OpenRaiser/Intern-Atlas huggingface dataset:
Learning to segment with image-level supervision
Venue: arXiv cs.CV 2019
Year: 2019

Deep convolutional networks have achieved the state-of-the-art for semantic image segmentation tasks. However, training these networks requires access to densely labeled images, which are known to be very expensive to obtain. On the other hand, the web provides an almost unlimited source of images annotated at the image level. How can one utilize this much larger weakly annotated set for tasks that require dense labeling? Prior work often relied on localization cues, such as saliency maps, objectness priors, bounding boxes etc., to address this challenging problem. In this paper, we propose a model that generates auxiliary labels for each image, while simultaneously forcing the output of the CNN to satisfy the mean-field constraints imposed by a conditional random field. We show that one can enforce the CRF constraints by forcing the distribution at each pixel to be close to the distribution of its neighbors. This is in stark contrast with methods that compute a recursive expansion of the mean-field distribution using a recurrent architecture and train the resultant distribution. Instead, the proposed model adds an extra loss term to the output of the CNN, and hence, is faster than recursive implementations. We achieve the state-of-the-art for weakly supervised semantic image segmentation on VOC 2012 dataset, assuming no manually labeled pixel level information is available. Furthermore, the incorporation of conditional random fields in CNN incurs little extra time during training.
A measure of authorship by publications
Venue: arXiv cs.DL 2019
Year: 2019

Measuring publication success of a researcher is a complicated task as publications are often co-authored by multiple authors, and so, require comparison of solo publications with joint publications. In this paper, like \cite{price1981multiple}, we argue for an egalitarian perspective in accomplishing this task. More specifically, we justify the need for an ethical perspective in quantifying academic author by identifying certain ethical difficulties of some popular contemporary indices used for this purpose. And then we show that for any given dataset of research papers, the unique method satisfying the ethical notions of {\it identity independence} and performance invariance must be the egaliatarian E-index proposed by \cite{bps} and \cite{price1981multiple}. In our setting, this egalitarian method divides authorship of joint projects equally among authors and sums across all publications of each author.
On Optimal Mechanisms in the Two-Item Single-Buyer Unit-Demand Setting
Venue: arXiv cs.GT 2019
Year: 2019

We consider the problem of designing a revenue-optimal mechanism in the two-item, single-buyer, unit-demand setting when the buyer's valuations, $(z_1, z_2)$, are uniformly distributed in an arbitrary rectangle $[c,c+b_1]\times[c,c+b_2]$ in the positive quadrant. We provide a complete and explicit solution for arbitrary nonnegative values of $(c,b_1,b_2)$. We identify five simple structures, each with at most five (possibly stochastic) menu items, and prove that the optimal mechanism has one of the five structures. We also characterize the optimal mechanism as a function of $b_1, b_2$, and $c$. When $c$ is low, the optimal mechanism is a posted price mechanism with an exclusion region; when $c$ is high, it is a posted price mechanism without an exclusion region. Our results are the first to show the existence of optimal mechanisms with no exclusion region, to the best of our knowledge.
DeepTingle
Venue: arXiv cs.CL 2019
Year: 2019

DeepTingle is a text prediction and classification system trained on the collected works of the renowned fantastic gay erotica author Chuck Tingle. Whereas the writing assistance tools you use everyday (in the form of predictive text, translation, grammar checking and so on) are trained on generic, purportedly "neutral" datasets, DeepTingle is trained on a very specific, internally consistent but externally arguably eccentric dataset. This allows us to foreground and confront the norms embedded in data-driven creativity and productivity assistance tools. As such tools effectively function as extensions of our cognition into technology, it is important to identify the norms they embed within themselves and, by extension, us. DeepTingle is realized as a web application based on LSTM networks and the GloVe word embedding, implemented in JavaScript with Keras-JS.
Constructing a Consensus Phylogeny from a Leaf-Removal Distance
Venue: arXiv cs.DS 2019
Year: 2019

Understanding the evolution of a set of genes or species is a fundamental problem in evolutionary biology. The problem we study here takes as input a set of trees describing {possibly discordant} evolutionary scenarios for a given set of genes or species, and aims at finding a single tree that minimizes the leaf-removal distance to the input trees. This problem is a specific instance of the general consensus/supertree problem, widely used to combine or summarize discordant evolutionary trees. The problem we introduce is specifically tailored to address the case of discrepancies between the input trees due to the misplacement of individual taxa. Most supertree or consensus tree problems are computationally intractable, and we show that the problem we introduce is also NP-hard. We provide tractability results in form of a 2-approximation algorithm. We also introduce a variant that minimizes the maximum number $d$ of leaves that are removed from any input tree, and provide a parameterized algorithm for this problem with parameter $d$.
Self-stabilising Byzantine Clock Synchronisation is Almost as Easy as Consensus
Venue: arXiv cs.DC 2019
Year: 2019

We give fault-tolerant algorithms for establishing synchrony in distributed systems in which each of the $n$ nodes has its own clock. Our algorithms operate in a very strong fault model: we require self-stabilisation, i.e., the initial state of the system may be arbitrary, and there can be up to $f<n/3$ ongoing Byzantine faults, i.e., nodes that deviate from the protocol in an arbitrary manner. Furthermore, we assume that the local clocks of the nodes may progress at different speeds (clock drift) and communication has bounded delay. In this model, we study the pulse synchronisation problem, where the task is to guarantee that eventually all correct nodes generate well-separated local pulse events (i.e., unlabelled logical clock ticks) in a synchronised manner. Compared to prior work, we achieve exponential improvements in stabilisation time and the number of communicated bits, and give the first sublinear-time algorithm for the problem: - In the deterministic setting, the state-of-the-art solutions stabilise in time $Θ(f)$ and have each node broadcast $Θ(f \log f)$ bits per time unit. We exponentially reduce the number of bits broadcasted per time unit to $Θ(\log f)$ while retaining the same stabilisation time. - In the randomised setting, the state-of-the-art solutions stabilise in time $Θ(f)$ and have each node broadcast $O(1)$ bits per time unit. We exponentially reduce the stabilisation time to $\log^{O(1)} f$ while each node broadcasts $\log^{O(1)} f$ bits per time unit. These results are obtained by means of a recursive approach reducing the above task of self-stabilising pulse synchronisation in the bounded-delay model to non-self-stabilising binary consensus in the synchronous model. In general, our approach introduces at most logarithmic overheads in terms of stabilisation time and broadcasted bits over the underlying consensus routine.
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense
Venue: arXiv cs.LG 2019
Year: 2019

Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for 'boosting' the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image, a trained network is picked randomly from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that this approach, MTDeep, reduces misclassification on perturbed images in various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.
A New Parallel Message-distribution Technique for Cost-based Steganography
Venue: arXiv cs.MM 2019
Year: 2019

This paper presents two novel approaches to increase performance bounds of image steganography under the criteria of minimizing distortion. First, in order to efficiently use the images' capacities, we propose using parallel images in the embedding stage. The result is then used to prove sub-optimality of the message distribution technique used by all cost based algorithms including HUGO, S-UNIWARD, and HILL. Second, a new distribution approach is presented to further improve the security of these algorithms. Experiments show that this distribution method avoids embedding in smooth regions and thus achieves a better performance, measured by state-of-the-art steganalysis, when compared with the current used distribution.
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
Venue: arXiv cs.LG 2019
Year: 2019

Graph-based methods have been quite successful in solving unsupervised and semi-supervised learning problems, as they provide a means to capture the underlying geometry of the dataset. It is often desirable for the constructed graph to satisfy two properties: first, data points that are similar in the feature space should be strongly connected on the graph, and second, the class label information should vary smoothly with respect to the graph, where smoothness is measured using the spectral properties of the graph Laplacian matrix. Recent works have justified some of these smoothness conditions by showing that they are strongly linked to the semi-supervised smoothness assumption and its variants. In this work, we reinforce this connection by viewing the problem from a graph sampling theoretic perspective, where class indicator functions are treated as bandlimited graph signals (in the eigenvector basis of the graph Laplacian) and label prediction as a bandlimited reconstruction problem. Our approach involves analyzing the bandwidth of class indicator signals generated from statistical data models with separable and nonseparable classes. These models are quite general and mimic the nature of most real-world datasets. Our results show that in the asymptotic limit, the bandwidth of any class indicator is also closely related to the geometry of the dataset. This allows one to theoretically justify the assumption of bandlimitedness of class indicator signals, thereby providing a sampling theoretic interpretation of graph-based semi-supervised classification.
Slimness of graphs
Venue: arXiv cs.DM 2019
Year: 2019

Slimness of a graph measures the local deviation of its metric from a tree metric. In a graph $G=(V,E)$, a geodesic triangle $\bigtriangleup(x,y,z)$ with $x, y, z\in V$ is the union $P(x,y) \cup P(x,z) \cup P(y,z)$ of three shortest paths connecting these vertices. A geodesic triangle $\bigtriangleup(x,y,z)$ is called $δ$-slim if for any vertex $u\in V$ on any side $P(x,y)$ the distance from $u$ to $P(x,z) \cup P(y,z)$ is at most $δ$, i.e. each path is contained in the union of the $δ$-neighborhoods of two others. A graph $G$ is called $δ$-slim, if all geodesic triangles in $G$ are $δ$-slim. The smallest value $δ$ for which $G$ is $δ$-slim is called the slimness of $G$. In this paper, using the layering partition technique, we obtain sharp bounds on slimness of such families of graphs as (1) graphs with cluster-diameter $Δ(G)$ of a layering partition of $G$, (2) graphs with tree-length $λ$, (3) graphs with tree-breadth $ρ$, (4) $k$-chordal graphs, AT-free graphs and HHD-free graphs. Additionally, we show that the slimness of every 4-chordal graph is at most 2 and characterize those 4-chordal graphs for which the slimness of every of its induced subgraph is at most 1.
Multi-point Codes from the GGS Curves
Venue: arXiv cs.IT 2019
Year: 2019

This paper is concerned with the construction of algebraic geometric codes defined from GGS curves. It is of significant use to describe bases for the Riemann-Roch spaces associated with totally ramified places, which enables us to study multi-point AG codes. Along this line, we characterize explicitly the Weierstrass semigroups and pure gaps. Additionally, we determine the floor of a certain type of divisor and investigate the properties of AG codes from GGS curves. Finally, we apply these results to find multi-point codes with excellent parameters. As one of the examples, a presented code with parameters $ [216,190,\geqslant 18] $ over $ \mathbb{F}_{64} $ yields a new record.
Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
Venue: arXiv cs.SE 2019
Year: 2019

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.
PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks
Venue: arXiv cs.SI 2019
Year: 2019

As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks. Inspired by our probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective. We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods. A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN. We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks. Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure.
A Survey Of Cross-lingual Word Embedding Models
Venue: arXiv cs.CL 2019
Year: 2019

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.
ROS Navigation Tuning Guide
Venue: arXiv cs.RO 2019
Year: 2019

The ROS navigation stack is powerful for mobile robots to move from place to place reliably. The job of navigation stack is to produce a safe path for the robot to execute, by processing data from odometry, sensors and environment map. Maximizing the performance of this navigation stack requires some fine tuning of parameters, and this is not as simple as it looks. One who is sophomoric about the concepts and reasoning may try things randomly, and wastes a lot of time. This article intends to guide the reader through the process of fine tuning navigation parameters. It is the reference when someone need to know the "how" and "why" when setting the value of key parameters. This guide assumes that the reader has already set up the navigation stack and ready to optimize it. This is also a summary of my work with the ROS navigation stack.
Dominating Induced Matchings in $S_{1,2,4}$-Free Graphs
Venue: arXiv cs.DM 2019
Year: 2019

Let $G=(V,E)$ be a finite undirected graph without loops and multiple edges. A subset $M \subseteq E$ of edges is a {\em dominating induced matching} ({\em d.i.m.}) in $G$ if every edge in $E$ is intersected by exactly one edge of $M$. In particular, this means that $M$ is an induced matching, and every edge not in $M$ shares exactly one vertex with an edge in $M$. Clearly, not every graph has a d.i.m. The \emph{Dominating Induced Matching} (\emph{DIM}) problem asks for the existence of a d.i.m.\ in $G$; this problem is also known as the \emph{Efficient Edge Domination} problem; it is the {\em Efficient Domination} problem for line graphs. The DIM problem is \NP-complete in general, and even for very restricted graph classes such as planar bipartite graphs with maximum degree 3. However, DIM is solvable in polynomial time for claw-free (i.e., $S_{1,1,1}$-free) graphs, for $S_{1,2,3}$-free graphs as well as for $S_{2,2,2}$-free graphs, in linear time for $P_7$-free graphs, and in polynomial time for $P_8$-free graphs ($P_k$ is a special case of $S_{i,j,\ell}$). In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for $S_{i,j,k}$-free graphs for every fixed $i,j,k$. In this paper, combining two distinct approaches, we solve it in polynomial time for $S_{1,2,4}$-free graphs which generalizes the $S_{1,2,3}$-free as well as the $P_7$-free case.
Theoretical Performance Analysis of Vehicular Broadcast Communications at Intersection and their Optimization
Venue: arXiv cs.PF 2019
Year: 2019

In this paper, we propose an optimization method for the broadcast rate in vehicle-to-vehicle (V2V) broadcast communications at an intersection on the basis of theoretical analysis. We consider a model in which locations of vehicles are modeled separately as queuing and running segments and derive key performance metrics of V2V broadcast communications via a stochastic geometry approach. Since these theoretical expressions are mathematically intractable, we developed closed-form approximate formulae for them. Using them, we optimize the broadcast rate such that the mean number of successful receivers per unit time is maximized. Because of the closed form approximation, the optimal rate can be used as a guideline for a real-time control-method, which is not achieved through time-consuming simulations. We evaluated our method through numerical examples and demonstrated the effectiveness of our method.
Robust Detection in Leak-Prone Population Protocols
Venue: arXiv cs.DS 2019
Year: 2019

In contrast to electronic computation, chemical computation is noisy and susceptible to a variety of sources of error, which has prevented the construction of robust complex systems. To be effective, chemical algorithms must be designed with an appropriate error model in mind. Here we consider the model of chemical reaction networks that preserve molecular count (population protocols), and ask whether computation can be made robust to a natural model of unintended "leak" reactions. Our definition of leak is motivated by both the particular spurious behavior seen when implementing chemical reaction networks with DNA strand displacement cascades, as well as the unavoidable side reactions in any implementation due to the basic laws of chemistry. We develop a new "Robust Detection" algorithm for the problem of fast (logarithmic time) single molecule detection, and prove that it is robust to this general model of leaks. Besides potential applications in single molecule detection, the error-correction ideas developed here might enable a new class of robust-by-design chemical algorithms. Our analysis is based on a non-standard hybrid argument, combining ideas from discrete analysis of population protocols with classic Markov chain techniques.
Noisy Networks for Exploration
Venue: arXiv cs.LG 2019
Year: 2019

We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and dueling agents (entropy reward and $ε$-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.
RDV: An Alternative To Proof-of-Work And A Real Decentralized Consensus For Blockchain
Venue: arXiv cs.CR 2019
Year: 2019

A blockchain is a decentralized ledger where all transactions are recorded. For having a reliable blockchain and double-spending prevention, we need a decentralized consensus and agreement on a blockchain. Bitcoin uses proof-of-work (PoW). It is a cryptographic puzzle that is difficult to solve but easy to verify. However, because of significant latency of proof-of-work for transactions confirmation, this consensus mechanism is vulnerable against double-spending. On the other hand, PoW consumes a significant amount of energy that by growing the network, it becomes a major problematic of this consensus mechanism. In this paper, we introduce an alternative to PoW, because of all its major problems and security issues that may lead to collapsing decentralization of the blockchain, while a full decentralized system is the main purpose of using blockchain technology. The approach we introduce is based on a distributed voting process and called "RDV: Register, Deposit, Vote". Since in RDV algorithm, there is no mining process, so it is appropriate for low-level energy devices and Internet of Things (IoT).