OpenRaiser/Intern-Atlas huggingface dataset:
One-Shot Weakly Supervised Video Object Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks. Recent work has shown to perform on par with weaker levels of supervision in terms of scribbles and bounding boxes. However, there has been limited attention given to the problem of few-shot object segmentation with image-level supervision. We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embeddings. It enables our model to achieve 5.1% improvement over previously proposed image-level few-shot object segmentation. Our method compares relatively close to the state of the art methods that use strong supervision, while ours use the least possible supervision. We further propose a novel setup for few-shot weakly supervised video object segmentation(VOS) that relies on image-level labels for the first frame. The proposed setup uses weak annotation unlike semi-supervised VOS setting that utilizes strongly labelled segmentation masks. The setup evaluates the effectiveness of generalizing to novel classes in the VOS setting. The setup splits the VOS data into multiple folds with different categories per fold. It provides a potential setup to evaluate how few-shot object segmentation methods can benefit from additional object poses, or object interactions that is not available in static frames as in PASCAL-5i benchmark.
A Dimension Reduction-Based Joint Activity Detection and Channel Estimation Algorithm for Massive Access
Venue: arXiv cs.IT 2019
Year: 2019

Grant-free random access is a promising protocol to support massive access in beyond fifth-generation (B5G) cellular Internet-of-Things (IoT) with sporadic traffic. Specifically, in each coherence interval, the base station (BS) performs joint activity detection and channel estimation (JADCE) before data transmission. Due to the deployment of a large-scale antennas array and the existence of a huge number of IoT devices, JADCE usually has high computational complexity and needs long pilot sequences. To solve these challenges, this paper proposes a dimension reduction method, which projects the original device state matrix to a low-dimensional space by exploiting its sparse and low-rank structure. Then, we develop an optimized design framework with a coupled full column rank constraint for JADCE to reduce the size of the search space. However, the resulting problem is non-convex and highly intractable, for which the conventional convex relaxation approaches are inapplicable. To this end, we propose a logarithmic smoothing method for the non-smoothed objective function and transform the interested matrix to a positive semidefinite matrix, followed by giving a Riemannian trust-region algorithm to solve the problem in complex field. Simulation results show that the proposed algorithm is efficient to a large-scale JADCE problem and requires shorter pilot sequences than the state-of-art algorithms which only exploit the sparsity of device state matrix.
Edit Based Grading of SQL Queries
Venue: arXiv cs.DB 2019
Year: 2019

Grading student SQL queries manually is a tedious and error-prone process. Earlier work on testing correctness of student SQL queries, such as the XData system, can be used to test correctness of a student query. However, in case a student query is found to be incorrect there is currently no way to automatically assign partial marks. Partial marking is important so that small errors are penalized less than large errors. Manually awarding partial marks is not scalable for classes with large number of students, especially MOOCs, and is also prone to human errors. In this paper, we discuss techniques to find a minimum cost set of edits to a student query that would make it correct, which can help assign partial marks, and to help students understand exactly where they went wrong. Given the limitations of current formal methods for checking equivalence, our approach is based on finding nearest query, from a set of instructor provided correct queries, that is found to be equivalent based on query canonicalization. We show that exhaustive techniques are expensive, and propose a greedy heuristic approach that works well both in terms of runtime and accuracy on queries in real-world datasets. Our system can also be used in a learning mode where query edits can be suggested as feedback to students to guide them towards a correct query. Our partial marking system has been successfully used in courses at IIT Bombay and IIT Dharwad.
Energy Minimization in DAG Scheduling on MPSoCs at Run-Time: Theory and Practice
Venue: arXiv cs.DS 2019
Year: 2019

Static (offline) techniques for mapping applications given by task graphs to MPSoC systems often deliver overly pessimistic and thus suboptimal results w.r.t. exploiting time slack in order to minimize the energy consumption. This holds true in particular in case computation times of tasks may be workload-dependent and becoming known only at runtime or in case of conditionally executed tasks or scenarios. This paper studies and quantitatively evaluates different classes of algorithms for scheduling periodic applications given by task graphs (i.e., DAGs) with precedence constraints and a global deadline on homogeneous MPSoCs purely at runtime on a per-instance base. We present and analyze algorithms providing provably optimal results as well as approximation algorithms with proven guarantees on the achieved energy savings. For problem instances taken from realistic embedded system benchmarks as well as synthetic scalable problems, we provide results on the computation time and quality of each algorithm to perform a) scheduling and b) voltage/speed assignments for each task at runtime. In our portfolio, we distinguish as well continuous and discrete speed (e.g., DVFS-related) assignment problems. In summary, the presented ties between theory (algorithmic complexity and optimality) and execution time analysis deliver important insights on the practical usability of the presented algorithms for runtime optimization of task scheduling and speed assignment on MPSoCs.
Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)
Venue: arXiv cs.LG 2019
Year: 2019

Quantization of weights of deep neural networks (DNN) has proven to be an effective solution for the purpose of implementing DNNs on edge devices such as mobiles, ASICs and FPGAs, because they have no sufficient resources to support computation involving millions of high precision weights and multiply-accumulate operations. This paper proposes a novel method to compress vectors of high precision weights of DNNs to ternary vectors, namely a cosine similarity based target non-retraining ternary (TNT) compression method. Our method leverages cosine similarity instead of Euclidean distances as commonly used in the literature and succeeds in reducing the size of the search space to find optimal ternary vectors from 3N to N, where N is the dimension of target vectors. As a result, the computational complexity for TNT to find theoretically optimal ternary vectors is only O(N log(N)). Moreover, our experiments show that, when we ternarize models of DNN with high precision parameters, the obtained quantized models can exhibit sufficiently high accuracy so that re-training models is not necessary.
Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications
Venue: arXiv cs.NI 2019
Year: 2019

Device-to-device (D2D) communication underlay cellular networks is a promising technique to improve spectrum efficiency. In this situation, D2D transmission may cause severe interference to both the cellular and other D2D links, which imposes a great technical challenge to spectrum allocation. Existing centralized schemes require global information, which causes a large signaling overhead. While existing distributed schemes requires frequent information exchange among D2D users and cannot achieve global optimization. In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named multi-agent actor critic (MAAC). MAAC shares global historical states, actions and policies during centralized training, requires no signal interaction during execution and utilizes cooperation among users to further optimize system performance. Moreover, in order to decrease the computing complexity of the training, we further propose the neighbor-agent actor critic (NAAC) based on the neighbor users' historical information for centralized training. The simulation results show that the proposed MAAC and NAAC can effectively reduce the outage probability of cellular links, greatly improve the sum rate of D2D links and converge quickly.
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning
Venue: arXiv cs.DC 2019
Year: 2019

This report considers the problem of Byzantine fault-tolerance in synchronous parallelized learning that is founded on the parallelized stochastic gradient descent (parallelized-SGD) algorithm. The system comprises a master, and $n$ workers, where up to $f$ of the workers are Byzantine faulty. Byzantine workers need not follow the master's instructions correctly, and might send malicious incorrect (or faulty) information. The identity of the Byzantine workers remains fixed throughout the learning process, and is unknown a priori to the master. We propose two coding schemes, a deterministic scheme and a randomized scheme, for guaranteeing exact fault-tolerance if $2f < n$. The coding schemes use the concept of reactive redundancy for isolating Byzantine workers that eventually send faulty information. We note that the computation efficiencies of the schemes compare favorably with other (deterministic or randomized) coding schemes, for exact fault-tolerance.
RIMAX: Ranking Semantic Rhymes by calculating Definition Similarity
Venue: arXiv cs.CL 2019
Year: 2019

This paper presents RIMAX, a new system for detecting semantic rhymes, using a Comprehensive Mexican Spanish Dictionary (DEM) and its Rhyming Dictionary (REM). We use the Vector Space Model to calculate the similarity of the definition of a query with the definitions corresponding to the assonant and consonant rhymes of the query. The preliminary results using a manual evaluation are very encouraging.
High Resolution Millimeter Wave Imaging For Self-Driving Cars
Venue: arXiv cs.CV 2019
Year: 2019

Recent years have witnessed much interest in expanding the use of networking signals beyond communication to sensing, localization, robotics, and autonomous systems. This paper explores how we can leverage recent advances in 5G millimeter wave (mmWave) technology for imaging in self-driving cars. Specifically, the use of mmWave in 5G has led to the creation of compact phased arrays with hundreds of antenna elements that can be electronically steered. Such phased arrays can expand the use of mmWave beyond vehicular communications and simple ranging sensors to a full-fledged imaging system that enables self-driving cars to see through fog, smog, snow, etc. Unfortunately, using mmWave signals for imaging in self-driving cars is challenging due to the very low resolution, the presence of fake artifacts resulting from multipath reflections and the absence of portions of the car due to specularity. This paper presents HawkEye, a system that can enable high resolution mmWave imaging in self driving cars. HawkEye addresses the above challenges by leveraging recent advances in deep learning known as Generative Adversarial Networks (GANs). HawkEye introduces a GAN architecture that is customized to mmWave imaging and builds a system that can significantly enhance the quality of mmWave images for self-driving cars.
MVF Designer: Design and Visualization of Morse Vector Fields
Venue: arXiv cs.GR 2019
Year: 2019

Vector field design on surfaces was originally motivated by applications in graphics such as texture synthesis and rendering. In this paper, we consider the idea of vector field design with a new motivation from computational topology. We are interested in designing and visualizing vector fields to aid the study of Morse functions, Morse vector fields, and Morse-Smale complexes. To achieve such a goal, we present MVF Designer, a new interactive design system that provides fine-grained control over vector field geometry, enables the editing of vector field topology, and supports a design process in a simple and efficient way using elementary moves, which are actions that initiate or advance our design process. Our system allows mathematicians to explore the complex configuration spaces of Morse functions, their gradients, and their associated Morse-Smale complexes. Understanding these spaces will help us expand further their applicability in topological data analysis and visualization.
Analysis of Graph Transformation Systems: Native vs Translation-based Techniques
Venue: arXiv cs.SE 2019
Year: 2019

The paper summarises the contributions in a session at GCM 2019 presenting and discussing the use of native and translation-based solutions to common analysis problems for Graph Transformation Systems (GTSs). In addition to a comparison of native and translation-based techniques in this area, we explore design choices for the latter, s.a. choice of logic and encoding method, which have a considerable impact on the overall quality and complexity of the analysis. We substantiate our arguments by citing literature on application of theorem provers, model checkers, and SAT/SMT solver in GTSs, and conclude with a general discussion from a software engineering perspective, including comments from the workshop participants, and recommendations on how to investigate important design choices in the future.
Rule-based Graph Repair
Venue: arXiv cs.SE 2019
Year: 2019

Model repair is an essential topic in model-driven engineering. Since models are suitably formalized as graph-like structures, we consider the problem of rule-based graph repair: Given a rule set and a graph constraint, try to construct a graph program based on the given set of rules, such that the application to any graph yields a graph satisfying the graph constraint. We show the existence of repair programs for specific constraints, and show the existence of rule-based repair programs for specific constraints compatible with the rule set.
Spatio-Temporal Correlation of Interference in MANET Under Spatially Correlated Shadowing Environment
Venue: arXiv cs.NI 2019
Year: 2019

Correlation of interference affects spatio-temporal aspects of various wireless mobile systems, such as retransmission, multiple antennas and cooperative relaying. In this paper, we study the spatial and temporal correlation of interference in mobile ad-hoc networks under a correlated shadowing environment. By modeling the node locations as a Poisson point process with an i.i.d. mobility model and considering Gudmundson (1991)' s spatially correlated shadowing model, we theoretically analyze the relationship between the correlation distance of log-normal shadowing and the spatial and temporal correlation coefficients of interference. Since the exact expressions of the correlation coefficients are intractable, we obtain their simple asymptotic expressions as the variance of log-normal shadowing increases. We found in our numerical examples that the asymptotic expansions can be used as tight approximate formulas and useful for modeling general wireless systems under spatially correlated shadowing.
Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree Videos
Venue: arXiv cs.CV 2019
Year: 2019

The 360-degree video allows users to enjoy the whole scene by interactively switching viewports. However, the huge data volume of the 360-degree video limits its remote applications via network. To provide high quality of experience (QoE) for remote web users, this paper presents a tile-based adaptive streaming method for 360-degree videos. First, we propose a simple yet effective rate adaptation algorithm to determine the requested bitrate for downloading the current video segment by considering the balance between the buffer length and video quality. Then, we propose to use a Gaussian model to predict the field of view at the beginning of each requested video segment. To deal with the circumstance that the view angle is switched during the display of a video segment, we propose to download all the tiles in the 360-degree video with different priorities based on a Zipf model. Finally, in order to allocate bitrates for all the tiles, a two-stage optimization algorithm is proposed to preserve the quality of tiles in FoV and guarantee the spatial and temporal smoothness. Experimental results demonstrate the effectiveness and advantage of the proposed method compared with the state-of-the-art methods. That is, our method preserves both the quality and the smoothness of tiles in FoV, thus providing the best QoE for users.
Performance and Cost Evaluation of Smart Contracts in Collaborative Health Care Environments
Venue: arXiv cs.CR 2019
Year: 2019

Blockchain emerged as a solution for data integrity, non-repudiation, and availability in different applications. Data sensitive scenarios, such as Health Care, can also benefit from these blockchain properties. Consequently, different research proposed the adoption of blockchain in Health Care applications. However, few are discussed about incentive methods to attract new users, as well as to motivate the system or application usage by existing end-users. Also, little is discussed about performance during code execution in blockchains. In order to tackle these issues, this work presents the preliminary evaluation of TokenHealth, an application for collaborative health practice monitoring with gamification and token-based incentives. The proposed solution is implemented through smart contracts using Solidity in the Ethereum blockchain. We evaluated the performance of both in Ropsten test network and in a Private instance. The preliminary results show that the execution of smart contracts takes less than a minute for a full cycle of different smart contracts. Also, we present a discussion about costs for using a Private instance and the public Ethereum main network.
Axiomatizing rectangular grids with no extra non-unary relations
Venue: arXiv cs.LO 2019
Year: 2019

We construct a formula $ϕ$ which axiomatizes non-narrow rectangular grids without using any binary relations other than the grid neighborship relations. As a corollary, we prove that a set $A \subseteq \mathbb{N}$ is a spectrum of a formula which has only planar models if numbers $n \in A$ can be recognized by a non-deterministic Turing machine (or a one-dimensional cellular automaton) in time $t(n)$ and space $s(n)$, where $t(n)s(n) \leq n$ and $t(n),s(n) = Ω(\log(n))$.
Morphy: A Datamorphic Software Test Automation Tool
Venue: arXiv cs.SE 2019
Year: 2019

This paper presents an automated tool called Morphy for datamorphic testing. It classifies software test artefacts into test entities and test morphisms, which are mappings on testing entities. In addition to datamorphisms, metamorphisms and seed test case makers, Morphy also employs a set of other test morphisms including test case metrics and filters, test set metrics and filters, test result analysers and test executers to realise test automation. In particular, basic testing activities can be automated by invoking test morphisms. Test strategies can be realised as complex combinations of test morphisms. Test processes can be automated by recording, editing and playing test scripts that invoke test morphisms and strategies. Three types of test strategies have been implemented in Morphy: datamorphism combination strategies, cluster border exploration strategies and strategies for test set optimisation via genetic algorithms. This paper focuses on the datamorphism combination strategies by giving their definitions and implementation algorithms. The paper also illustrates their uses for testing both traditional software and AI applications with three case studies.
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
Venue: arXiv cs.LG 2019
Year: 2019

It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top-$k$ predictions are more relevant. In this work, we aim to derive certified robustness for top-$k$ predictions. In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example. We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier. We derive a tight robustness in $\ell_2$ norm for top-$k$ predictions when using randomized smoothing with Gaussian noise. We find that generalizing the certified robustness from top-1 to top-$k$ predictions faces significant technical challenges. We also empirically evaluate our method on CIFAR10 and ImageNet. For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8\% when the $\ell_2$-norms of the adversarial perturbations are less than 0.5 (=127/255). Our code is publicly available at: \url{https://github.com/jjy1994/Certify_Topk}.
Report on the First HIPstIR Workshop on the Future of Information Retrieval
Venue: arXiv cs.IR 2019
Year: 2019

The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first iteration of this vision materialized in the form of a three day workshop in Portsmouth, New Hampshire attended by 24 researchers across academia and industry. Attendees pre-submitted one or more topics that they want to pitch at the meeting. Then over the three days during the workshop, we self-organized into groups and worked on six specific proposals of common interest. In this report, we present an overview of the workshop and brief summaries of the six proposals that resulted from the workshop.