OpenRaiser/Intern-Atlas huggingface dataset:
Targeting Infeasibility Questions on Obfuscated Codes
Venue: arXiv cs.CR 2016
Year: 2016

Software deobfuscation is a crucial activity in security analysis and especially, in malware analysis. While standard static and dynamic approaches suffer from well-known shortcomings, Dynamic Symbolic Execution (DSE) has recently been proposed has an interesting alternative, more robust than static analysis and more complete than dynamic analysis. Yet, DSE addresses certain kinds of questions encountered by a reverser namely feasibility questions. Many issues arising during reverse, e.g. detecting protection schemes such as opaque predicates fall into the category of infeasibility questions. In this article, we present the Backward-Bounded DSE, a generic, precise, efficient and robust method for solving infeasibility questions. We demonstrate the benefit of the method for opaque predicates and call stack tampering, and give some insight for its usage for some other protection schemes. Especially, the technique has successfully been used on state-of-the-art packers as well as on the government-grade X-Tunnel malware -- allowing its entire deobfuscation. Backward-Bounded DSE does not supersede existing DSE approaches, but rather complements them by addressing infeasibility questions in a scalable and precise manner. Following this line, we propose sparse disassembly, a combination of Backward-Bounded DSE and static disassembly able to enlarge dynamic disassembly in a guaranteed way, hence getting the best of dynamic and static disassembly. This work paves the way for robust, efficient and precise disassembly tools for heavily-obfuscated binaries.
Backdoors to Tractable Valued CSP
Venue: arXiv cs.DS 2016
Year: 2016

We extend the notion of a strong backdoor from the CSP setting to the Valued CSP setting (VCSP, for short). This provides a means for augmenting a class of tractable VCSP instances to instances that are outside the class but of small distance to the class, where the distance is measured in terms of the size of a smallest backdoor. We establish that VCSP is fixed-parameter tractable when parameterized by the size of a smallest backdoor into every tractable class of VCSP instances characterized by a (possibly infinite) tractable valued constraint language of finite arity and finite domain. We further extend this fixed-parameter tractability result to so-called "scattered classes" of VCSP instances where each connected component may belong to a different tractable class.
Fast Matrix Multiplication and Symbolic Computation
Venue: arXiv cs.SC 2016
Year: 2016

The complexity of matrix multiplication (hereafter MM) has been intensively studied since 1969, when Strassen surprisingly decreased the exponent 3 in the cubic cost of the straightforward classical MM to log 2 (7) $\approx$ 2.8074. Applications to some fundamental problems of Linear Algebra and Computer Science have been immediately recognized, but the researchers in Computer Algebra keep discovering more and more applications even today, with no sign of slowdown. We survey the unfinished history of decreasing the exponent towards its information lower bound 2, recall some important techniques discovered in this process and linked to other fields of computing, reveal sample surprising applications to fast computation of the inner products of two vectors and summation of integers, and discuss the curse of recursion, which separates the progress in fast MM into its most acclaimed and purely theoretical part and into valuable acceleration of MM of feasible sizes. Then, in the second part of our paper, we cover fast MM in realistic symbolic computations and discuss applications and implementation of fast exact matrix multiplication. We first review how most of exact linear algebra can be reduced to matrix multiplication over small finite fields. Then we highlight the differences in the design of approximate and exact implementations of fast MM, taking into account nowadays processor and memory hierarchies. In the concluding section we comment on current perspectives of the study of fast MM.
Autonomous Localization and Mapping Using a Single Mobile Device
Venue: arXiv cs.RO 2016
Year: 2016

This paper considers the problem of simultaneous 2-D room shape reconstruction and self-localization without the requirement of any pre-established infrastructure. A mobile device equipped with co-located microphone and loudspeaker as well as internal motion sensors is used to emit acoustic pulses and collect echoes reflected by the walls. Using only first order echoes, room shape recovery and self-localization is feasible when auxiliary information is obtained using motion sensors. In particular, it is established that using echoes collected at three measurement locations and the two distances between consecutive measurement points, unique localization and mapping can be achieved provided that the three measurement points are not collinear. Practical algorithms for room shape reconstruction and self-localization in the presence of noise and higher order echoes are proposed along with experimental results to demonstrate the effectiveness of the proposed approach.
Partitioning, Indexing and Querying Spatial Data on Cloud
Venue: arXiv cs.DB 2016
Year: 2016

The number of mobile devices (e.g., smartphones, wearable technologies) is rapidly growing. In line with this trend, a massive amount of spatial data is being collected since these devices allow users to geo-tag user-generated content. Clearly, a scalable computing infrastructure is needed to manage such large datasets. Meanwhile, Cloud Computing service providers (e.g., Amazon, Google, and Microsoft) allow users to lease computing resources. However, most of the existing spatial indexing techniques are designed for the centralized paradigm which is limited to the capabilities of a single sever. To address the scalability shortcomings of existing approaches, we provide a study that focus on generating a distributed spatial index structure that not only scales out to multiple servers but also scales up since it fully exploits the multi-core CPUs available on each server using Voronoi diagram as the partitioning and indexing technique which we also use to process spatial queries effectively. More specifically, since the data objects continuously move and issue position updates to the index structure, we collect the latest positions of objects and periodically generate a read-only index to eliminate costly distributed updates. Our approach scales near-linearly in index construction and query processing, and can efficiently construct an index for millions of objects within a few seconds. In addition to scalability and efficiency, we also aim to maximize the server utilization that can support the same workload with less number of servers. Server utilization is a crucial point while using Cloud Computing because users are charged based on the total amount of time they reserve each server, with no consideration of utilization.
A Characterization of Constant-Sample Testable Properties
Venue: arXiv cs.DS 2016
Year: 2016

We characterize the set of properties of Boolean-valued functions on a finite domain $\mathcal{X}$ that are testable with a constant number of samples. Specifically, we show that a property $\mathcal{P}$ is testable with a constant number of samples if and only if it is (essentially) a $k$-part symmetric property for some constant $k$, where a property is {\em $k$-part symmetric} if there is a partition $S_1,\ldots,S_k$ of $\mathcal{X}$ such that whether $f:\mathcal{X} \to \{0,1\}$ satisfies the property is determined solely by the densities of $f$ on $S_1,\ldots,S_k$. We use this characterization to obtain a number of corollaries, namely: (i) A graph property $\mathcal{P}$ is testable with a constant number of samples if and only if whether a graph $G$ satisfies $\mathcal{P}$ is (essentially) determined by the edge density of $G$. (ii) An affine-invariant property $\mathcal{P}$ of functions $f:\mathbb{F}_p^n \to \{0,1\}$ is testable with a constant number of samples if and only if whether $f$ satisfies $\mathcal{P}$ is (essentially) determined by the density of $f$. (iii) For every constant $d \geq 1$, monotonicity of functions $f : [n]^d \to \{0, 1\}$ on the $d$-dimensional hypergrid is testable with a constant number of samples.
Distributed Detection of a Non-cooperative Target via Generalized Locally-optimum Approaches
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an unknown random signal with amplitude attenuation depending on the distance between the sensor and the target (unknown) positions, embedded in white Gaussian noise. The Fusion Center (FC) receives sensors decisions through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a (potentially) more-accurate global decision. The resulting problem is a one-sided testing with nuisance parameters present only under the target-present hypothesis. We first focus on fusion rules based on Generalized Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at reducing the computational complexity, we develop fusion rules based on generalizations of the well-known Locally-Optimum Detection (LOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity.
On the Hierarchies for Deterministic, Nondeterministic and Probabilistic Ordered Read-k-times Branching Programs
Venue: arXiv cs.CC 2016
Year: 2016

The paper examines hierarchies for nondeterministic and deterministic ordered read-$k$-times Branching programs. The currently known hierarchies for deterministic $k$-OBDD models of Branching programs for $ k=o(n^{1/2}/\log^{3/2}n)$ are proved by B. Bollig, M. Sauerhoff, D. Sieling, and I. Wegener in 1998. Their lower bound technique was based on communication complexity approach. For nondeterministic $k$-OBDD it is known that, if $k$ is constant then polynomial size $k$-OBDD computes same functions as polynomial size OBDD (The result of Brosenne, Homeister and Waack, 2006). In the same time currently known hierarchies for nondeterministic read $k$-times Branching programs for $k=o(\sqrt{\log{n}}/\log\log{n})$ are proved by Okolnishnikova in 1997, and for probabilistic read $k$-times Branching programs for $k\leq \log n/3$ are proved by Hromkovic and Saurhoff in 2003. We show that increasing $k$ for polynomial size nodeterministic $k$-OBDD makes model more powerful if $k$ is not constant. Moreover, we extend the hierarchy for probabilistic and nondeterministic $k$-OBDDs for $ k=o(n/ \log n)$. These results extends hierarchies for read $k$-times Branching programs, but $k$-OBDD has more regular structure. The lower bound techniques we propose are a "functional description" of Boolean function presented by nondeterministic $k$-OBDD and communication complexity technique. We present similar hierarchies for superpolynomial and subexponential width nondeterministic $k$-OBDDs. Additionally we expand the hierarchies for deterministic $k$-OBDDs using our lower bounds for $ k=o(n/ \log n)$. We also analyze similar hierarchies for superpolynomial and subexponential width $k$-OBDDs.
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
Venue: arXiv cs.CV 2016
Year: 2016

The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet. In consequence, we need curious and continuously learning algorithms that actively acquire knowledge about semantic concepts which are present in available unlabeled data. As a step towards this goal, we show how to perform continuous active learning and exploration, where an algorithm actively selects relevant batches of unlabeled examples for annotation. These examples could either belong to already known or to yet undiscovered classes. Our algorithm is based on a new generalization of the Expected Model Output Change principle for deep architectures and is especially tailored to deep neural networks. Furthermore, we show easy-to-implement approximations that yield efficient techniques for active selection. Empirical experiments show that our method outperforms currently used heuristics.
Domain specialization: a post-training domain adaptation for Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Computer Assisted Translation (CAT). With Neural Machine Translation (NMT), we introduce a new notion of domain adaptation that we call "specialization" and which is showing promising results both in the learning speed and in adaptation accuracy. In this paper, we propose to explore this approach under several perspectives.
A recurrent neural network without chaos
Venue: arXiv cs.NE 2016
Year: 2016

We introduce an exceptionally simple gated recurrent neural network (RNN) that achieves performance comparable to well-known gated architectures, such as LSTMs and GRUs, on the word-level language modeling task. We prove that our model has simple, predicable and non-chaotic dynamics. This stands in stark contrast to more standard gated architectures, whose underlying dynamical systems exhibit chaotic behavior.
Handwritten Signature Verification Using Hand-Worn Devices
Venue: arXiv cs.CR 2016
Year: 2016

Online signature verification technologies, such as those available in banks and post offices, rely on dedicated digital devices such as tablets or smart pens to capture, analyze and verify signatures. In this paper, we suggest a novel method for online signature verification that relies on the increasingly available hand-worn devices, such as smartwatches or fitness trackers, instead of dedicated ad-hoc devices. Our method uses a set of known genuine and forged signatures, recorded using the motion sensors of a hand-worn device, to train a machine learning classifier. Then, given the recording of an unknown signature and a claimed identity, the classifier can determine whether the signature is genuine or forged. In order to validate our method, it was applied on 1980 recordings of genuine and forged signatures that we collected from 66 subjects in our institution. Using our method, we were able to successfully distinguish between genuine and forged signatures with a high degree of accuracy (0.98 AUC and 0.05 EER).
Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs
Venue: arXiv cs.AI 2016
Year: 2016

We investigate solving discrete optimisation problems using the estimation of distribution (EDA) approach via a novel combination of deep belief networks(DBN) and inductive logic programming (ILP).While DBNs are used to learn the structure of successively better feasible solutions,ILP enables the incorporation of domain-based background knowledge related to the goodness of solutions.Recent work showed that ILP could be an effective way to use domain knowledge in an EDA scenario.However,in a purely ILP-based EDA,sampling successive populations is either inefficient or not straightforward.In our Neuro-symbolic EDA,an ILP engine is used to construct a model for good solutions using domain-based background knowledge.These rules are introduced as Boolean features in the last hidden layer of DBNs used for EDA-based optimization.This incorporation of logical ILP features requires some changes while training and sampling from DBNs: (a)our DBNs need to be trained with data for units at the input layer as well as some units in an otherwise hidden layer, and (b)we would like the samples generated to be drawn from instances entailed by the logical model.We demonstrate the viability of our approach on instances of two optimisation problems: predicting optimal depth-of-win for the KRK endgame,and jobshop scheduling.Our results are promising: (i)On each iteration of distribution estimation,samples obtained with an ILP-assisted DBN have a substantially greater proportion of good solutions than samples generated using a DBN without ILP features, and (ii)On termination of distribution estimation,samples obtained using an ILP-assisted DBN contain more near-optimal samples than samples from a DBN without ILP features.These results suggest that the use of ILP-constructed theories could be useful for incorporating complex domain-knowledge into deep models for estimation of distribution based procedures.
Action-Driven Object Detection with Top-Down Visual Attentions
Venue: arXiv cs.CV 2016
Year: 2016

A dominant paradigm for deep learning based object detection relies on a "bottom-up" approach using "passive" scoring of class agnostic proposals. These approaches are efficient but lack of holistic analysis of scene-level context. In this paper, we present an "action-driven" detection mechanism using our "top-down" visual attention model. We localize an object by taking sequential actions that the attention model provides. The attention model conditioned with an image region provides required actions to get closer toward a target object. An action at each time step is weak itself but an ensemble of the sequential actions makes a bounding-box accurately converge to a target object boundary. This attention model we call AttentionNet is composed of a convolutional neural network. During our whole detection procedure, we only utilize the actions from a single AttentionNet without any modules for object proposals nor post bounding-box regression. We evaluate our top-down detection mechanism over the PASCAL VOC series and ILSVRC CLS-LOC dataset, and achieve state-of-the-art performances compared to the major bottom-up detection methods. In particular, our detection mechanism shows a strong advantage in elaborate localization by outperforming Faster R-CNN with a margin of +7.1% over PASCAL VOC 2007 when we increase the IoU threshold for positive detection to 0.7.
Video Stream Retrieval of Unseen Queries using Semantic Memory
Venue: arXiv cs.IR 2016
Year: 2016

Retrieval of live, user-broadcast video streams is an under-addressed and increasingly relevant challenge. The on-line nature of the problem requires temporal evaluation and the unforeseeable scope of potential queries motivates an approach which can accommodate arbitrary search queries. To account for the breadth of possible queries, we adopt a no-example approach to query retrieval, which uses a query's semantic relatedness to pre-trained concept classifiers. To adapt to shifting video content, we propose memory pooling and memory welling methods that favor recent information over long past content. We identify two stream retrieval tasks, instantaneous retrieval at any particular time and continuous retrieval over a prolonged duration, and propose means for evaluating them. Three large scale video datasets are adapted to the challenge of stream retrieval. We report results for our search methods on the new stream retrieval tasks, as well as demonstrate their efficacy in a traditional, non-streaming video task.
Optimal Containment of Epidemics in Temporal and Adaptive Networks
Venue: arXiv cs.SI 2016
Year: 2016

In this chapter, we focus on the problem of containing the spread of diseases taking place in both temporal and adaptive networks (i.e., networks whose structure `adapts' to the state of the disease). We specifically focus on the problem of finding the optimal allocation of containment resources (e.g., vaccines, medical personnel, traffic control resources, etc.) to eradicate epidemic outbreaks over the following three models of temporal and adaptive networks: (i) Markovian temporal networks, (ii) aggregated-Markovian temporal networks, and (iii) stochastically adaptive models. For each model, we present a rigorous and tractable mathematical framework to efficiently find the optimal distribution of control resources to eliminate the disease. In contrast with other existing results, our results are not based on heuristic control strategies, but on a disciplined analysis using tools from dynamical systems and convex optimization.
Fast Domain Adaptation for Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

Neural Machine Translation (NMT) is a new approach for automatic translation of text from one human language into another. The basic concept in NMT is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is gaining popularity in the research community because it outperformed traditional SMT approaches in several translation tasks at WMT and other evaluation tasks/benchmarks at least for some language pairs. However, many of the enhancements in SMT over the years have not been incorporated into the NMT framework. In this paper, we focus on one such enhancement namely domain adaptation. We propose an approach for adapting a NMT system to a new domain. The main idea behind domain adaptation is that the availability of large out-of-domain training data and a small in-domain training data. We report significant gains with our proposed method in both automatic metrics and a human subjective evaluation metric on two language pairs. With our adaptation method, we show large improvement on the new domain while the performance of our general domain only degrades slightly. In addition, our approach is fast enough to adapt an already trained system to a new domain within few hours without the need to retrain the NMT model on the combined data which usually takes several days/weeks depending on the volume of the data.
Relieving Core Routers from Dynamic Routing with off-the-shelf Equipment and Protocols
Venue: arXiv cs.NI 2016
Year: 2016

To answer traffic engineering goals, current backbone networks use expensive and sophisticated equipments, that run distributed algorithms to imple- ment dynamic multi-path routing (e.g., MPLS tunnels and dynamic trunk rerout- ing). We think that the same goals can be fulfilled using a simpler approach, where the core of the backbone only implements many a priori computed paths, and most adaptation to traffic engineering goals only takes place at the edge of the network. In the vein of Software Defined Networking, edge adaptation should be driven by a logically centralized controller that leverages the available paths to adapt traffic load balancing to the current demands and network status. In this article we present two algorithms to help building this vision. The first one selects sets of paths able to support future load balancing needs and adaptation to network faults. As the total number of required paths is very important, and their continuous availability requires many FIB entries in core routers, we also present a second algorithm that aggregates these paths in a reduced number of trees. This second algorithm achieves better results than previously proposed algorithms for path aggregation. To conclude, we show that off-the-shelf equipment supporting simple protocols may be used to implement routing with these trees, what shows that simplicity in the core can be achieved by using only trivially available proto- cols and their most common and unsophisticated implementation