OpenRaiser/Intern-Atlas huggingface dataset:
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM
Venue: arXiv cs.RO 2018
Year: 2018

We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping. However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered. Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned. Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging. In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution. We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view. In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.
Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder
Venue: arXiv cs.CL 2018
Year: 2018

In this work we present an unsupervised approach to summarize sentences in abstractive way using Variational Autoencoder (VAE). VAE are known to learn a semantically rich latent variable, representing high dimensional input. VAEs are trained by learning to reconstruct the input from the probabilistic latent variable. Explicitly providing the information about output length during training influences the VAE to not encode this information and thus can be manipulated during inference. Instructing the decoder to produce a shorter output sequence leads to expressing the input sentence with fewer words. We show on different summarization data sets, that these shorter sentences can not beat a simple baseline but yield higher ROUGE scores than trying to reconstruct the whole sentence.
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
Venue: arXiv cs.LG 2018
Year: 2018

Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection problem as a partially observable Markov decision process (POMDP) problem and propose a universal robust online detection algorithm using the framework of model-free reinforcement learning (RL) for POMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based algorithm in timely and accurate detection of cyber-attacks targeting the smart grid.
An On-line Design of Physical Watermarks
Venue: arXiv cs.SY 2018
Year: 2018

This paper considers the problem to design physical watermark signals to protect a control system against replay attacks. We first define the replay attack model, where an adversary replays the previous sensory data in order to fool the system. The physical watermarking scheme, which leverages a random control input as a watermark, to detect the replay attack is introduced. The optimal watermark signal design problem is then proposed as an optimization problem, which achieves the optimal trade-off between the control performance and attack detection performance. For the system with unknown parameters, we provide a procedure to asymptotically derive the optimal watermarking signal. Numerical examples are provided to illustrate the effectiveness of the proposed strategy.
Reasoning about Discrete and Continuous Noisy Sensors and Effectors in Dynamical Systems
Venue: arXiv cs.AI 2018
Year: 2018

Among the many approaches for reasoning about degrees of belief in the presence of noisy sensing and acting, the logical account proposed by Bacchus, Halpern, and Levesque is perhaps the most expressive. While their formalism is quite general, it is restricted to fluents whose values are drawn from discrete finite domains, as opposed to the continuous domains seen in many robotic applications. In this work, we show how this limitation in that approach can be lifted. By dealing seamlessly with both discrete distributions and continuous densities within a rich theory of action, we provide a very general logical specification of how belief should change after acting and sensing in complex noisy domains.
Approximate Query Processing over Static Sets and Sliding Windows
Venue: arXiv cs.DS 2018
Year: 2018

Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank(i) (i.e., computing sum_{j <= i}B[j]) and select(i) (i.e., finding min{p | rank(p) >= i}) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just (1+o(1)) factor more space than the fixed-window summation algorithms.
Enhanced Multiuser Superposition Transmission through Structured Modulation
Venue: arXiv cs.IT 2018
Year: 2018

The 5G air interface, namely, dynamic multiple access (MA) based on multiuser superposition transmission (MUST) and orthogonal multiple access (OMA), may require complicated scheduling and heavy signaling overhead. To address these challenges, we propose a a unified MA scheme for future cellular networks, which we refer to as structured multiuser superposition transmission (S-MUST). In S-MUST, we apply complex power allocation coefficients (CPACs) over multiuser legacy constellations to generate a composite constellation. In particular, the in-phase (I) and quadrature (Q) components of the legacy constellation of each user are separately multiplied by those of the CPACs. As such, the CPACs offer an extra degree of freedom for multiplexing users and guarantee fairness in symmetric broadcast channels. This new paradigm of superposition coding allows us to design IQ separation at the user side, which significantly reduces the decoding complexity without degrading performance. Hence, it supports low-complexity frequency-selective scheduling that does not entail dynamically switching between MUST and OMA. We further propose to quantize the CPACs into complex numbers where I and Q components of each quantized coefficient are primes, facilitating parallel interference cancellation at each user via modulo operations, last but not least, we generalize the design of S-MUST to exploit the capabilities of multiantenna base stations. The proposed S-MUST exhibits an improved user fairness with respect to conventional MUST (134% spectral efficiency enhancement) and a lower system complexity compared with dynamically alternating MUST and OMA.
Reconfiguration of graphs with connectivity constraints
Venue: arXiv cs.DM 2018
Year: 2018

A graph $G$ realizes the degree sequence $S$ if the degrees of its vertices is $S$. Hakimi gave a necessary and sufficient condition to guarantee that there exists a connected multigraph realizing $S$. Taylor later proved that any connected multigraph can be transformed into any other via a sequence of flips (maintaining connectivity at any step). A flip consists in replacing two edges $ab$ and $cd$ by the diagonals $ac$ and $bd$. In this paper, we study a generalization of this problem. A set of subsets of vertices $\mathcal{CC}$ is \emph{nested} if for every $C,C' \in \mathcal{CC}$ either $C \cap C' = \emptyset$ or one is included in the other. We are interested in multigraphs realizing a degree sequence $S$ and such that all the sets of a nested collection $\mathcal{CC}$ induce connected subgraphs. Such constraints naturally appear in tandem mass spectrometry. We show that it is possible to decide in polynomial if there exists a graph realizing $S$ where all the sets in $\mathcal{CC}$ induce connected subgraphs. Moreover, we prove that all such graphs can be obtained via a sequence of flips such that all the intermediate graphs also realize $S$ and where all the sets of $\mathcal{CC}$ induce connected subgraphs. Our proof is algorithmic and provides a polynomial time approximation algorithm on the shortest sequence of flips between two graphs whose ratio depends on the depth of the nested partition.
Hardware-Aware Machine Learning: Modeling and Optimization
Venue: arXiv cs.LG 2018
Year: 2018

Recent breakthroughs in Deep Learning (DL) applications have made DL models a key component in almost every modern computing system. The increased popularity of DL applications deployed on a wide-spectrum of platforms have resulted in a plethora of design challenges related to the constraints introduced by the hardware itself. What is the latency or energy cost for an inference made by a Deep Neural Network (DNN)? Is it possible to predict this latency or energy consumption before a model is trained? If yes, how can machine learners take advantage of these models to design the hardware-optimal DNN for deployment? From lengthening battery life of mobile devices to reducing the runtime requirements of DL models executing in the cloud, the answers to these questions have drawn significant attention. One cannot optimize what isn't properly modeled. Therefore, it is important to understand the hardware efficiency of DL models during serving for making an inference, before even training the model. This key observation has motivated the use of predictive models to capture the hardware performance or energy efficiency of DL applications. Furthermore, DL practitioners are challenged with the task of designing the DNN model, i.e., of tuning the hyper-parameters of the DNN architecture, while optimizing for both accuracy of the DL model and its hardware efficiency. Therefore, state-of-the-art methodologies have proposed hardware-aware hyper-parameter optimization techniques. In this paper, we provide a comprehensive assessment of state-of-the-art work and selected results on the hardware-aware modeling and optimization for DL applications. We also highlight several open questions that are poised to give rise to novel hardware-aware designs in the next few years, as DL applications continue to significantly impact associated hardware systems and platforms.
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Venue: arXiv cs.DC 2018
Year: 2018

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads.
On the Integrity of Cross-Origin JavaScripts
Venue: arXiv cs.CR 2018
Year: 2018

The same-origin policy is a fundamental part of the Web. Despite the restrictions imposed by the policy, embedding of third-party JavaScript code is allowed and commonly used. Nothing is guaranteed about the integrity of such code. To tackle this deficiency, solutions such as the subresource integrity standard have been recently introduced. Given this background, this paper presents the first empirical study on the temporal integrity of cross-origin JavaScript code. According to the empirical results based on a ten day polling period of over 35 thousand scripts collected from popular websites, (i) temporal integrity changes are relatively common; (ii) the adoption of the subresource integrity standard is still in its infancy; and (iii) it is possible to statistically predict whether a temporal integrity change is likely to occur. With these results and the accompanying discussion, the paper contributes to the ongoing attempts to better understand security and privacy in the current Web.
DSTC: DNS-based Strict TLS Configurations
Venue: arXiv cs.CR 2018
Year: 2018

Most TLS clients such as modern web browsers enforce coarse-grained TLS security configurations. They support legacy versions of the protocol that have known design weaknesses, and weak ciphersuites that provide fewer security guarantees (e.g. non Forward-Secrecy), mainly to provide backward compatibility. This opens doors to downgrade attacks, as is the case of the POODLE attack [18], which exploits the client's silent fallback to downgrade the protocol version to exploit the legacy version's flaws. To achieve a better balance between security and backward compatibility, we propose a DNS-based mechanism that enables TLS servers to advertise their support for the latest version of the protocol and strong ciphersuites (that provide Forward-Secrecy and Authenticated-Encryption simultaneously). This enables clients to consider prior knowledge about the servers' TLS configurations to enforce a fine-grained TLS configurations policy. That is, the client enforces strict TLS configurations for connections going to the advertising servers, while enforcing default configurations for the rest of the connections. We implement and evaluate the proposed mechanism and show that it is feasible, and incurs minimal overhead. Furthermore, we conduct a TLS scan for the top 10,000 most visited websites globally, and show that most of the websites can benefit from our mechanism.
Approximation algorithms for the three-machine proportionate mixed shop scheduling
Venue: arXiv cs.DS 2018
Year: 2018

A mixed shop is a manufacturing infrastructure designed to process a mixture of a set of flow-shop jobs and a set of open-shop jobs. Mixed shops are in general much more complex to schedule than flow-shops and open-shops, and have been studied since the 1980's. We consider the three machine proportionate mixed shop problem denoted as $M3 \mid prpt \mid C_{\max}$, in which each job has equal processing times on all three machines. Koulamas and Kyparisis [{\it European Journal of Operational Research}, 243:70--74,2015] showed that the problem is solvable in polynomial time in some very special cases; for the non-solvable case, they proposed a $5/3$-approximation algorithm. In this paper, we present an improved $4/3$-approximation algorithm and show that this ratio of $4/3$ is asymptotically tight; when the largest job is a flow-shop job, we present a fully polynomial-time approximation scheme (FPTAS). On the negative side, while the $F3 \mid prpt \mid C_{\max}$ problem is polynomial-time solvable, we show an interesting hardness result that adding one open-shop job to the job set makes the problem NP-hard if this open-shop job is larger than any flow-shop job. We are able to design an FPTAS for this special case too.
Sampled Policy Gradient for Learning to Play the Game Agar.io
Venue: arXiv cs.AI 2018
Year: 2018

In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an approximated policy gradient by using the critic to evaluate the samples. This sampling allows SPG to search the action-Q-value space more globally than deterministic policy gradient (DPG), enabling it to theoretically avoid more local optima. SPG is compared to Q-learning and the actor-critic algorithms CACLA and DPG in a pellet collection task and a self play environment in the game Agar.io. The online game Agar.io has become massively popular on the internet due to intuitive game design and the ability to instantly compete against players around the world. From the point of view of artificial intelligence this game is also very intriguing: The game has a continuous input and action space and allows to have diverse agents with complex strategies compete against each other. The experimental results show that Q-Learning and CACLA outperform a pre-programmed greedy bot in the pellet collection task, but all algorithms fail to outperform this bot in a fighting scenario. The SPG algorithm is analyzed to have great extendability through offline exploration and it matches DPG in performance even in its basic form without extensive sampling.
Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteristics in travel decision making. It involves developing a joint tri-partite Bayesian graphical network model using a Restricted Boltzmann Machine (RBM) generative modelling framework. We apply this framework on a mode choice survey data to identify abstract latent variables and compare the performance with a traditional latent variable model with specific latent preferences -- safety, comfort, and environmental. Data collected from a joint stated and revealed preference mode choice survey in Quebec, Canada were used to calibrate the RBM model. Results show that a signficant impact on model likelihood statistics and suggests that machine learning tools are highly suitable for modelling complex networks of conditional independent behaviour interactions.
Inspecting Interactions: Online News Media Synergies in Social Media
Venue: arXiv cs.SI 2018
Year: 2018

The rising popularity of social media has radically changed the way news content is propagated, including interactive attempts with new dimensions. To date, traditional news media such as newspapers, television and radio have already adapted their activities to the online news media by utilizing social media, blogs, websites etc. This paper provides some insight into the social media presence of worldwide popular news media outlets. Despite the fact that these large news media propagate content via social media environments to a large extent and very little is known about the news item producers, providers and consumers in the news media community in social media.To better understand these interactions, this work aims to analyze news items in two large social media, Twitter and Facebook. Towards that end, we collected all published posts on Twitter and Facebook from 48 news media to perform descriptive and predictive analyses using the dataset of 152K tweets and 80K Facebook posts. We explored a set of news media that originate content by themselves in social media, those who distribute their news items to other news media and those who consume news content from other news media and/or share replicas. We propose a predictive model to increase news media popularity among readers based on the number of posts, number of followers and number of interactions performed within the news media community. The results manifested that, news media should disperse their own content and they should publish first in social media in order to become a popular news media and receive more attractions to their news items from news readers.
Energy Efficient Cloud Control and Pricing in Geographically Distributed Data Centers
Venue: arXiv cs.DC 2018
Year: 2018

It is estimated that data centers constitute 1.5% of global electricity usage. At the same time, to serve increasing user requirements, modern cloud providers are operating multiple geographically distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures that we call geotemporal inputs. Furthermore, pricing policies at which cloud providers can offer computational resources depend on the quality of service (QoS). With such pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem. Existing cloud control methods are suitable only for a single data center or do not consider all the available cloud control actions that can reduce energy costs in geographically distributed data centers. In this thesis, we propose a pervasive cloud control approach consisting of multiple methods for dynamic resource reallocation and hardware configuration adapted to volatile geotemporal inputs. The proposed methods consider the QoS impact of cloud control actions and the data quality limits of time series forecasting methods. We offer a cloud controller design that supports future extensions when new decision support components need to be added. We also propose novel pricing schemes which account for the computational resource availability and costs that arise from our cloud control approach to enable both flexible, energy-aware and high performance cloud computing. We evaluate our methods empirically and in a number of simulations using historical traces of electricity prices, temperatures, workloads and other data. Our results show that significant energy cost savings are possible without harming the QoS or service revenue in geographically distributed cloud computing.
In Defense of the Classification Loss for Person Re-Identification
Venue: arXiv cs.CV 2018
Year: 2018

The recent research for person re-identification has been focused on two trends. One is learning the part-based local features to form more informative feature descriptors. The other is designing effective metric learning loss functions such as the triplet loss family. We argue that learning global features with classification loss could achieve the same goal, even with some simple and cost-effective architecture design. In this paper, we first explain why the person re-id framework with standard classification loss usually has inferior performance compared to metric learning. Based on that, we further propose a person re-id framework featured by channel grouping and multi-branch strategy, which divides global features into multiple channel groups and learns the discriminative channel group features by multi-branch classification layers. The extensive experiments show that our framework outperforms prior state-of-the-arts in terms of both accuracy and inference speed.