OpenRaiser/Intern-Atlas huggingface dataset:
Estimation of Absolute Scale in Monocular SLAM Using Synthetic Data
Venue: arXiv cs.CV 2019
Year: 2019

This paper addresses the problem of scale estimation in monocular SLAM by estimating absolute distances between camera centers of consecutive image frames. These estimates would improve the overall performance of classical (not deep) SLAM systems and allow metric feature locations to be recovered from a single monocular camera. We propose several network architectures that lead to an improvement of scale estimation accuracy over the state of the art. In addition, we exploit a possibility to train the neural network only with synthetic data derived from a computer graphics simulator. Our key insight is that, using only synthetic training inputs, we can achieve similar scale estimation accuracy as that obtained from real data. This fact indicates that fully annotated simulated data is a viable alternative to existing deep-learning-based SLAM systems trained on real (unlabeled) data. Our experiments with unsupervised domain adaptation also show that the difference in visual appearance between simulated and real data does not affect scale estimation results. Our method operates with low-resolution images (0.03MP), which makes it practical for real-time SLAM applications with a monocular camera.
Know2Look: Commonsense Knowledge for Visual Search
Venue: arXiv cs.IR 2019
Year: 2019

With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval.
Dynamic Approach for Lane Detection using Google Street View and CNN
Venue: arXiv cs.CV 2019
Year: 2019

Lane detection algorithms have been the key enablers for a fully-assistive and autonomous navigation systems. In this paper, a novel and pragmatic approach for lane detection is proposed using a convolutional neural network (CNN) model based on SegNet encoder-decoder architecture. The encoder block renders low-resolution feature maps of the input and the decoder block provides pixel-wise classification from the feature maps. The proposed model has been trained over 2000 image data-set and tested against their corresponding ground-truth provided in the data-set for evaluation. To enable real-time navigation, we extend our model's predictions interfacing it with the existing Google APIs evaluating the metrics of the model tuning the hyper-parameters. The novelty of this approach lies in the integration of existing segNet architecture with google APIs. This interface makes it handy for assistive robotic systems. The observed results show that the proposed method is robust under challenging occlusion conditions due to pre-processing involved and gives superior performance when compared to the existing methods.
Faster Algorithms for Edge Connectivity via Random $2$-Out Contractions
Venue: arXiv cs.DS 2019
Year: 2019

We provide a simple new randomized contraction approach to the global minimum cut problem for simple undirected graphs. The contractions exploit 2-out edge sampling from each vertex rather than the standard uniform edge sampling. We demonstrate the power of our new approach by obtaining better algorithms for sequential, distributed, and parallel models of computation. Our end results include the following randomized algorithms for computing edge connectivity with high probability: -- Two sequential algorithms with complexities $O(m \log n)$ and $O(m+n \log^3 n)$. These improve on a long line of developments including a celebrated $O(m \log^3 n)$ algorithm of Karger [STOC'96] and the state of the art $O(m \log^2 n (\log\log n)^2)$ algorithm of Henzinger et al. [SODA'17]. Moreover, our $O(m+n \log^3 n)$ algorithm is optimal whenever $m = Ω(n \log^3 n)$. Within our new time bounds, whp, we can also construct the cactus representation of all minimal cuts. -- An $Õ(n^{0.8} D^{0.2} + n^{0.9})$ round distributed algorithm, where D denotes the graph diameter. This improves substantially on a recent breakthrough of Daga et al. [STOC'19], which achieved a round complexity of $Õ(n^{1-1/353}D^{1/353} + n^{1-1/706})$, hence providing the first sublinear distributed algorithm for exactly computing the edge connectivity. -- The first $O(1)$ round algorithm for the massively parallel computation setting with linear memory per machine.
Structured Spreadsheet Modelling and Implementation with Multiple Dimensions -- Part 2: Implementation
Venue: arXiv cs.SE 2019
Year: 2019

In Part 1, we showed how to develop a conceptual model of a problem involving variables of multiple dimensions, like Products, Regions, Sectors and Months. The conceptual model is presented as a Formula Diagram, giving a global view of the interaction between all the variables, and a Formula List, giving a precise view of the interaction between the variables. In this paper, we present precise steps to implement a multi-dimensional problem in a way that will produce a spreadsheet that is easy to maintain
Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data (Demo Paper)
Venue: arXiv cs.DB 2019
Year: 2019

With the widespread installation of location-enabled devices on public transportation, public vehicles are generating massive amounts of trajectory data in real time. However, using these trajectory data for meaningful analysis requires careful considerations in storing, managing, processing, and visualizing the data. Using the location data of the Los Angeles Metro bus system, along with publicly available bus schedule data, we conduct a data processing and analyses study to measure the performance of the public transportation system in Los Angeles utilizing a number of metrics including travel-time reliability, on-time performance, bus bunching, and travel-time estimation. We demonstrate the visualization of the data analysis results through an interactive web-based application. The developed algorithms and system provide powerful tools to detect issues and improve the efficiency of public transportation systems.
Finding Maximal Non-Redundant Association Rules in Tennis Data
Venue: arXiv cs.DB 2019
Year: 2019

The concept of association rules is well--known in data mining. But often redundancy and subsumption are not considered, and standard approaches produce thousands or even millions of resulting association rules. Without further information or post--mining approaches, this huge number of rules is typically useless for the domain specialist -- which is an instance of the infamous pattern explosion problem. In this work, we present a new definition of redundancy and subsumption based on the confidence and the support of the rules and propose post-- mining to prune a set of association rules. In a case study, we apply our method to association rules mined from spatio--temporal data. The data represent the trajectories of the ball in tennis matches -- more precisely, the points/times the tennis ball hits the ground. The goal is to analyze the strategies of the players and to try to improve their performance by looking at the resulting association rules. The proposed approach is general, and can also be applied to other spatio--temporal data with a similar structure.
Algorithms and System Architecture for Immediate Personalized News Recommendations
Venue: arXiv cs.IR 2019
Year: 2019

Personalization plays an important role in many services, just as news does. Many studies have examined news personalization algorithms, but few have considered practical environments. This paper provides algorithms and system architecture for generating immediate personalized news in a practical environment. Immediacy means changes in news trends and user interests are reflected in recommended news lists quickly. Since news trends and user interests rapidly change, immediacy is critical in news personalization applications. We develop algorithms and system architecture to realize immediacy. Our algorithms are based on collaborative filtering of user clusters and evaluate news articles using click-through rate and decay scores based on the time elapsed since the user's last access. Existing studies have not fully discussed system architecture, so a major contribution of this paper is that we demonstrate a system architecture and realize our algorithms and a configuration example implemented on top of Amazon Web Services. We evaluate the proposed method both offline and online. The offline experiments are conducted through a real-world dataset from a commercial news delivery service, and online experiments are conducted via A/B testing on production environments. We confirm the effectiveness of our proposed method and also that our system architecture can operate in large-scale production environments.
Harmonizing 3GPP and NFV description models to provide customized RAN slices in 5G networks
Venue: arXiv cs.NI 2019
Year: 2019

The standardization of Radio Access Network (RAN) in mobile networks has traditionally been led by 3GPP. However, the emergence of RAN slicing has introduced new aspects that fall outside 3GPP scope. Among them, network virtualization enables the particularization of multiple RAN behaviors over a common physical infrastructure. Using Virtualized Network Functions (VNFs) that comprise customized radio functionalities, each virtualized RAN, denominated RAN slice, could meet its specific requirements. Although 3GPP specifies the description model to manage RAN slices, it can neither particularize the behavior of a RAN slice nor leverage the NFV descriptors to define how its VNFs can accommodate its spatial and temporal traffic demands. In this article, we propose a description model that harmonizes 3GPP and ETSI-NFV viewpoints to manage RAN slices. The proposed model enables the translation of RAN slice requirements into customized virtualized radio functionalities defined through NFV descriptors. To clarify this proposal, we provide an example where three RAN slices with disruptive requirements are described following our solution.
Allen's Interval Algebra Makes the Difference
Venue: arXiv cs.AI 2019
Year: 2019

Allen's Interval Algebra constitutes a framework for reasoning about temporal information in a qualitative manner. In particular, it uses intervals, i.e., pairs of endpoints, on the timeline to represent entities corresponding to actions, events, or tasks, and binary relations such as precedes and overlaps to encode the possible configurations between those entities. Allen's calculus has found its way in many academic and industrial applications that involve, most commonly, planning and scheduling, temporal databases, and healthcare. In this paper, we present a novel encoding of Interval Algebra using answer-set programming (ASP) extended by difference constraints, i.e., the fragment abbreviated as ASP(DL), and demonstrate its performance via a preliminary experimental evaluation. Although our ASP encoding is presented in the case of Allen's calculus for the sake of clarity, we suggest that analogous encodings can be devised for other point-based calculi, too.
Bilingual is At Least Monolingual (BALM): A Novel Translation Algorithm that Encodes Monolingual Priors
Venue: arXiv cs.CL 2019
Year: 2019

State-of-the-art machine translation (MT) models do not use knowledge of any single language's structure; this is the equivalent of asking someone to translate from English to German while knowing neither language. BALM is a framework incorporates monolingual priors into an MT pipeline; by casting input and output languages into embedded space using BERT, we can solve machine translation with much simpler models. We find that English-to-German translation on the Multi30k dataset can be solved with a simple feedforward network under the BALM framework with near-SOTA BLEU scores.
Controlled Loosening-up (CLuP) -- achieving exact MIMO ML in polynomial time
Venue: arXiv cs.IT 2019
Year: 2019

In this paper we attack one of the most fundamental signal processing/informaton theory problems, widely known as the MIMO ML-detection. We introduce a powerful Random Duality Theory (RDT) mechanism that we refer to as the Controlled Loosening-up (CLuP) as a way of achieving the exact ML-performance in MIMO systems in polynomial time. We first outline the general strategy and then discuss the rationale behind the entire concept. A solid collection of results obtained through numerical experiments is presented as well and found to be in an excellent agreement with what the theory predicts. As this is the introductory paper of a massively general concept that we have developed, we mainly focus on keeping things as simple as possible and put the emphasis on the most fundamental ideas. In our several companion papers we present various other complementary results that relate to both, theoretical and practical aspects and their connections to a large collection of other problems and results that we have achieved over the years in Random Duality.
Threshold Greedy Based Task Allocation for Multiple Robot Operations
Venue: arXiv cs.MA 2019
Year: 2019

This paper deals with large-scale decentralised task allocation problems for multiple heterogeneous robots with monotone submodular objective functions. One of the significant challenges with the large-scale decentralised task allocation problem is the NP-hardness for computation and communication. This paper proposes a decentralised Decreasing Threshold Task Allocation (DTTA) algorithm that enables parallel allocation by leveraging a decreasing threshold to handle the NP-hardness. Then DTTA is upgraded to a more practical version Lazy Decreasing Threshold Task Allocation (LDTTA) by combining a variant of Lazy strategy. DTTA and LDTTA can release both computational and communicating burden for multiple robots in a decentralised network while providing an optimality bound of solution quality. To examine the performance of the proposed algorithms, this paper models a multi-target surveillance scenario and conducts Monte-Carlo simulations. Simulation results reveal that the proposed algorithms achieve similar function values but consume much less running time and consensus steps compared with benchmark decentralised task allocation algorithms.
Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech
Venue: arXiv cs.SD 2019
Year: 2019

We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis discrete wavelet transform was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db 6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.
An Event-Driven Approach to Serverless Seismic Imaging in the Cloud
Venue: arXiv cs.DC 2019
Year: 2019

Adapting the cloud for high-performance computing (HPC) is a challenging task, as software for HPC applications hinges on fast network connections and is sensitive to hardware failures. Using cloud infrastructure to recreate conventional HPC clusters is therefore in many cases an infeasible solution for migrating HPC applications to the cloud. As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven approach for running large-scale instances of this problem in the cloud. Instead of permanently running compute instances, our workflow is based on a serverless architecture with high throughput batch computing and event-driven computations, in which computational resources are only running as long as they are utilized. We demonstrate that this approach is very flexible and allows for resilient and nested levels of parallelization, including domain decomposition for solving the underlying partial differential equations. While the event-driven approach introduces some overhead as computational resources are repeatedly restarted, it inherently provides resilience to instance shut-downs and allows a significant reduction of cost by avoiding idle instances, thus making the cloud a viable alternative to on-premise clusters for large-scale seismic imaging.
Robust Invisible Video Watermarking with Attention
Venue: arXiv cs.MM 2019
Year: 2019

The goal of video watermarking is to embed a message within a video file in a way such that it minimally impacts the viewing experience but can be recovered even if the video is redistributed and modified, allowing media producers to assert ownership over their content. This paper presents RivaGAN, a novel architecture for robust video watermarking which features a custom attention-based mechanism for embedding arbitrary data as well as two independent adversarial networks which critique the video quality and optimize for robustness. Using this technique, we are able to achieve state-of-the-art results in deep learning-based video watermarking and produce watermarked videos which have minimal visual distortion and are robust against common video processing operations.
Understanding Continuous and Pleasant Linear Sensations on the Forearm from a Sequential Discrete Lateral Skin-Slip Haptic Device
Venue: arXiv cs.HC 2019
Year: 2019

A continuous stroking sensation on the skin can convey messages or emotion cues. We seek to induce this sensation using a combination of illusory motion and lateral stroking via a haptic device. Our system provides discrete lateral skin-slip on the forearm with rotating tactors, which independently provide lateral skin-slip in a timed sequence. We vary the sensation by changing the angular velocity and delay between adjacent tactors, such that the apparent speed of the perceived stroke ranges from 2.5 to 48.2 cm/s. We investigated which actuation parameters create the most pleasant and continuous sensations through a user study with 16 participants. On average, the sensations were rated by participants as both continuous and pleasant. The most continuous and pleasant sensations were created by apparent speeds of 7.7 and 5.1 cm/s, respectively. We also investigated the effect of spacing between contact points on the pleasantness and continuity of the stroking sensation, and found that the users experience a pleasant and continuous linear sensation even when the space between contact points is relatively large (40 mm). Understanding how sequential discrete lateral skin-slip creates continuous linear sensations can influence the design and control of future wearable haptic devices.
Deep Equilibrium Models
Venue: arXiv cs.LG 2019
Year: 2019

We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose the DEQ approach that directly finds these equilibrium points via root-finding. Such a method is equivalent to running an infinite depth (weight-tied) feedforward network, but has the notable advantage that we can analytically backpropagate through the equilibrium point using implicit differentiation. Using this approach, training and prediction in these networks require only constant memory, regardless of the effective "depth" of the network. We demonstrate how DEQs can be applied to two state-of-the-art deep sequence models: self-attention transformers and trellis networks. On large-scale language modeling tasks, such as the WikiText-103 benchmark, we show that DEQs 1) often improve performance over these state-of-the-art models (for similar parameter counts); 2) have similar computational requirements to existing models; and 3) vastly reduce memory consumption (often the bottleneck for training large sequence models), demonstrating an up-to 88% memory reduction in our experiments. The code is available at https://github.com/locuslab/deq .
Towards Models for Availability and Security Evaluation of Cloud Computing with Moving Target Defense
Venue: arXiv cs.CR 2019
Year: 2019

Security is one of the most relevant concerns in cloud computing. With the evolution of cyber-security threats, developing innovative techniques to thwart attacks is of utmost importance. One recent method to improve cloud computing security is Moving Target Defense (MTD). MTD makes use of dynamic reconfiguration in virtualized environments to "confuse" attackers or to nullify their knowledge about the system state. However, there is still no consolidated mechanism to evaluate the trade-offs between availability and security when using MTD on cloud computing. The evaluation through measurements is complex as one needs to deal with unexpected events as failures and attacks. To overcome this challenge, we intend to propose a set of models to evaluate the availability and security of MTD in cloud computing environments. The expected results include the quantification of availability and security levels under different conditions (e.g., different software aging rates, varying workloads, different attack intensities).
Exploration Without Global Consistency Using Local Volume Consolidation
Venue: arXiv cs.RO 2019
Year: 2019

In exploration, the goal is to build a map of an unknown environment. Most state-of-the-art approaches use map representations that require drift-free state estimates to function properly. Real-world state estimators, however, exhibit drift. In this paper, we present a 2D map representation for exploration that is robust to drift. Rather than a global map, it uses local metric volumes connected by relative pose estimates. This pose-graph does not need to be globally consistent. Overlaps between the volumes are resolved locally, rather than on the faulty estimate of space. We demonstrate our representation with a frontier-based exploration approach, evaluate it under different conditions and compare it with a commonly-used grid-based representation. We show that, at the cost of longer exploration time, using the proposed representation allows full coverage of space even for very large drift in the state estimate, contrary to the grid-based representation. The system is validated in a real world experiment and we discuss its extension to 3D.