OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Domain Adaptation with Adversarial Residual Transform Networks
Venue: arXiv cs.CV 2019
Year: 2019

Domain adaptation is widely used in learning problems lacking labels. Recent studies show that deep adversarial domain adaptation models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability whereas the latter is very hard to train. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Residual Transform Networks (ARTNs) to improve the generalization ability, which directly transforms the source features into the space of target features. In this model, residual connections are used to share features and adversarial loss is reconstructed, thus making the model more generalized and easier to train. Moreover, a special regularization term is added to the loss function to alleviate a vanishing gradient problem, which enables its training process stable. A series of experiments based on Amazon review dataset, digits datasets and Office-31 image datasets are conducted to show that the proposed ARTN can be comparable with the methods of the state-of-the-art.
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs
Venue: arXiv cs.DS 2019
Year: 2019

Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erdős-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward $O(n^{11/5} \log n )$-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erdős-Rényi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
Venue: arXiv cs.LG 2019
Year: 2019

We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as compressed text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
On Cyber Risk Management of Blockchain Networks: A Game Theoretic Approach
Venue: arXiv cs.CR 2019
Year: 2019

Open-access blockchains based on proof-of-work protocols have gained tremendous popularity for their capabilities of providing decentralized tamper-proof ledgers and platforms for data-driven autonomous organization. Nevertheless, the proof-of-work based consensus protocols are vulnerable to cyber-attacks such as double-spending. In this paper, we propose a novel approach of cyber risk management for blockchain-based service. In particular, we adopt the cyber-insurance as an economic tool for neutralizing cyber risks due to attacks in blockchain networks. We consider a blockchain service market, which is composed of the infrastructure provider, the blockchain provider, the cyber-insurer, and the users. The blockchain provider purchases from the infrastructure provider, e.g., a cloud, the computing resources to maintain the blockchain consensus, and then offers blockchain services to the users. The blockchain provider strategizes its investment in the infrastructure and the service price charged to the users, in order to improve the security of the blockchain and thus optimize its profit. Meanwhile, the blockchain provider also purchases a cyber-insurance from the cyber-insurer to protect itself from the potential damage due to the attacks. In return, the cyber-insurer adjusts the insurance premium according to the perceived risk level of the blockchain service. Based on the assumption of rationality for the market entities, we model the interaction among the blockchain provider, the users, and the cyber-insurer as a two-level Stackelberg game. Namely, the blockchain provider and the cyber-insurer lead to set their pricing/investment strategies, and then the users follow to determine their demand of the blockchain service. Specifically, we consider the scenario of double-spending attacks and provide a series of analytical results about the Stackelberg equilibrium in the market game.
Sentiment Adaptive End-to-End Dialog Systems
Venue: arXiv cs.CL 2019
Year: 2019

End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating. However, current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. We incorporated user sentiment information in both supervised and reinforcement learning settings. In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task. This work is the first attempt to incorporate multimodal user information in the adaptive end-to-end dialog system training framework and attained state-of-the-art performance.
Fundamentals of Parameterized Complexity Revisited
Venue: arXiv cs.CC 2019
Year: 2019

Flum and Grohe define a parameter (parameterization) as a function $κ$ which maps words over a given alphabet to natural numbers. They require such functions to be polynomial-time computable. We show how this technical restriction can be lifted without breaking the theory. More specifically, instead of $κ$ we consider the set of languages that it bounds as parameterization and define the basic notions of parameterized complexity in terms of promise problems, which completely replace slices. One advantage of this formalization is that it becomes possible to interpret any complexity-theoretic concept which can be considered on a restricted set of inputs as a parameterized concept. Moreover, this formalization provides a unified way to apply the parameterization paradigm to other kinds of complexity such as enumeration or approximation by simply defining promise problems.
Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification
Venue: arXiv cs.CL 2019
Year: 2019

The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB). This paper shows that overcoming LCB in Grammatical Error Correction (GEC) evaluation cannot be attained by re-scaling or by increasing the number of references in any feasible range, contrary to previous suggestions. This is due to the long-tailed distribution of valid corrections for a sentence. Concretely, we show that LCB incentivizes GEC systems to avoid correcting even when they can generate a valid correction. Consequently, existing systems obtain comparable or superior performance compared to humans, by making few but targeted changes to the input. Similar effects on Text Simplification further support our claims.
Dynamic Active Constraints for Surgical Robots using Vector Field Inequalities
Venue: arXiv cs.RO 2019
Year: 2019

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still underrepresented in procedures with constrained workspaces, such as deep brain neurosurgery and endonasal surgery. In these procedures, surgeons have restricted vision to areas near the surgical tooltips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings. In this work, our vector-field-inequalities method is extended to provide dynamic active-constraints to any number of robots and moving objects sharing the same workspace. The method is evaluated with experiments and simulations in which robot tools have to avoid collisions autonomously and in real-time, in a constrained endonasal surgical environment. Simulations show that with our method the combined trajectory error of two robotic systems is optimal. Experiments using a real robotic system show that the method can autonomously prevent collisions between the moving robots themselves and between the robots and the environment. Moreover, the framework is also successfully verified under teleoperation with tool-tissue interactions.
Exploiting CSMA/ECA and Adaptive Sensitivity Control for Simultaneous Transmit and Receive in IEEE 802.11 WLANs
Venue: arXiv cs.NI 2019
Year: 2019

Ever since the feasibility of in-band full-duplex (FD) at the Physical (PHY) layer has been established, several studies have emerged investigating protocol aspects of enabling FD operation in various legacy wireless technologies. Recently, the adoption of a simultaneous transmit and receive (STR) mode for next generation wireless local area networks (WLANs) has received significant attention. Enabling STR mode (FD communication mode) in 802.11 WLANs creates bi-directional FD (BFD) and uni-directional FD (UFD) links. STR mode in 802.11 WLANs must be enabled with minimal protocol modifications while accounting for the co-existence and compatibility with legacy nodes and protocols. This paper provides a novel solution, that can leverage carrier sense multiple access with enhanced collision avoidance (CSMA/ECA) and adaptive sensitivity control mechanisms, for enabling STR operation. The key aspects of the proposed solution include co-existence with legacy nodes, identification of eligible nodes for UFD, optimization of secondary BFD and UFD transmissions, and creation of UFD opportunities. Performance evaluation demonstrates that the proposed solution is effective in achieving the gains provided by STR operation.
Elevation Beamforming with Full Dimension MIMO Architectures in 5G Systems: A Tutorial
Venue: arXiv cs.IT 2019
Year: 2019

Full dimension (FD) multiple-input multiple-output (MIMO) technology has attracted substantial research attention from both wireless industry and academia in the last few years as a promising technique for next-generation wireless communication networks. FD-MIMO scenarios utilize a planar two-dimensional active antenna system (AAS) that not only allows a large number of antenna elements to be placed within feasible base station (BS) form factors, but also provides the ability of adaptive electronic beam control over both the elevation and the traditional azimuth dimensions. This paper presents a tutorial on elevation beamforming analysis for cellular networks utilizing FD Massive MIMO antenna arrays. In contrast to existing works that focus on the standardization of FD-MIMO in the 3rd Generation Partnership Project (3GPP), this tutorial is distinguished by its depth with respect to the theoretical aspects of antenna array and 3D channel modeling. In an attempt to bridge the gap between industry and academia, this preliminary tutorial introduces the relevant array and transceiver architecture designs proposed in the 3GPP Release 13 that enable elevation beamforming. Then it presents and compares two different 3D channel modeling approaches that can be utilized for the performance analysis of elevation beamforming techniques. The spatial correlation in FD-MIMO arrays is characterized and compared based on both channel modeling approaches and some insights into the impact of different channel and array parameters on the correlation are drawn. All these aspects are put together to provide a mathematical framework for the design of elevation beamforming schemes in single-cell and multi-cell scenarios. Simulation examples associated with comparisons and discussions are also presented. To this end, this paper highlights the state-of-the-art research and points out future research directions.
A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.
How deep should be the depth of convolutional neural networks: a backyard dog case study
Venue: arXiv cs.NE 2019
Year: 2019

The work concerns the problem of reducing a pre-trained deep neuronal network to a smaller network, with just few layers, whilst retaining the network's functionality on a given task The proposed approach is motivated by the observation that the aim to deliver the highest accuracy possible in the broadest range of operational conditions, which many deep neural networks models strive to achieve, may not necessarily be always needed, desired, or even achievable due to the lack of data or technical constraints. In relation to the face recognition problem, we formulated an example of such a usecase, the `backyard dog' problem. The `backyard dog', implemented by a lean network, should correctly identify members from a limited group of individuals, a `family', and should distinguish between them. At the same time, the network must produce an alarm to an image of an individual who is not in a member of the family. To produce such a network, we propose a shallowing algorithm. The algorithm takes an existing deep learning model on its input and outputs a shallowed version of it. The algorithm is non-iterative and is based on the Advanced Supervised Principal Component Analysis. Performance of the algorithm is assessed in exhaustive numerical experiments. In the above usecase, the `backyard dog' problem, the method is capable of drastically reducing the depth of deep learning neural networks, albeit at the cost of mild performance deterioration. We developed a simple non-iterative method for shallowing down pre-trained deep networks. The method is generic in the sense that it applies to a broad class of feed-forward networks, and is based on the Advanced Supervise Principal Component Analysis. The method enables generation of families of smaller-size shallower specialized networks tuned for specific operational conditions and tasks from a single larger and more universal legacy network.
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Venue: arXiv cs.RO 2019
Year: 2019

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.
Scheduling wireless links in the physical interference model by fractional edge coloring
Venue: arXiv cs.NI 2019
Year: 2019

We consider the problem of scheduling the links of wireless mesh networks for capacity maximization in the physical interference model. We represent such a network by an undirected graph $G$, with vertices standing for network nodes and edges for links. We define network capacity to be $1/χ'^*_\mathrm{phys}(G)$, where $χ'^*_\mathrm{phys}(G)$ is a novel edge-chromatic indicator of $G$, one that modifies the notion of $G$'s fractional chromatic index. This index asks that the edges of $G$ be covered by matchings in a certain optimal way. The new indicator does the same, but requires additionally that the matchings used be all feasible in the sense of the physical interference model. Sometimes the resulting optimal covering of $G$'s edge set by feasible matchings is simply a partition of the edge set. In such cases, the index $χ'^*_\mathrm{phys}(G)$ becomes the particular case that we denote by $χ'_\mathrm{phys}(G)$, a similar modification of $G$'s well-known chromatic index. We formulate the exact computation of $χ'^*_\mathrm{phys}(G)$ as a linear programming problem, which we solve for an extensive collection of random geometric graphs used to instantiate networks in the physical interference model. We have found that, depending on node density (number of nodes per unit deployment area), often $G$ is such that $χ'^*_\mathrm{phys}(G)<χ'_\mathrm{phys}(G)$. This bespeaks the possibility of increased network capacity by virtue of simply defining it so that edges are colored in the fractional, rather than the integer, sense.
Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images
Venue: arXiv cs.CV 2019
Year: 2019

Recent research has explored the possibility of automatically deducing information such as gender, age and race of an individual from their biometric data. While the face modality has been extensively studied in this regard, the iris modality less so. In this paper, we first review the medical literature to establish a biological basis for extracting gender and race cues from the iris. Then, we demonstrate that it is possible to use simple texture descriptors, like BSIF (Binarized Statistical Image Feature) and LBP (Local Binary Patterns), to extract gender and race attributes from an NIR ocular image used in a typical iris recognition system. The proposed method predicts gender and race from a single eye image with an accuracy of 86% and 90%, respectively. In addition, the following analysis are conducted: (a) the role of different parts of the ocular region on attribute prediction; (b) the influence of gender on race prediction, and vice-versa; (c) the impact of eye color on gender and race prediction; (d) the impact of image blur on gender and race prediction; (e) the generalizability of the method across different datasets; and (f) the consistency of prediction performance across the left and right eyes.
Integration in Social Networks
Venue: arXiv cs.SI 2019
Year: 2019

We propose the notion of $k$-integration as a measure of equality of opportunity in social networks. A social network is $k$-integrated if there is a path of length at most $k$ between any two individuals, thus guaranteeing that everybody has the same network opportunities to find a job, a romantic partner, or valuable information. We compute the minimum number of bridges (i.e. edges between nodes belonging to different components) or central nodes (those which are endpoints to a bridge) required to ensure $k$-integration. The answer depends only linearly on the size of each component for $k=2$, and does not depend on the size of each component for $k \geq 3$. Our findings provide a simple and intuitive way to compare the equality of opportunity of real-life social networks.
Distributed Monitoring of Election Winners
Venue: arXiv cs.GT 2019
Year: 2019

We consider distributed elections, where there is a center and $k$ sites. In such distributed elections, each voter has preferences over some set of candidates, and each voter is assigned to exactly one site such that each site is aware only of the voters assigned to it. The center is able to directly communicate with all sites. We are interested in designing communication-efficient protocols, allowing the center to maintain a candidate which, with arbitrarily high probability, is guaranteed to be a winner, or at least close to being a winner. We consider various single-winner voting rules, such as variants of Approval voting and scoring rules, tournament-based voting rules, and several round-based voting rules. For the voting rules we consider, we show that, using communication which is logarithmic in the number of voters, it is possible for the center to maintain such approximate winners; that is, upon a query at any time the center can immediately return a candidate which is guaranteed to be an approximate winner with high probability. We complement our protocols with lower bounds. Our results are theoretical in nature and relate to various scenarios, such as aggregating customer preferences in online shopping websites or supermarket chains and collecting votes from different polling stations of political elections.
Wavelet Decomposition of Gradient Boosting
Venue: arXiv cs.LG 2019
Year: 2019

In this paper we introduce a significant improvement to the popular tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition of the trees. This approach is based on harmonic analysis and approximation theoretical elements, and as we show through extensive experimentation, our wavelet based method generally outperforms existing methods, particularly in difficult scenarios of class unbalance and mislabeling in the training data.