OpenRaiser/Intern-Atlas huggingface dataset:
Cooperative Planning for Fuel-constrained Aerial Vehicles and Ground-based Refueling Vehicles for Large-Scale Coverage
Venue: arXiv cs.RO 2018
Year: 2018

Low cost Unmanned Aerial Vehicles (UAVs) need multiple refuels to accomplish large area coverage. The number of refueling stations and their placement plays a vital role in determining coverage efficiency. In this paper, we propose the use of a ground-based refueling vehicle (RV) to increase the operational range of a UAV in both spatial and temporal domains. Determining optimal routes for the UAV and RV, and selecting optimized locations for refueling to aid in minimizing coverage time is a challenging problem due to different vehicle speeds, coupling between refueling location placement, and the coverage area at each location. We develop a two-stage strategy for coupled route planning for UAV and RV to perform a coverage mission. The first stage computes a minimal set of refueling sites that permit a feasible UAV route. In the second stage, multiple Mixed-Integer Linear Programming (MILP) formulations are developed to plan optimal routes for the UAV and the refueling vehicle taking into account the feasible set of refueling sites generated in stage one. The performance of different formulations is compared empirically. In addition, computationally efficient heuristics are developed to solve the routing problem. Extensive simulations are conducted to corroborate the effectiveness of proposed approaches.
Evaluation of Game Templates to support Programming Activities in Schools
Venue: arXiv cs.CY 2018
Year: 2018

Game creation challenges in schools potentially provide engaging, goal-oriented, and interactive experiences in classes; thereby supporting the transfer of knowledge for learning in a fun and pedagogic manner. A key element of the ongoing European project No One Left Behind (NOLB) is to integrate a game-making teaching framework (GMTF) into the educational app Pocket Code. Pocket Code allows learners to create programs in a visual Lego-style way to facilitate learning how to code at secondary high schools. The concept of the NOLB GMTF is based on principles of the Universal Design for Learning (UDL) model. This framework provides a coherent approach to learning and teaching by integrating leisure oriented gaming methods into multi-discipline curricula. One output of this framework is the integration of game-based methods via game templates that refer to didactical scenarios that include a refined set of genres, assets, rules, challenges, and strategies. These templates allows: 1) teachers to start with a well-structured program, and 2) pupils to add content and adjust the code to integrate their own ideas. During the project game genres such as adventure, action, and quiz, as well as rewards or victory point mechanisms, have been embedded into different subjects, e.g., science, mathematics, and arts. The insights gained during the class hours were used to generate 13 game templates, which are integrated in Create@School (a new version of the Pocket Code app which targets schools). To test the efficiency of these templates, user experience (UX) tests were conducted during classes to compare games created by pupils who used templates and those who started to create a game from scratch. Preliminary results showed that these templates allow learners to focus on subject-relevant problem solving activities rather than on understanding the functionality of the app.
Branching Temporal Logic of Calls and Returns for Pushdown Systems
Venue: arXiv cs.FL 2018
Year: 2018

Pushdown Systems (PDSs) are a natural model for sequential programs with (recursive) procedure calls. In this work, we define the Branching temporal logic of CAlls and RETurns (BCARET) that allows to write branching temporal formulas while taking into account the matching between calls and returns. We consider the model-checking problem of PDSs against BCARET formulas with "standard" valuations (where an atomic proposition holds at a configuration c or not depends only on the control state of c, not on its stack) as well as regular valuations (where the set of configurations in which an atomic proposition holds is regular). We show that these problems can be effectively solved by a reduction to the emptiness problem of Alternating Büchi Pushdown Systems. We show that our results can be applied for malware detection.
Towards Distributed Clouds
Venue: arXiv cs.DC 2018
Year: 2018

This review focuses on the evolution of cloud computing and distributed ledger technologies (blockchains) over the last decade. Cloud computing relies mainly on a conceptually centralized service provisioning model, while blockchain technologies originate from a peer-to-peer and a completely distributed approach. Still, noteworthy commonalities between both approaches are often overlooked by researchers. Therefore, to the best of the authors knowledge, this paper reviews both domains in parallel for the first time. We conclude that both approaches have advantages and disadvantages. The advantages of centralized service provisioning approaches are often the disadvantages of distributed ledger approaches and vice versa. It is obviously an interesting question whether both approaches could be combined in a way that the advantages can be added while the disadvantages could be avoided. We derive a software stack that could build the foundation unifying the best of these two worlds and that would avoid existing shortcomings like vendor lock-in, some security problems, and inherent platform dependencies.
Hindering reaction attacks by using monomial codes in the McEliece cryptosystem
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we study recent reaction attacks against QC-LDPC and QC-MDPC code-based cryptosystems, which allow an opponent to recover the private parity-check matrix through its distance spectrum by observing a sufficiently high number of decryption failures. We consider a special class of codes, known as monomial codes, to form private keys with the desirable property of having a unique and complete distance spectrum. We verify that for these codes the problem of recovering the secret key from the distance spectrum is equivalent to that of finding cliques in a graph, and use this equivalence to prove that current reaction attacks are not applicable when codes of this type are used in the McEliece cryptosystem.
Consumer Centric Data Control, Tracking and Transparency -- A Position Paper
Venue: arXiv cs.CR 2018
Year: 2018

Personal data related to a user's activities, preferences and services, is considered to be a valuable commodity not only for a wide range of technology-oriented companies like Google, Amazon and Apple but also for more traditional companies like travel/transport, banking, entertainment and marketing industry. This has resulted in more targeted and to a great extend personalised services for individuals -- in most cases at a minimal financial cost to them. The operational reality upon which a user authorises companies to collect his/her personal data to receive, in return, more personalised/targeted/context-aware services and hassle-free activities (for users) is widely deployed. It becomes evident that the security, integrity and accessibility of the collected data are of paramount importance. These characteristics are becoming more entrenched in the era of Internet-of-Things (IoT), autonomous vehicles and seamless travel. In this position paper, we examine the challenges faced by both users and organisations in dealing with the Personal Identifiable Information (PII). Furthermore, we expand on the implications of the General Data Protection Regulation (GDPR) specifically for the management of the PII. Subsequently, we extend the discussion to future technologies, especially the IoT and integrated transport systems for better customer experience -- and their ramification on the data governance and PII management. Finally, we propose a framework that balances user's privacy and data control with an organisation's objective of delivering quality, targeted and efficient services to their customers using the "collected user data". This framework is referred to as "Consumer Oriented Data Control \& Auditability" (CODCA) and defines the technologies that are adapted to privacy concerns and legal/regulation-frameworks.
New Distributed Algorithms in Almost Mixing Time via Transformations from Parallel Algorithms
Venue: arXiv cs.DS 2018
Year: 2018

We show that many classical optimization problems --- such as $(1\pmε)$-approximate maximum flow, shortest path, and transshipment --- can be computed in $\newcommand{\tmix}{τ_{\text{mix}}}\tmix(G)\cdot n^{o(1)}$ rounds of distributed message passing, where $\tmix(G)$ is the mixing time of the network graph $G$. This extends the result of Ghaffari et al.\ [PODC'17], whose main result is a distributed MST algorithm in $\tmix(G)\cdot 2^{O(\sqrt{\log n \log\log n})}$ rounds in the CONGEST model, to a much wider class of optimization problems. For many practical networks of interest, e.g., peer-to-peer or overlay network structures, the mixing time $\tmix(G)$ is small, e.g., polylogarithmic. On these networks, our algorithms bypass the $\tildeΩ(\sqrt n+D)$ lower bound of Das Sarma et al.\ [STOC'11], which applies for worst-case graphs and applies to all of the above optimization problems. For all of the problems except MST, this is the first distributed algorithm which takes $o(\sqrt n)$ rounds on a (nontrivial) restricted class of network graphs. Towards deriving these improved distributed algorithms, our main contribution is a general transformation that simulates any work-efficient PRAM algorithm running in $T$ parallel rounds via a distributed algorithm running in $T\cdot \tmix(G)\cdot 2^{O(\sqrt{\log n})}$ rounds. Work- and time-efficient parallel algorithms for all of the aforementioned problems follow by combining the work of Sherman [FOCS'13, SODA'17] and Peng and Spielman [STOC'14]. Thus, simulating these parallel algorithms using our transformation framework produces the desired distributed algorithms. The core technical component of our transformation is the algorithmic problem of solving \emph{multi-commodity routing}---that is, roughly, routing $n$ packets each from a given source to a given destination---in random graphs. For this problem, we obtain a...
Block Oriented Programming: Automating Data-Only Attacks
Venue: arXiv cs.CR 2018
Year: 2018

With the widespread deployment of Control-Flow Integrity (CFI), control-flow hijacking attacks, and consequently code reuse attacks, are significantly more difficult. CFI limits control flow to well-known locations, severely restricting arbitrary code execution. Assessing the remaining attack surface of an application under advanced control-flow hijack defenses such as CFI and shadow stacks remains an open problem. We introduce BOPC, a mechanism to automatically assess whether an attacker can execute arbitrary code on a binary hardened with CFI/shadow stack defenses. BOPC computes exploits for a target program from payload specifications written in a Turing-complete, high-level language called SPL that abstracts away architecture and program-specific details. SPL payloads are compiled into a program trace that executes the desired behavior on top of the target binary. The input for BOPC is an SPL payload, a starting point (e.g., from a fuzzer crash) and an arbitrary memory write primitive that allows application state corruption. To map SPL payloads to a program trace, BOPC introduces Block Oriented Programming (BOP), a new code reuse technique that utilizes entire basic blocks as gadgets along valid execution paths in the program, i.e., without violating CFI or shadow stack policies. We find that the problem of mapping payloads to program traces is NP-hard, so BOPC first reduces the search space by pruning infeasible paths and then uses heuristics to guide the search to probable paths. BOPC encodes the BOP payload as a set of memory writes. We execute 13 SPL payloads applied to 10 popular applications. BOPC successfully finds payloads and complex execution traces -- which would likely not have been found through manual analysis -- while following the target's Control-Flow Graph under an ideal CFI policy in 81% of the cases.
Security-Enhanced SC-FDMA Transmissions Using Temporal Artificial-Noise and Secret-Key Aided Schemes
Venue: arXiv cs.NI 2018
Year: 2018

We investigate the physical layer security of uplink single-carrier frequency-division multiple-access (SC-FDMA) systems. Multiple users, Alices, send confidential messages to a common legitimate base-station, Bob, in the presence of an eavesdropper, Eve. To secure the legitimate transmissions, each user superimposes an artificial noise (AN) signal on the time-domain SC-FDMA data block. We reduce the computational and storage requirements at Bob's receiver by assuming simple per-subchannel detectors. We assume that Eve has global channel knowledge of all links in addition to high computational capabilities, where she adopts high-complexity detectors such as single-user maximum likelihood (ML), multiuser minimum-mean-square-error (MMSE), and multiuser ML. We analyze the correlation properties of the time-domain AN signal and illustrate how Eve can exploit them to reduce the AN effects. We prove that the number of useful AN streams that can degrade Eve's signal-to-noise ratio (SNR) is dependent on the channel memories of Alices-Bob and Alices-Eve links. Furthermore, we enhance the system security for the case of partial Alices-Bob channel knowledge at Eve, where Eve only knows the precoding matrices of the data and AN signals instead of knowing the entire Alices-Bob channel matrices, and propose a hybrid scheme that integrates temporal AN with channel-based secret-key extraction.
Scene-Aware Audio for 360\textdegree{} Videos
Venue: arXiv cs.GR 2018
Year: 2018

Although 360\textdegree{} cameras ease the capture of panoramic footage, it remains challenging to add realistic 360\textdegree{} audio that blends into the captured scene and is synchronized with the camera motion. We present a method for adding scene-aware spatial audio to 360\textdegree{} videos in typical indoor scenes, using only a conventional mono-channel microphone and a speaker. We observe that the late reverberation of a room's impulse response is usually diffuse spatially and directionally. Exploiting this fact, we propose a method that synthesizes the directional impulse response between any source and listening locations by combining a synthesized early reverberation part and a measured late reverberation tail. The early reverberation is simulated using a geometric acoustic simulation and then enhanced using a frequency modulation method to capture room resonances. The late reverberation is extracted from a recorded impulse response, with a carefully chosen time duration that separates out the late reverberation from the early reverberation. In our validations, we show that our synthesized spatial audio matches closely with recordings using ambisonic microphones. Lastly, we demonstrate the strength of our method in several applications.
Community Detection by Information Flow Simulation
Venue: arXiv cs.SI 2018
Year: 2018

Community detection remains an important problem in data mining, owing to the lack of scalable algorithms that exploit all aspects of available data - namely the directionality of flow of information and the dynamics thereof. Most existing methods use measures of connectedness in the graphical structure. In this paper, we present a fast, scalable algorithm to detect communities in directed, weighted graph representations of social networks by simulating flow of information through them. By design, our algorithm naturally handles undirected or unweighted networks as well. Our algorithm runs in $\mathcal{O}(|E|)$ time, which is better than most existing work and uses $\mathcal{O}(|E|)$ space and hence scales easily to very large datasets. Finally, we show that our algorithm outperforms the state-of-the-art Markov Clustering Algorithm (MCL) in both accuracy and scalability on ground truth data (in a number of cases, we can find communities in graphs too large for MCL).
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
Venue: arXiv cs.CV 2018
Year: 2018

A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure and driving context make existing datasets practically ineffective due to a lack of uniform frameworks and searchable indexes. In order to overcome these limitations on existing public datasets, this paper proposes a data unification framework based on traffic primitives with ability to automatically unify and label heterogeneous traffic data. This is achieved by two steps: 1) Carefully arrange raw multidimensional time series driving data into a relational database and then 2) automatically extract labeled and indexed traffic primitives from traffic data through a Bayesian nonparametric learning method. Finally, we evaluate the effectiveness of our developed framework using the collected real vehicle data.
The Global Optimization Geometry of Shallow Linear Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

We examine the squared error loss landscape of shallow linear neural networks. We show---with significantly milder assumptions than previous works---that the corresponding optimization problems have benign geometric properties: there are no spurious local minima and the Hessian at every saddle point has at least one negative eigenvalue. This means that at every saddle point there is a directional negative curvature which algorithms can utilize to further decrease the objective value. These geometric properties imply that many local search algorithms (such as the gradient descent which is widely utilized for training neural networks) can provably solve the training problem with global convergence.
CARL: Content-Aware Representation Learning for Heterogeneous Networks
Venue: arXiv cs.SI 2018
Year: 2018

Heterogeneous networks not only present a challenge of heterogeneity in the types of nodes and relations, but also the attributes and content associated with the nodes. While recent works have looked at representation learning on homogeneous and heterogeneous networks, there is no work that has collectively addressed the following challenges: (a) the heterogeneous structural information of the network consisting of multiple types of nodes and relations; (b) the unstructured semantic content (e.g., text) associated with nodes; and (c) online updates due to incoming new nodes in growing network. We address these challenges by developing a Content-Aware Representation Learning model (CARL). CARL performs joint optimization of heterogeneous SkipGram and deep semantic encoding for capturing both heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network. Furthermore, an additional online update module is proposed for efficiently learning representations of incoming nodes. Extensive experiments demonstrate that CARL outperforms state-of-the-art baselines in various heterogeneous network mining tasks, such as link prediction, document retrieval, node recommendation and relevance search. We also demonstrate the effectiveness of the CARL's online update module through a category visualization study.
Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
Venue: arXiv cs.CL 2018
Year: 2018

To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Venue: arXiv cs.LG 2018
Year: 2018

Recently, it has been shown that deep neural networks (DNN) are subject to attacks through adversarial samples. Adversarial samples are often crafted through adversarial perturbation, i.e., manipulating the original sample with minor modifications so that the DNN model labels the sample incorrectly. Given that it is almost impossible to train perfect DNN, adversarial samples are shown to be easy to generate. As DNN are increasingly used in safety-critical systems like autonomous cars, it is crucial to develop techniques for defending such attacks. Existing defense mechanisms which aim to make adversarial perturbation challenging have been shown to be ineffective. In this work, we propose an alternative approach. We first observe that adversarial samples are much more sensitive to perturbations than normal samples. That is, if we impose random perturbations on a normal and an adversarial sample respectively, there is a significant difference between the ratio of label change due to the perturbations. Observing this, we design a statistical adversary detection algorithm called nMutant (inspired by mutation testing from software engineering community). Our experiments show that nMutant effectively detects most of the adversarial samples generated by recently proposed attacking methods. Furthermore, we provide an error bound with certain statistical significance along with the detection.
A Systematic Approach to Constructing Families of Incremental Topology Control Algorithms Using Graph Transformation
Venue: arXiv cs.SE 2018
Year: 2018

In the communication systems domain, constructing and maintaining network topologies via topology control (TC) algorithms is an important cross-cutting research area. Network topologies are usually modeled using attributed graphs whose nodes and edges represent the network nodes and their interconnecting links. A key requirement of TC algorithms is to fulfill certain consistency and optimization properties to ensure a high quality of service. Still, few attempts have been made to constructively integrate these properties into the development process of TC algorithms. Furthermore, even though many TC algorithms share substantial parts (such as structural patterns or tie-breaking strategies), few works constructively leverage these commonalities and differences of TC algorithms systematically. In previous work, we addressed the constructive integration of consistency properties into the development process. We outlined a constructive, model-driven methodology for designing individual TC algorithms. Valid and high-quality topologies are characterized using declarative graph constraints; TC algorithms are specified using programmed graph transformation. We applied a well-known static analysis technique to refine a given TC algorithm in a way that the resulting algorithm preserves the specified graph constraints. In this paper, we extend our constructive methodology by generalizing it to support the specification of families of TC algorithms. To show the feasibility of our approach, we reneging six existing TC algorithms and develop e-kTC, a novel energy-efficient variant of the TC algorithm kTC. Finally, we evaluate a subset of the specified TC algorithms using a new tool integration of the graph transformation tool eMoflon and the Simonstrator network simulation framework.