OpenRaiser/Intern-Atlas huggingface dataset:
RED: Reinforced Encoder-Decoder Networks for Action Anticipation
Venue: arXiv cs.CV 2017
Year: 2017

Action anticipation aims to detect an action before it happens. Many real world applications in robotics and surveillance are related to this predictive capability. Current methods address this problem by first anticipating visual representations of future frames and then categorizing the anticipated representations to actions. However, anticipation is based on a single past frame's representation, which ignores the history trend. Besides, it can only anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED) network for action anticipation. RED takes multiple history representations as input and learns to anticipate a sequence of future representations. One salient aspect of RED is that a reinforcement module is adopted to provide sequence-level supervision; the reward function is designed to encourage the system to make correct predictions as early as possible. We test RED on TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation and achieve state-of-the-art performance on all datasets.
Divide and Conquer: Variable Set Separation in Hybrid Systems Reachability Analysis
Venue: arXiv cs.SY 2017
Year: 2017

In this paper we propose an improvement for flowpipe-construction-based reachability analysis techniques for hybrid systems. Such methods apply iterative successor computations to pave the reachable region of the state space by state sets in an over-approximative manner. As the computational costs steeply increase with the dimension, in this work we analyse the possibilities for improving scalability by dividing the search space in sub-spaces and execute reachability computations in the sub-spaces instead of the global space. We formalise such an algorithm and provide experimental evaluations to compare the efficiency as well as the precision of our sub-space search to the original search in the global space.
Tunnel Effects in Cognition: A new Mechanism for Scientific Discovery and Education
Venue: arXiv cs.AI 2017
Year: 2017

It is quite exceptional, if it ever happens, that a new conceptual domain be built from scratch. Usually, it is developed and mastered in interaction, both positive and negative, with other more operational existing domains. Few reasoning mechanisms have been proposed to account for the interplay of different conceptual domains and the transfer of information from one to another. Analogical reasoning is one, blending is another. This paper presents a new mechanism, called 'tunnel effect', that may explain, in part, how scientists and students reason while constructing a new conceptual domain. One experimental study with high school students and analyses from the history of science, particularly about the birth of classical thermodynamics, provide evidence and illustrate this mechanism. The knowledge organization, processes and conditions for its appearance are detailed and put into the perspective of a computational model. Specifically, we put forward the hypothesis that two levels of knowledge, notional and conceptual, cooperate in the scientific discovery process when a new conceptual domain is being built. The type of conceptual learning that can be associated with tunnel effect is discussed and a thorough comparison is made with analogical reasoning in order to underline the main features of the new proposed mechanism.
Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning
Venue: arXiv cs.AI 2017
Year: 2017

Management of chronic diseases such as heart failure (HF) is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing HF using answer set programming(ASP). In this paper we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.
graph2vec: Learning Distributed Representations of Graphs
Venue: arXiv cs.AI 2017
Year: 2017

Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered by problems such as poor generalization. To address this limitation, in this work, we propose a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs. graph2vec's embeddings are learnt in an unsupervised manner and are task agnostic. Hence, they could be used for any downstream task such as graph classification, clustering and even seeding supervised representation learning approaches. Our experiments on several benchmark and large real-world datasets show that graph2vec achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels.
Protecting Military Avionics Platforms from Attacks on MIL-STD-1553 Communication Bus
Venue: arXiv cs.CR 2017
Year: 2017

MIL-STD-1553 is a military standard that defines the physical and logical layers, and a command/response time division multiplexing of a communication bus used in military and aerospace avionic platforms for more than 40 years. As a legacy platform, MIL-STD-1553 was designed for high level of fault tolerance while less attention was taken with regard to security. Recent studies already addressed the impact of successful cyber attacks on aerospace vehicles that are implementing MIL-STD-1553. In this study we present a security analysis of MIL-STD-1553. In addition, we present a method for anomaly detection in MIL-STD-1553 communication bus and its performance in the presence of several attack scenarios implemented in a testbed, as well as results on real system data. Moreover, we propose a general approach towards an intrusion detection system (IDS) for a MIL-STD-1553 communication bus.
Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware
Venue: arXiv cs.CR 2017
Year: 2017

Over the last decade, malicious software (or malware, for short) has shown an increasing sophistication and proliferation, fueled by a flourishing underground economy, in response to the increasing complexity of modern defense mechanisms. PDF documents are among the major vectors used to convey malware, thanks to the flexibility of their structure and the ability of embedding different kinds of content, ranging from images to JavaScript code. Despite the numerous efforts made by the research and industrial communities, PDF malware is still one of the major threats on the cyber-security landscape. In this paper, we provide an overview of the current attack techniques used to convey PDF malware, and discuss state-of-the-art PDF malware analysis tools that provide valuable support to digital forensic investigations. We finally discuss limitations and open issues of the current defense mechanisms, and sketch some interesting future research directions.
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents the LIG-CRIStAL submission to the shared Automatic Post- Editing task of WMT 2017. We propose two neural post-editing models: a monosource model with a task-specific attention mechanism, which performs particularly well in a low-resource scenario; and a chained architecture which makes use of the source sentence to provide extra context. This latter architecture manages to slightly improve our results when more training data is available. We present and discuss our results on two datasets (en-de and de-en) that are made available for the task.
Capacitated Covering Problems in Geometric Spaces
Venue: arXiv cs.DS 2017
Year: 2017

In this article, we consider the following capacitated covering problem. We are given a set $P$ of $n$ points and a set $\mathcal{B}$ of balls from some metric space, and a positive integer $U$ that represents the capacity of each of the balls in $\mathcal{B}$. We would like to compute a subset $\mathcal{B}' \subseteq \mathcal{B}$ of balls and assign each point in $P$ to some ball in $\mathcal{B}$ that contains it, such that the number of points assigned to any ball is at most $U$. The objective function that we would like to minimize is the cardinality of $\mathcal{B}$. We consider this problem in arbitrary metric spaces as well as Euclidean spaces of constant dimension. In the metric setting, even the uncapacitated version of the problem is hard to approximate to within a logarithmic factor. In the Euclidean setting, the best known approximation guarantee in dimensions $3$ and higher is logarithmic in the number of points. Thus we focus on obtaining "bi-criteria" approximations. In particular, we are allowed to expand the balls in our solution by some factor, but optimal solutions do not have that flexibility. Our main result is that allowing constant factor expansion of the input balls suffices to obtain constant approximations for these problems. In fact, in the Euclidean setting, only $(1+ε)$ factor expansion is sufficient for any $ε> 0$, with the approximation factor being a polynomial in $1/ε$. We obtain these results using a unified scheme for rounding the natural LP relaxation; this scheme may be useful for other capacitated covering problems. We also complement these bi-criteria approximations by obtaining hardness of approximation results that shed light on our understanding of these problems.
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
Venue: arXiv cs.AI 2017
Year: 2017

AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven't yet learned to avoid actions that could cause serious harm. How can an AI system explore and learn without making a single mistake that harms humans or otherwise causes serious damage? For model-free reinforcement learning, having a human "in the loop" and ready to intervene is currently the only way to prevent all catastrophes. We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions. We evaluate this scheme on Atari games, with a Deep RL agent being overseen by a human for four hours. When the class of catastrophes is simple, we are able to prevent all catastrophes without affecting the agent's learning (whereas an RL baseline fails due to catastrophic forgetting). However, this scheme is less successful when catastrophes are more complex: it reduces but does not eliminate catastrophes and the supervised learner fails on adversarial examples found by the agent. Extrapolating to more challenging environments, we show that our implementation would not scale (due to the infeasible amount of human labor required). We outline extensions of the scheme that are necessary if we are to train model-free agents without a single catastrophe.
Auxiliary Objectives for Neural Error Detection Models
Venue: arXiv cs.CL 2017
Year: 2017

We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objectives allow it to be optimised more efficiently and achieve better performance.
Automated Synthesis of Safe and Robust PID Controllers for Stochastic Hybrid Systems
Venue: arXiv cs.SY 2017
Year: 2017

We present a new method for the automated synthesis of safe and robust Proportional-Integral-Derivative (PID) controllers for stochastic hybrid systems. Despite their widespread use in industry, no automated method currently exists for deriving a PID controller (or any other type of controller, for that matter) with safety and performance guarantees for such a general class of systems. In particular, we consider hybrid systems with nonlinear dynamics (Lipschitz-continuous ordinary differential equations) and random parameters, and we synthesize PID controllers such that the resulting closed-loop systems satisfy safety and performance constraints given as probabilistic bounded reachability properties. Our technique leverages SMT solvers over the reals and nonlinear differential equations to provide formal guarantees that the synthesized controllers satisfy such properties. These controllers are also robust by design since they minimize the probability of reaching an unsafe state in the presence of random disturbances. We apply our approach to the problem of insulin regulation for type 1 diabetes, synthesizing controllers with robust responses to large random meal disturbances, thereby enabling them to maintain blood glucose levels within healthy, safe ranges.
Computation Rate Maximization for Wireless Powered Mobile Edge Computing
Venue: arXiv cs.IT 2017
Year: 2017

Integrating mobile edge computing (MEC) and wireless power transfer (WPT) has been regarded as a promising technique to improve computation capabilities for self-sustainable Internet of Things (IoT) devices. This paper investigates a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users for mobile computing. We consider a time-division multiple access (TDMA) protocol for multiuser computation offloading. Under this setup, we aim to maximize the weighted sum of the computation rates (in terms of the number of computation bits) across all the users, by jointly optimizing the energy transmit beamformer at the AP, the task partition for the users (for local computing and offloading, respectively), and the time allocation among the users. We derive the optimal solution in a semi-closed form via convex optimization techniques. Numerical results show the merit of the proposed design over alternative benchmark schemes.
On the Pitfalls of End-to-End Encrypted Communications: A Study of Remote Key-Fingerprint Verification
Venue: arXiv cs.CR 2017
Year: 2017

Many widely used Internet messaging and calling apps, such as WhatsApp, Viber, Telegram, and Signal, have deployed an end-to-end encryption functionality. To defeat potential MITM attackers against the key exchange protocol, the approach relies on users to perform a code verification task whereby each user must compare the code (a fingerprint of the cryptographic keys) computed by her app with the one computed by the other user's app and reject the session if the two do not match. In this paper, we study the security and usability of this human-centered code verification task for a setting where the end users are remotely located, and compare it as a baseline to a less frequent scenario where the users are in close proximity. We consider several variations of the code presentation and verification methods, incorporated into representative real-world apps, including codes encoded as numbers or images, displayed on the screen, and verbally spoken by the users. We perform a human factors study in a lab setting to quantify the security and usability of these different methods. Our study results expose key weaknesses in the security and usability of the code verification methods employed in the apps. First, we show that most code verification methods offer poor security (high false accepts) and low usability (high false rejects and low user experience ratings) in the remote setting. Second, we demonstrate that, security and usability under the remote code verification setting is significantly lower than that in the proximity setting. We attribute this result to the increased cognitive overhead associated with comparing the codes across two apps on the same device (remote setting) rather than across two devices (proximity setting). Overall, our work serves to highlight a serious vulnerability of Internet-based communication apps in the remote setting stemming from human errors.
Ticker: A System for Incremental ASP-based Stream Reasoning
Venue: arXiv cs.LO 2017
Year: 2017

In complex reasoning tasks, as expressible by Answer Set Programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information. This paper introduces Ticker, a prototypical engine for well-defined logical reasoning over streaming data. Ticker builds on a practical fragment of the recent rule-based language LARS which extends Answer Set Programming for streams by providing flexible expiration control and temporal modalities. We discuss Ticker's reasoning strategies: First, the repeated one-shot solving mode calls Clingo on an ASP encoding. We show how this translation can be incrementally updated when new data is streaming in or time passes by. Based on this, we build on Doyle's classic justification-based truth maintenance system (TMS) to update models of non-stratified programs. Finally, we empirically compare the obtained evaluation mechanisms. This paper is under consideration for acceptance in TPLP.
A Flexible Approach for Finding Optimal Paths with Minimal Conflicts
Venue: arXiv cs.SE 2017
Year: 2017

Complex systems are usually modelled through a combination of structural and behavioural models, where separate behavioural models make it easier to design and understand partial behaviour. When partial models are combined, we need to guarantee that they are consistent, and several automated techniques have been developed to check this. We argue that in some cases it is impossible to guarantee total consistency, and instead we want to find execution paths across such models with minimal conflicts with respect to a certain metric of interest. We present an efficient and scalable solution to find optimal paths through a combination of the theorem prover Isabelle with the constraint solver Z3. Our approach has been inspired by a healthcare problem, namely how to detect conflicts between medications taken by patients with multiple chronic conditions, and how to find preferable alternatives automatically.
Slanted Stixels: Representing San Francisco's Steepest Streets
Venue: arXiv cs.CV 2017
Year: 2017

In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.
On Treewidth and Stable Marriage
Venue: arXiv cs.DS 2017
Year: 2017

Stable Marriage is a fundamental problem to both computer science and economics. Four well-known NP-hard optimization versions of this problem are the Sex-Equal Stable Marriage (SESM), Balanced Stable Marriage (BSM), max-Stable Marriage with Ties (max-SMT) and min-Stable Marriage with Ties (min-SMT) problems. In this paper, we analyze these problems from the viewpoint of Parameterized Complexity. We conduct the first study of these problems with respect to the parameter treewidth. First, we study the treewidth $\mathtt{tw}$ of the primal graph. We establish that all four problems are W[1]-hard. In particular, while it is easy to show that all four problems admit algorithms that run in time $n^{O(\mathtt{tw})}$, we prove that all of these algorithms are likely to be essentially optimal. Next, we study the treewidth $\mathtt{tw}$ of the rotation digraph. In this context, the max-SMT and min-SMT are not defined. For both SESM and BSM, we design (non-trivial) algorithms that run in time $2^{\mathtt{tw}}n^{O(1)}$. Then, for both SESM and BSM, we also prove that unless SETH is false, algorithms that run in time $(2-ε)^{\mathtt{tw}}n^{O(1)}$ do not exist for any fixed $ε>0$. We thus present a comprehensive, complete picture of the behavior of central optimization versions of Stable Marriage with respect to treewidth.
Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

Pairwise ranking methods are the basis of many widely used discriminative training approaches for structure prediction problems in natural language processing(NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables simple and efficient solutions. However, neglecting the global ordering of the hypothesis list may hinder learning. We propose a listwise learning framework for structure prediction problems such as machine translation. Our framework directly models the entire translation list's ordering to learn parameters which may better fit the given listwise samples. Furthermore, we propose top-rank enhanced loss functions, which are more sensitive to ranking errors at higher positions. Experiments on a large-scale Chinese-English translation task show that both our listwise learning framework and top-rank enhanced listwise losses lead to significant improvements in translation quality.