose a novel approach, Decomposed Adversarial Learned Inference (DALI), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model. We derive an equivalent form of the prior and conditional matching objective that can be optimized efficiently without any parametric assumption on the data. We validate the effectiveness of DALI on the MNIST, CIFAR-10, and CelebA datasets by conducting quantitative and qualitative evaluations. Results demonstrate that DALI significantly improves both reconstruction and generation as compared to other adversarial inference models.
Probabilistic Safety for Bayesian Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations. Given a compact set of input points, $T \subseteq \mathbb{R}^m$, we study the probability w.r.t. the BNN posterior that all the points in $T$ are mapped to the same region $S$ in the output space. In particular, this can be used to evaluate the probability that a network sampled from the BNN is vulnerable to adversarial attacks. We rely on relaxation techniques from non-convex optimization to develop a method for computing a lower bound on probabilistic safety for BNNs, deriving explicit procedures for the case of interval and linear function propagation techniques. We apply our methods to BNNs trained on a regression task, airborne collision avoidance, and MNIST, empirically showing that our approach allows one to certify probabilistic safety of BNNs with millions of parameters.
Structured Mechanical Models for Robot Learning and Control
Venue: arXiv cs.RO 2020
Year: 2020

Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have been used to learn models of robot dynamics from data, but they suffer from data-inefficiency and the difficulty to incorporate prior knowledge. We introduce Structured Mechanical Models, a flexible model class for mechanical systems that are data-efficient, easily amenable to prior knowledge, and easily usable with model-based control techniques. The goal of this work is to demonstrate the benefits of using Structured Mechanical Models in lieu of black-box neural networks when modeling robot dynamics. We demonstrate that they generalize better from limited data and yield more reliable model-based controllers on a variety of simulated robotic domains.
How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking
Venue: arXiv cs.CV 2020
Year: 2020

We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects. Such a problem poses multiple challenges originating both from the objects' nature and their interaction with their environment, which previous approaches have failed to fully address. The proposed framework encapsulates methods for background clutter and occlusion handling by integrating multiple parallel soft spatial attention modules into a multitask Convolutional Neural Network (CNN) architecture. Moreover, we consider the special geometrical properties of both the object's 3D model and the pose space, and we use a more sophisticated approach for data augmentation during training. The provided experimental results confirm the effectiveness of the proposed multi-attentional architecture, as it improves the State-of-the-Art (SoA) tracking performance by an average score of 34.03% for translation and 40.01% for rotation, when tested on the most complete dataset designed, up to date,for the problem of RGB-D object tracking.
MIDI Passage Retrieval Using Cell Phone Pictures of Sheet Music
Venue: arXiv cs.MM 2020
Year: 2020

This paper investigates a cross-modal retrieval problem in which a user would like to retrieve a passage of music from a MIDI file by taking a cell phone picture of a physical page of sheet music. While audio-sheet music retrieval has been explored by a number of works, this scenario is novel in that the query is a cell phone picture rather than a digital scan. To solve this problem, we introduce a mid-level feature representation called a bootleg score which explicitly encodes the rules of Western musical notation. We convert both the MIDI and the sheet music into bootleg scores using deterministic rules of music and classical computer vision techniques for detecting simple geometric shapes. Once the MIDI and cell phone image have been converted into bootleg scores, we estimate the alignment using dynamic programming. The most notable characteristic of our system is that it does test-time adaptation and has no trainable weights at all -- only a set of about 30 hyperparameters. On a dataset containing 1000 cell phone pictures taken of 100 scores of classical piano music, our system achieves an F measure score of .869 and outperforms baseline systems based on commercial optical music recognition software.
Modeling Network Architecture: A Cloud Case Study
Venue: arXiv cs.SE 2020
Year: 2020

The Internet s ability to support a wide range of services depends on the network architecture and theoretical and practical innovations necessary for future networks. Network architecture in this context refers to the structure of a computer network system as well as interactions among its physical components, their configuration, and communication protocols. Various descriptions of architecture have been developed over the years with an unusually large number of superficial icons and symbols. This situation has created a need for more coherent systematic representations of network architecture. This paper is intended to refine the design, analysis, and documentation of network architecture by adopting a conceptual model called a thinging (abstract) machine (TM), which views all components of a network in terms of a single notion: the flow of things in a TM. Since cloud computing has become increasingly popular in the last few years as a model for a shared pool of networks, servers, storage, and applications, we apply the TM to model a real case study of cloud networks. The resultant model introduces an integrated representation of computer networks.
Optimal Online Algorithms for One-Way Trading and Online Knapsack Problems: A Unified Competitive Analysis
Venue: arXiv cs.DS 2020
Year: 2020

We study two canonical online optimization problems under capacity/budget constraints: the fractional one-way trading problem (OTP) and the integral online knapsack problem (OKP) under an infinitesimal assumption. Under the competitive analysis framework, it is well-known that both problems have the same optimal competitive ratio. However, these two problems are investigated by distinct approaches under separate contexts in the literature. There is a gap in understanding the connection between these two problems and the nature of their online algorithm design. This paper provides a unified framework for the online algorithm design, analysis and optimality proof for both problems. We find that the infinitesimal assumption of the OKP is the key that connects the OTP in the analysis of online algorithms and the construction of worst-case instances. With this unified understanding, our framework shows its potential for analyzing other extensions of OKP and OTP in a more systematic manner.
Breaking Down Memory Walls: Adaptive Memory Management in LSM-based Storage Systems (Extended Version)
Venue: arXiv cs.DB 2020
Year: 2020

Log-Structured Merge-trees (LSM-trees) have been widely used in modern NoSQL systems. Due to their out-of-place update design, LSM-trees have introduced memory walls among the memory components of multiple LSM-trees and between the write memory and the buffer cache. Optimal memory allocation among these regions is non-trivial because it is highly workload-dependent. Existing LSM-tree implementations instead adopt static memory allocation schemes due to their simplicity and robustness, sacrificing performance. In this paper, we attempt to break down these memory walls in LSM-based storage systems. We first present a memory management architecture that enables adaptive memory management. We then present a partitioned memory component structure with new flush policies to better exploit the write memory to minimize the write cost. To break down the memory wall between the write memory and the buffer cache, we further introduce a memory tuner that tunes the memory allocation between these two regions. We have conducted extensive experiments in the context of Apache AsterixDB using the YCSB and TPC-C benchmarks and we present the results here.
Exploring Trade-offs in Dynamic Task Triggering for Loosely Coupled Scientific Workflows
Venue: arXiv cs.DC 2020
Year: 2020

In order to achieve near-time insights, scientific workflows tend to be organized in a flexible and dynamic way. Data-driven triggering of tasks has been explored as a way to support workflows that evolve based on the data. However, the overhead introduced by such dynamic triggering of tasks is an under-studied topic. This paper discusses different facets of dynamic task triggers. Particularly, we explore different ways of constructing a data-driven dynamic workflow and then evaluate the overheads introduced by such design decisions. We evaluate workflows with varying data size, percentage of interesting data, temporal data distribution, and number of tasks triggered. Finally, we provide advice based upon analysis of the evaluation results for users looking to construct data-driven scientific workflows.
A Data-driven Approach for Constructing Multilayer Network-based Service Ecosystem Models
Venue: arXiv cs.CY 2020
Year: 2020

Services are flourishing drastically both on the Internet and in the real world. Additionally, services have become much more interconnected to facilitate transboundary business collaboration to create and deliver distinct new values to customers. Various service ecosystems have become a focus in both research and practice. However, due to the lack of widely recognized service ecosystem models and sufficient data for constructing such models, existing studies on service ecosystems are limited to very narrow scope and cannot effectively guide the design, optimization, and evolution of service ecosystems. We propose a Multilayer network-based Service Ecosystem Model, which covers a variety of service-related elements, including stakeholders, channels, functional and nonfunctional features, and domains, and especially, structural and evolutionary relations between them. "Events" are introduced to describe the triggers of service ecosystem evolution. We propose a data-driven approach for constructing MSEM from public media news and external data sources. Qualitative comparison with state-of-the-art models shows that MSEM has a higher coverage degree of fine-grained elements/relations in service ecosystems and richer semantics for higher interpretability. Experiments conducted on real news corpora show that compared with other approaches, our approach can construct large-scale models for real-world service ecosystems with lower cost and higher efficiency.
Security in Energy Harvesting Networks: A Survey of Current Solutions and Research Challenges
Venue: arXiv cs.CR 2020
Year: 2020

The recent advancements in hardware miniaturization capabilities have boosted the diffusion of systems based on Energy Harvesting (EH) technologies, as a means to power embedded wireless devices in a sustainable and low-cost fashion. Despite the undeniable management advantages, the intermittent availability of the energy source and the limited power supply has led to challenging system trade-offs, resulting in an increased attack surface and a general relaxation of the available security services. In this paper, we survey the security issues, applications, techniques, and challenges arising in wireless networks powered via EH technologies. We explore the vulnerabilities of EH networks, and we provide a comprehensive overview of the scientific literature, including attack vectors, cryptography techniques, physical-layer security schemes for data secrecy, and additional physical-layer countermeasures. For each of the identified macro-areas, we compare the scientific contributions across a range of shared features, indicating the pros and cons of the described techniques, the research challenges, and a few future directions. Finally, we also provide an overview of the emerging topics in the area, such as Non-Orthogonal Multiple Access (NOMA) and Rate-Splitting Multiple Access (RSMA) schemes, and Intelligent Reconfigurable Surfaces, that could trigger the interest of industry and academia and unleash the full potential of pervasive EH wireless networks.
Sequential Anomaly Detection using Inverse Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

One of the most interesting application scenarios in anomaly detection is when sequential data are targeted. For example, in a safety-critical environment, it is crucial to have an automatic detection system to screen the streaming data gathered by monitoring sensors and to report abnormal observations if detected in real-time. Oftentimes, stakes are much higher when these potential anomalies are intentional or goal-oriented. We propose an end-to-end framework for sequential anomaly detection using inverse reinforcement learning (IRL), whose objective is to determine the decision-making agent's underlying function which triggers his/her behavior. The proposed method takes the sequence of actions of a target agent (and possibly other meta information) as input. The agent's normal behavior is then understood by the reward function which is inferred via IRL. We use a neural network to represent a reward function. Using a learned reward function, we evaluate whether a new observation from the target agent follows a normal pattern. In order to construct a reliable anomaly detection method and take into consideration the confidence of the predicted anomaly score, we adopt a Bayesian approach for IRL. The empirical study on publicly available real-world data shows that our proposed method is effective in identifying anomalies.
Efficient Trace Encodings of Bounded Synthesis for Asynchronous Distributed Systems
Venue: arXiv cs.LO 2020
Year: 2020

The manual implementation of distributed systems is an error-prone task because of the asynchronous interplay of components and the environment. Bounded synthesis automatically generates an implementation for the specification of the distributed system if one exists. So far, bounded synthesis for distributed systems does not utilize their asynchronous nature. Instead, concurrent behavior of components is encoded by all interleavings and only then checked against the specification. We close this gap by identifying true concurrency in synthesis of asynchronous distributed systems represented as Petri games. This defines when several interleavings can be subsumed by one true concurrent trace. Thereby, fewer and shorter verification problems have to be solved in each iteration of the bounded synthesis algorithm. For Petri games, experimental results show that our implementation using true concurrency outperforms the implementation based on checking all interleavings.
Applying Normalization Process Theory to Explain Large-Scale Agile Transformations
Venue: arXiv cs.SE 2020
Year: 2020

Given the prevalence and effectiveness of agile methods at a team level, large organizations are now attempting to mimic this success at large scale by adopting large-scale methods such as Scaled Agile Framework (SAFe), Spotify, and Large Scale Scrum (LeSS). However, compared to insights on traditionally small scale methods, the extant literature provides sparse coverage on theories to examine large-scale agile transformations. In this article, we focus on the challenge of normalizing large scale agile transformations and apply Normalization Process Theory (NPT) to support theorize about this process. We present our initial case study findings and outline future research on the application of NPT for large-scale transformations. From a research and practice perspective, we explain how NPT can be adopted to focus on the processes of embedding and sustaining practices, activities which are very often ignored, yet central to the success or failure of transformations.
ApproxFPGAs: Embracing ASIC-Based Approximate Arithmetic Components for FPGA-Based Systems
Venue: arXiv cs.AR 2020
Year: 2020

There has been abundant research on the development of Approximate Circuits (ACs) for ASICs. However, previous studies have illustrated that ASIC-based ACs offer asymmetrical gains in FPGA-based accelerators. Therefore, an AC that might be pareto-optimal for ASICs might not be pareto-optimal for FPGAs. In this work, we present the ApproxFPGAs methodology that uses machine learning models to reduce the exploration time for analyzing the state-of-the-art ASIC-based ACs to determine the set of pareto-optimal FPGA-based ACs. We also perform a case-study to illustrate the benefits obtained by deploying these pareto-optimal FPGA-based ACs in a state-of-the-art automation framework to systematically generate pareto-optimal approximate accelerators that can be deployed in FPGA-based systems to achieve high performance or low-power consumption.
A Non-Ideal NOMA-based mmWave D2D Networks with Hardware and CSI Imperfections
Venue: arXiv cs.IT 2020
Year: 2020

This letter investigates a non-orthogonal multiple access (NOMA) assisted millimeter-wave device-to-device (D2D) network practically limited by multiple interference noises, transceiver hardware impairments, imperfect successive interference cancellation, and channel state information mismatch. Generalized outage probability expressions for NOMA-D2D users are deduced and achieved results, validated by Monte Carlo simulations, are compared with the orthogonal multiple access to show the superior performance of the proposed network model