OpenRaiser/Intern-Atlas huggingface dataset:
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Venue: arXiv cs.LG 2019
Year: 2019

Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current methods often fail to efficiently learn policies. We thus investigate the presence of a common weakness in single-agent RL, namely value function overestimation bias, in the multi-agent setting. Based on our findings, we propose an approach that reduces this bias by using double centralized critics. We evaluate it on six mixed cooperative-competitive tasks, showing a significant advantage over current methods. Finally, we investigate the application of multi-agent methods to high-dimensional robotic tasks and show that our approach can be used to learn decentralized policies in this domain.
Complex networks based word embeddings
Venue: arXiv cs.CL 2019
Year: 2019

Most of the time, the first step to learn word embeddings is to build a word co-occurrence matrix. As such matrices are equivalent to graphs, complex networks theory can naturally be used to deal with such data. In this paper, we consider applying community detection, a main tool of this field, to the co-occurrence matrix corresponding to a huge corpus. Community structure is used as a way to reduce the dimensionality of the initial space. Using this community structure, we propose a method to extract word embeddings that are comparable to the state-of-the-art approaches.
A Look at the Dark Side of Hardware Reverse Engineering -- A Case Study
Venue: arXiv cs.CR 2019
Year: 2019

A massive threat to the modern and complex IC production chain is the use of untrusted off-shore foundries which are able to infringe valuable hardware design IP or to inject hardware Trojans causing severe loss of safety and security. Similarly, market dominating SRAM-based FPGAs are vulnerable to both attacks since the crucial gate-level netlist can be retrieved even in field for the majority of deployed device series. In order to perform IP infringement or Trojan injection, reverse engineering (parts of) the hardware design is necessary to understand its internal workings. Even though IP protection and obfuscation techniques exist to hinder both attacks, the security of most techniques is doubtful since realistic capabilities of reverse engineering are often neglected. The contribution of our work is twofold: first, we carefully review an IP watermarking scheme tailored to FPGAs and improve its security by using opaque predicates. In addition, we show novel reverse engineering strategies on proposed opaque predicate implementations that again enables to automatically detect and alter watermarks. Second, we demonstrate automatic injection of hardware Trojans specifically tailored for third-party cryptographic IP gate-level netlists. More precisely, we extend our understanding of adversary's capabilities by presenting how block and stream cipher implementations can be surreptitiously weakened.
iVRNote: Design, Creation and Evaluation of an Interactive Note-Taking Interface for Study and Reflection in VR Learning Environments
Venue: arXiv cs.HC 2019
Year: 2019

In this contribution, we design, implement and evaluate the pedagogical benefits of a novel interactive note taking interface (iVRNote) in VR for the purpose of learning and reflection lectures. In future VR learning environments, students would have challenges in taking notes when they wear a head mounted display (HMD). To solve this problem, we installed a digital tablet on the desk and provided several tools in VR to facilitate the learning experience. Specifically, we track the stylus position and orientation in the physical world and then render a virtual stylus in VR. In other words, when students see a virtual stylus somewhere on the desk, they can reach out with their hand for the physical stylus. The information provided will also enable them to know where they will draw or write before the stylus touches the tablet. Since the presented iVRNote featuring our note taking system is a digital environment, we also enable students save efforts in taking extensive notes by providing several functions, such as post-editing and picture taking, so that they can pay more attention to lectures in VR. We also record the time of each stroke on the note to help students review a lecture. They can select a part of their note to revisit the corresponding segment in a virtual online lecture. Figures and the accompanying video demonstrate the feasibility of the presented iVRNote system. To evaluate the system, we conducted a user study with 20 participants to assess the preference and pedagogical benefits of the iVRNote interface.
SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge
Venue: arXiv cs.NI 2019
Year: 2019

In IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy.
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
Venue: arXiv cs.LG 2019
Year: 2019

A key element of understanding the efficacy of overparameterized neural networks is characterizing how they represent functions as the number of weights in the network approaches infinity. In this paper, we characterize the norm required to realize a function $f:\mathbb{R}^d\rightarrow\mathbb{R}$ as a single hidden-layer ReLU network with an unbounded number of units (infinite width), but where the Euclidean norm of the weights is bounded, including precisely characterizing which functions can be realized with finite norm. This was settled for univariate univariate functions in Savarese et al. (2019), where it was shown that the required norm is determined by the L1-norm of the second derivative of the function. We extend the characterization to multivariate functions (i.e., networks with d input units), relating the required norm to the L1-norm of the Radon transform of a (d+1)/2-power Laplacian of the function. This characterization allows us to show that all functions in Sobolev spaces $W^{s,1}(\mathbb{R})$, $s\geq d+1$, can be represented with bounded norm, to calculate the required norm for several specific functions, and to obtain a depth separation result. These results have important implications for understanding generalization performance and the distinction between neural networks and more traditional kernel learning.
Benchmarking Batch Deep Reinforcement Learning Algorithms
Venue: arXiv cs.LG 2019
Year: 2019

Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment. Following this result, there have been several papers showing reasonable performances under a variety of environments and batch settings. In this paper, we benchmark the performance of recent off-policy and batch reinforcement learning algorithms under unified settings on the Atari domain, with data generated by a single partially-trained behavioral policy. We find that under these conditions, many of these algorithms underperform DQN trained online with the same amount of data, as well as the partially-trained behavioral policy. To introduce a strong baseline, we adapt the Batch-Constrained Q-learning algorithm to a discrete-action setting, and show it outperforms all existing algorithms at this task.
Semi-Supervised Generative Modeling for Controllable Speech Synthesis
Venue: arXiv cs.CL 2019
Year: 2019

We present a novel generative model that combines state-of-the-art neural text-to-speech (TTS) with semi-supervised probabilistic latent variable models. By providing partial supervision to some of the latent variables, we are able to force them to take on consistent and interpretable purposes, which previously hasn't been possible with purely unsupervised TTS models. We demonstrate that our model is able to reliably discover and control important but rarely labelled attributes of speech, such as affect and speaking rate, with as little as 1% (30 minutes) supervision. Even at such low supervision levels we do not observe a degradation of synthesis quality compared to a state-of-the-art baseline. Audio samples are available on the web.
False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics
Venue: arXiv cs.LG 2019
Year: 2019

Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. To date, the majority of the published literature focuses on the accuracy of DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. The obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model two different types of false data injection attacks (FDIA) on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. The obtained results demonstrate that FDI attacks on even a few IoT sensors can strongly defect the RUL prediction. However, the GRU-based PdM model performs better in terms of accuracy and resiliency. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths. Our experiments reveal an interesting relationship between the accuracy, resiliency and sequence length for the GRU-based PdM models.
GmCN: Graph Mask Convolutional Network
Venue: arXiv cs.CV 2019
Year: 2019

Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks. Existing GCNs usually conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors which is biased by its own representation. However, this fixed aggregation strategy is not guaranteed to be optimal for GCN based graph learning and also can be affected by some graph structure noises, such as incorrect or undesired edge connections. To address these issues, we propose a novel Graph mask Convolutional Network (GmCN) in which nodes can adaptively select the optimal neighbors in their feature aggregation to better serve GCN learning. GmCN can be theoretically interpreted by a regularization framework, based on which we derive a simple update algorithm to determine the optimal mask adaptively in GmCN training process. Experiments on several datasets validate the effectiveness of GmCN.
Finding monotone patterns in sublinear time
Venue: arXiv cs.DS 2019
Year: 2019

We study the problem of finding monotone subsequences in an array from the viewpoint of sublinear algorithms. For fixed $k \in \mathbb{N}$ and $\varepsilon > 0$, we show that the non-adaptive query complexity of finding a length-$k$ monotone subsequence of $f \colon [n] \to \mathbb{R}$, assuming that $f$ is $\varepsilon$-far from free of such subsequences, is $Θ((\log n)^{\lfloor \log_2 k \rfloor})$. Prior to our work, the best algorithm for this problem, due to Newman, Rabinovich, Rajendraprasad, and Sohler (2017), made $(\log n)^{O(k^2)}$ non-adaptive queries; and the only lower bound known, of $Ω(\log n)$ queries for the case $k = 2$, followed from that on testing monotonicity due to Ergün, Kannan, Kumar, Rubinfeld, and Viswanathan (2000) and Fischer (2004).
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances
Venue: arXiv cs.CV 2019
Year: 2019

Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression problem, requiring annotated labels as supervision at training time, in the past few years a substantial amount of work has been done in self-supervised depth training based on strong geometric cues, both from stereo cameras and more recently from monocular video sequences. In this paper we investigate how these two approaches (supervised & self-supervised) can be effectively combined, so that a depth model can learn to encode true scale from sparse supervision while achieving high fidelity local accuracy by leveraging geometric cues. To this end, we propose a novel supervised loss term that complements the widely used photometric loss, and show how it can be used to train robust semi-supervised monocular depth estimation models. Furthermore, we evaluate how much supervision is actually necessary to train accurate scale-aware monocular depth models, showing that with our proposed framework, very sparse LiDAR information, with as few as 4 beams (less than 100 valid depth values per image), is enough to achieve results competitive with the current state-of-the-art.
Multi-level Gated Recurrent Neural Network for Dialog Act Classification
Venue: arXiv cs.CL 2019
Year: 2019

In this paper we focus on the problem of dialog act (DA) labelling. This problem has recently attracted a lot of attention as it is an important sub-part of an automatic question answering system, which is currently in great demand. Traditional methods tend to see this problem as a sequence labelling task and deals with it by applying classifiers with rich features. Most of the current neural network models still omit the sequential information in the conversation. Henceforth, we apply a novel multi-level gated recurrent neural network (GRNN) with non-textual information to predict the DA tag. Our model not only utilizes textual information, but also makes use of non-textual and contextual information. In comparison, our model has shown significant improvement over previous works on Switchboard Dialog Act (SWDA) task by over 6%.
Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph
Venue: arXiv cs.LG 2019
Year: 2019

With the increasing number of deep learning applications, there is a growing demand for explanations. Visual explanations provide information about which parts of an image are relevant for a classifier's decision. However, highlighting of image parts (e.g., an eye) cannot capture the relevance of a specific feature value for a class (e.g., that the eye is wide open). Furthermore, highlighting cannot convey whether the classification depends on the mere presence of parts or on a specific spatial relation between them. Consequently, we present an approach that is capable of explaining a classifier's decision in terms of logic rules obtained by the Inductive Logic Programming system Aleph. The examples and the background knowledge needed for Aleph are based on the explanation generation method LIME. We demonstrate our approach with images of a blocksworld domain. First, we show that our approach is capable of identifying a single relation as important explanatory construct. Afterwards, we present the more complex relational concept of towers. Finally, we show how the generated relational rules can be explicitly related with the input image, resulting in richer explanations.
Modeling Confidence in Sequence-to-Sequence Models
Venue: arXiv cs.CL 2019
Year: 2019

Recently, significant improvements have been achieved in various natural language processing tasks using neural sequence-to-sequence models. While aiming for the best generation quality is important, ultimately it is also necessary to develop models that can assess the quality of their output. In this work, we propose to use the similarity between training and test conditions as a measure for models' confidence. We investigate methods solely using the similarity as well as methods combining it with the posterior probability. While traditionally only target tokens are annotated with confidence measures, we also investigate methods to annotate source tokens with confidence. By learning an internal alignment model, we can significantly improve confidence projection over using state-of-the-art external alignment tools. We evaluate the proposed methods on downstream confidence estimation for machine translation (MT). We show improvements on segment-level confidence estimation as well as on confidence estimation for source tokens. In addition, we show that the same methods can also be applied to other tasks using sequence-to-sequence models. On the automatic speech recognition (ASR) task, we are able to find 60% of the errors by looking at 20% of the data.
PINFER: Privacy-Preserving Inference for Machine Learning
Venue: arXiv cs.CR 2019
Year: 2019

The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. Several technical solutions are being proposed to address the issue. Hardware security modules in cloud data centres appear limited to enterprise customers due to their complexity, while general multi-party computation techniques require a large number of message exchanges. This paper proposes a variety of protocols for privacy-preserving regression and classification that (i) only require additively homomorphic encryption algorithms, (ii) limit interactions to a mere request and response, and (iii) that can be used directly for important machine-learning algorithms such as logistic regression and SVM classification. The basic protocols are then extended and applied to feed-forward neural networks.
FPT Inapproximability of Directed Cut and Connectivity Problems
Venue: arXiv cs.DS 2019
Year: 2019

(see paper for full abstract) Cut problems and connectivity problems on digraphs are two well-studied classes of problems from the viewpoint of parameterized complexity. After a series of papers over the last decade, we now have (almost) tight bounds for the running time of several standard variants of these problems parameterized by two parameters: the number $k$ of terminals and the size $p$ of the solution. When there is evidence of FPT intractability, then the next natural alternative is to consider FPT approximations. In this paper, we show two types of results for several directed cut and connectivity problems, building on existing results from the literature: first is to circumvent the hardness results for these problems by designing FPT approximation algorithms, or alternatively strengthen the existing hardness results by creating "gap-instances" under stronger hypotheses such as the (Gap-)Exponential Time Hypothesis (ETH).
The Thing With E.coli: Highlighting Opportunities and Challenges of Integrating Bacteria in IoT and HCI
Venue: arXiv cs.HC 2019
Year: 2019

With advances in nano- and biotechnology, bacteria are receiving increasing attention in scientific research as a potential substrate for Internet of Bio-Nano Things (IoBNT), which involve networking and communication through nanoscale and biological entities. Harnessing the special features of bacteria, including an ability to become autonomous - helped by an embedded, natural propeller motor - the microbes show promising array of application in healthcare and environmental health. In this paper, we briefly outline significant features of bacteria that allow analogies between them and traditional computerized IoT device to be made. We argue that such comparisons are critical in terms of helping researchers to explore human-bacteria interaction in the context of IoT and HCI. Furthermore, we highlight the current lack of tangible infrastructure for researchers in IoT and HCI to access and experiment with bacteria. As a potential solution, we propose to utilize the DIY biology movement and gamification techniques to leverage user engagement and introduction to bacteria.
Detecting Deception in Political Debates Using Acoustic and Textual Features
Venue: arXiv cs.CL 2019
Year: 2019

We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates. Thanks to the efforts of fact-checking organizations, it is possible to obtain annotations for statements in the context of a political discourse as true, half-true, or false. Starting with such data from the CLEF-2018 CheckThat! Lab, which was limited to text, we performed alignment to the corresponding videos, thus producing a multimodal dataset. We further developed a multimodal deep-learning architecture for the task of deception detection, which yielded sizable improvements over the state of the art for the CLEF-2018 Lab task 2. Our experiments show that the use of the acoustic signal consistently helped to improve the performance compared to using textual and metadata features only, based on several different evaluation measures. We release the new dataset to the research community, hoping to help advance the overall field of multimodal deception detection.