OpenRaiser/Intern-Atlas huggingface dataset:
EgoReID Dataset: Person Re-identification in Videos Acquired by Mobile Devices with First-Person Point-of-View
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, we have seen the performance of video-based person Re-Identification (ReID) methods have improved considerably. However, most of the work in this area has dealt with videos acquired by fixed cameras with wider field of view. Recently, widespread use of wearable cameras and recording devices such as cellphones have opened the door to interesting research in first-person Point-of-view (POV) videos (egocentric videos). Nonetheless, analysis of such videos is challenging due to factors such as poor video quality due to ego-motion, blurriness, severe changes in lighting conditions and perspective distortions. To facilitate the research towards conquering these challenges, this paper contributes a new dataset called EgoReID. The dataset is captured using 3 mobile cellphones with non-overlapping field-of-view. It contains 900 IDs and around 10,200 tracks with a total of 176,000 detections. The dataset also contains 12-sensor meta data e.g. camera orientation pitch and rotation for each video. In addition, we propose a new framework which takes advantage of both visual and sensor meta data to successfully perform Person ReID. We extend image-based re-ID method employing human body parsing trained on ten datasets to video-based re-ID. In our method, first frame level local features are extracted for each semantic region, then 3D convolutions are applied to encode the temporal information in each sequence of semantic regions. Additionally, we employ sensor meta data to predict targets' next camera and their estimated time of arrival, which considerably improves our ReID performance as it significantly reduces our search space.
Meta Architecture Search
Venue: arXiv cs.LG 2019
Year: 2019

Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks. Unfortunately, the computational cost can make it difficult to scale. In this paper, we make the first attempt to study Meta Architecture Search which aims at learning a task-agnostic representation that can be used to speed up the process of architecture search on a large number of tasks. We propose the Bayesian Meta Architecture SEarch (BASE) framework which takes advantage of a Bayesian formulation of the architecture search problem to learn over an entire set of tasks simultaneously. We show that on Imagenet classification, we can find a model that achieves 25.7% top-1 error and 8.1% top-5 error by adapting the architecture in less than an hour from an 8 GPU days pretrained meta-network. By learning a good prior for NAS, our method dramatically decreases the required computation cost while achieving comparable performance to current state-of-the-art methods - even finding competitive models for unseen datasets with very quick adaptation. We believe our framework will open up new possibilities for efficient and massively scalable architecture search research across multiple tasks.
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering
Venue: arXiv cs.MM 2019
Year: 2019

One of the key issues of Visual Question Answering (VQA) is to reason with semantic clues in the visual content under the guidance of the question, how to model relational semantics still remains as a great challenge. To fully capture visual semantics, we propose to reason over a structured visual representation - scene graph, with embedded objects and inter-object relationships. This shows great benefit over vanilla vector representations and implicit visual relationship learning. Based on existing visual relationship models, we propose a visual relationship encoder that projects visual relationships into a learned deep semantic space constrained by visual context and language priors. Upon the constructed graph, we propose a Scene Graph Convolutional Network (SceneGCN) to jointly reason the object properties and relational semantics for the correct answer. We demonstrate the model's effectiveness and interpretability on the challenging GQA dataset and the classical VQA 2.0 dataset, remarkably achieving state-of-the-art 54.56% accuracy on GQA compared to the existing best model.
Optimizing Answer Set Computation via Heuristic-Based Decomposition
Venue: arXiv cs.AI 2019
Year: 2019

Answer Set Programming (ASP) is a purely declarative formalism developed in the field of logic programming and nonmonotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem; however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones; the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP system DLV, in particular into its grounding subsystem I-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal. Under consideration in Theory and Practice of Logic Programming (TPLP).
bigMap: Big Data Mapping with Parallelized t-SNE
Venue: arXiv cs.LG 2019
Year: 2019

We introduce an improved unsupervised clustering protocol specially suited for large-scale structured data. The protocol follows three steps: a dimensionality reduction of the data, a density estimation over the low dimensional representation of the data, and a final segmentation of the density landscape. For the dimensionality reduction step we introduce a parallelized implementation of the well-known t-Stochastic Neighbouring Embedding (t-SNE) algorithm that significantly alleviates some inherent limitations, while improving its suitability for large datasets. We also introduce a new adaptive Kernel Density Estimation particularly coupled with the t-SNE framework in order to get accurate density estimates out of the embedded data, and a variant of the rainfalling watershed algorithm to identify clusters within the density landscape. The whole mapping protocol is wrapped in the bigMap R package, together with visualization and analysis tools to ease the qualitative and quantitative assessment of the clustering.
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
Venue: arXiv cs.CV 2019
Year: 2019

During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is one of the core tasks in many applications such as autonomous driving and augmented reality. However, to train CNNs requires a considerable amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNNs on photo-realistic synthetic imagery with computer-generated annotations. Despite this, the domain mismatch between the real images and the synthetic data hinders the models' performance. Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation. The curriculum domain adaptation solves easy tasks first to infer necessary properties about the target domain; in particular, the first task is to learn global label distributions over images and local distributions over landmark superpixels. These are easy to estimate because images of urban scenes have strong idiosyncrasies (e.g., the size and spatial relations of buildings, streets, cars, etc.). We then train a segmentation network, while regularizing its predictions in the target domain to follow those inferred properties. In experiments, our method outperforms the baselines on two datasets and two backbone networks. We also report extensive ablation studies about our approach.
High Order Disturbance Rejection and Tracking via Delayed Feedback Control Method
Venue: arXiv cs.SY 2019
Year: 2019

Delayed feedback control is an easy realizable control method which generates control force by comparing the current and the delayed version of the system states. In this paper, a new form of the delayed feedback structure is introduced. Based on the proposed delayed feedback method, a new robust tracking system is designed. This tracking system improves the features of the conventional state feedback with integral action and it is also able to reject higher order disturbances compared to the conventional method. In addition, the proposed tracking system tracks the ramp-shape reference input signal as well, which this is not possible through the conventional state feedback. Due to easy implementable feature of the proposed delayed feedback tracking system, it can be used in practical applications effectively. Moreover, since the proposed method adds delays to the closed loop system dynamics, the ordinary differential equation of the system changes to a delay differential equation with an infinite number of characteristic roots. Thus, conventional pole placement procedures cannot be used to design the delayed feedback controller parameters and place the unstable roots in the left half plane. In this paper, the simulated annealing algorithm is used to determine the proposed control system parameters and move the unstable roots of the delay differential equation to the left half plane. Finally, the efficiency of the proposed reference input tracker is demonstrated on a case study.
Format-aware Learn&Fuzz: Deep Test Data Generation for Efficient Fuzzing
Venue: arXiv cs.SE 2019
Year: 2019

Appropriate test data is a crucial factor to reach success in dynamic software testing, e.g., fuzzing. Most of the real-world applications, however, accept complex structure inputs containing data surrounded by meta-data which is processed in several stages comprising of the parsing and rendering (execution). It makes the automatically generating efficient test data, to be non-trivial and laborious activity. The success of deep learning to cope in solving complex tasks especially in generative tasks has motivated us to exploit it in the context of complex test data generation. To do so, a neural language model (NLM) based on deep recurrent neural networks (RNNs) is used to learn the structure of complex input. Our approach generates new test data while distinguishes between data and meta-data that makes it possible to target both the parsing and rendering parts of software under test (SUT). Such test data can improve, input fuzzing. To assess the proposed approach, we developed a modular file format fuzzer, IUST-DeepFuzz. Our conducted experiments on the MuPDF, a lightweight and favorite portable document format (PDF) reader, reveal that IUST-DeepFuzz reaches high coverage of SUT in comparison with the state-of-the-art tools such as learn&fuzz, AFL, Augmented-AFL and random fuzzing. We also observed that the simpler deep learning models, the higher code coverage.
PBMap: A Path Balancing Technology Mapping Algorithm for Single Flux Quantum Logic Circuits
Venue: arXiv cs.ET 2019
Year: 2019

This paper presents a path balancing technology mapping algorithm, which is a new algorithm for generating a mapping solution for a given Boolean network such that the average logic level difference among fanin gates of each gate in the network is minimized. Path balancing technology mapping is required in dc-biased Single Flux Quantum (SFQ) circuits for guaranteeing the correct operation, and it is beneficial in CMOS circuits to reduce the hazard issues. We present a dynamic programming based algorithm for path balancing technology mapping which generates optimal solutions for dc-biased SFQ (e.g. Rapid SFQ or RSFQ) circuits with tree structure and acts as an effective heuristic for circuits with general Directed Acyclic Graph (DAG) structure. Experimental results show that our path balancing technology mapper reduces the balancing overhead by up to 2.7 times and with an average of 21% compared to the state-of-the-art academic technology mappers.
Induction, Coinduction, and Fixed Points: A Concise Comparative Survey
Venue: arXiv cs.LO 2019
Year: 2019

In this survey article (which hitherto is an ongoing work-in-progress) we present the formulation of the induction and coinduction principles using the language and conventions of each of order theory, set theory, programming languages' type theory, first-order logic, and category theory, for the purpose of examining some of the similarities and, more significantly, the dissimilarities between these various mathematical disciplines, and hence shed some light on the precise relation between these disciplines. Towards that end, in this article we discuss plenty of related concepts, such as fixed points, pre-fixed points, post-fixed points, inductive sets and types, coinductive sets and types, algebras and coalgebras. We conclude the survey by hinting at the possibility of a more abstract and unified treatment that uses concepts from category theory such as monads and comonads.
Correction to Byzantine Agreement in Expected Polynomial Time, JACM 2016
Venue: arXiv cs.DC 2019
Year: 2019

This is a correction by the authors to "Byzantine Agreement in Expected Polynomial Time" which appeared in the Journal of the ACM in 2016. It corrects a failure in the paper to consider the adversary's ability to decide the number of fair coinflips in an iteration, where this number ranges between n(n-t) and n(n-2t).
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors. Particularly, for Hiding Attack (HA), we proposed the feature-interference reinforcement (FIR) method and the enhanced realistic constraints generation (ERG) to enhance robustness, and for Appearing Attack (AA), we proposed the nested-AE, which combines two AEs together to attack object detectors in both long and short distance. We also designed diverse styles of AEs to make AA more surreptitious. Evaluation results show that our AEs can attack the state-of-the-art real-time object detectors (i.e., YOLO V3 and faster-RCNN) at the success rate up to 92.4% with varying distance from 1m to 25m and angles from -60° to 60°. Our AEs are also demonstrated to be highly transferable, capable of attacking another three state-of-the-art black-box models with high success rate.
Extraction of Behavioral Features from Smartphone and Wearable Data
Venue: arXiv cs.CY 2019
Year: 2019

The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild. The raw data streams, however, can rarely be directly used in the modeling pipeline. We provide a generic framework that can process raw data streams and extract useful features related to non-verbal human behavior. This framework can be used by researchers in the field who are interested in processing data from smartphones and Wearable devices.
Linearized Gaussian Processes for Fast Data-driven Model Predictive Control
Venue: arXiv cs.SY 2019
Year: 2019

Data-driven Model Predictive Control (MPC), where the system model is learned from data with machine learning, has recently gained increasing interests in the control community. Gaussian Processes (GP), as a type of statistical models, are particularly attractive due to their modeling flexibility and their ability to provide probabilistic estimates of prediction uncertainty. GP-based MPC has been developed and applied, however the optimization problem is typically non-convex and highly demanding, and scales poorly with model size. This causes unsatisfactory solving performance, even with state-of-the-art solvers, and makes the approach less suitable for real-time control. We develop a method based on a new concept, called linearized Gaussian Process, and Sequential Convex Programming, that can significantly improve the solving performance of GP-based MPC. Our method is not only faster but also much more scalable and predictable than other commonly used methods, as it is much less influenced by the model size. The efficiency and advantages of the algorithm are demonstrated clearly in a numerical example.
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

We show that it is possible to learn semantic segmentation from very limited amounts of manual annotations, by enforcing geometric 3D constraints between multiple views. More exactly, image locations corresponding to the same physical 3D point should all have the same label. We show that introducing such constraints during learning is very effective, even when no manual label is available for a 3D point, and can be done simply by employing techniques from 'general' semi-supervised learning to the context of semantic segmentation. To demonstrate this idea, we use RGB-D image sequences of rigid scenes, for a 4-class segmentation problem derived from the ScanNet dataset. Starting from RGB-D sequences with a few annotated frames, we show that we can incorporate RGB-D sequences without any manual annotations to improve the performance, which makes our approach very convenient. Furthermore, we demonstrate our approach for semantic segmentation of objects on the LabelFusion dataset, where we show that one manually labeled image in a scene is sufficient for high performance on the whole scene.
CASPR: Judiciously Using the Cloud for Wide-Area Packet Recovery
Venue: arXiv cs.NI 2019
Year: 2019

We revisit a classic networking problem -- how to recover from lost packets in the best-effort Internet. We propose CASPR, a system that judiciously leverages the cloud to recover from lost or delayed packets. CASPR supplements and protects best-effort connections by sending a small number of coded packets along the highly reliable but expensive cloud paths. When receivers detect packet loss, they recover packets with the help of the nearby data center, not the sender, thus providing quick and reliable packet recovery for latency-sensitive applications. Using a prototype implementation and its deployment on the public cloud and the PlanetLab testbed, we quantify the benefits of CASPR in providing fast, cost effective packet recovery. Using controlled experiments, we also explore how these benefits translate into improvements up and down the network stack.
AT2: Asynchronous Trustworthy Transfers
Venue: arXiv cs.DC 2019
Year: 2019

Many blockchain-based protocols, such as Bitcoin, implement a decentralized asset transfer (or exchange) system. As clearly stated in the original paper by Nakamoto, the crux of this problem lies in prohibiting any participant from engaging in double-spending. There seems to be a common belief that consensus is necessary for solving the double-spending problem. Indeed, whether it is for a permissionless or a permissioned environment, the typical solution uses consensus to build a totally ordered ledger of submitted transfers. In this paper we show that this common belief is false: consensus is not needed to implement of a decentralized asset transfer system. We do so by introducing AT2 (Asynchronous Trustworthy Transfers), a class of consensusless algorithms. To show formally that consensus is unnecessary for asset transfers, we consider this problem first in the shared-memory context. We introduce AT2$_{SM}$, a wait-free algorithm that asynchronously implements asset transfer in the read-write shared-memory model. In other words, we show that the consensus number of an asset-transfer object is one. In the message passing model with Byzantine faults, we introduce a generic asynchronous algorithm called AT2$_{MP}$ and discuss two instantiations of this solution. First, AT2$_{D}$ ensures deterministic guarantees and consequently targets a small scale deployment (tens to hundreds of nodes), typically for a permissioned environment. Second, AT2$_{P}$ provides probabilistic guarantees and scales well to a very large system size (tens of thousands of nodes), ensuring logarithmic latency and communication complexity. Instead of consensus, we construct AT2$_{D}$ and AT2$_{P}$ on top of a broadcast primitive with causal ordering guarantees offering deterministic and probabilistic properties, respectively.
Rejection-Based Simulation of Stochastic Spreading Processes on Complex Networks
Venue: arXiv cs.SI 2019
Year: 2019

Stochastic processes can model many emerging phenomena on networks, like the spread of computer viruses, rumors, or infectious diseases. Understanding the dynamics of such stochastic spreading processes is therefore of fundamental interest. In this work we consider the wide-spread compartment model where each node is in one of several states (or compartments). Nodes change their state randomly after an exponentially distributed waiting time and according to a given set of rules. For networks of realistic size, even the generation of only a single stochastic trajectory of a spreading process is computationally very expensive. Here, we propose a novel simulation approach, which combines the advantages of event-based simulation and rejection sampling. Our method outperforms state-of-the-art methods in terms of absolute run-time and scales significantly better, while being statistically equivalent.
Fast Breadth-First Search in Still Less Space
Venue: arXiv cs.DS 2019
Year: 2019

It is shown that a breadth-first search in a directed or undirected graph with $n$ vertices and $m$ edges can be carried out in $O(n+m)$ time with $n\log_2 3+O((\log n)^2)$ bits of working memory.
Image Processing in Quantum Computers
Venue: arXiv cs.CV 2019
Year: 2019

Quantum Image Processing (QIP)is an exciting new field showing a lot of promise as a powerful addition to the arsenal of Image Processing techniques. Representing image pixel by pixel using classical information requires an enormous amount of computational resources. Hence, exploring methods to represent images in a different paradigm of information is important. In this work, we study the representation of images in Quantum Information. The main motivation for this pursuit is the ability of storing N bits of classical information in only log(2N) quantum bits (qubits). The promising first step was the exponentially efficient implementation of the Fourier transform in quantum computers as compared to Fast Fourier Transform in classical computers. In addition, images encoded in quantum information could obey unique quantum properties like superposition or entanglement.
Degree Bounded Bottleneck Spanning Trees in Three Dimensions
Venue: arXiv cs.CG 2019
Year: 2019

The geometric $δ$-minimum spanning tree problem ($δ$-MST) is the problem of finding a minimum spanning tree for a set of points in a normed vector space, such that no vertex in the tree has a degree which exceeds $δ$, and the sum of the lengths of the edges in the tree is minimum. The similarly defined geometric $δ$-minimum bottleneck spanning tree problem ($δ$-MBST), is the problem of finding a degree bounded spanning tree such that the length of the longest edge is minimum. For point sets that lie in the Euclidean plane, both of these problems have been shown to be NP-hard for certain specific values of $δ$. In this paper, we investigate the $δ$-MBST problem in $3$-dimensional Euclidean space and $3$-dimensional rectilinear space. We show that the problems are NP-hard for certain values of $δ$, and we provide inapproximability results for these cases. We also describe new approximation algorithms for solving these $3$-dimensional variants, and then analyse their worst-case performance.