OpenRaiser/Intern-Atlas huggingface dataset:
A Random Walk Based Algorithm for Structural Test Case Generation
Venue: arXiv cs.SE 2017
Year: 2017

Structural testing is a significant and expensive process in software development. By converting test data generation into an optimization problem, search-based software testing is one of the key technologies of automated test case generation. Motivated by the success of random walk in solving the satisfiability problem (SAT), we proposed a random walk based algorithm (WalkTest) to solve structural test case generation problem. WalkTest provides a framework, which iteratively calls random walk operator to search the optimal solutions. In order to improve search efficiency, we sorted the test goals with the costs of solutions completely instead of traditional dependence analysis from control flow graph. Experimental results on the condition-decision coverage demonstrated that WalkTest achieves better performance than existing algorithms (random test and tabu search) in terms of running time and coverage rate.
Approximating the Backbone in the Weighted Maximum Satisfiability Problem
Venue: arXiv cs.AI 2017
Year: 2017

The weighted Maximum Satisfiability problem (weighted MAX-SAT) is a NP-hard problem with numerous applications arising in artificial intelligence. As an efficient tool for heuristic design, the backbone has been applied to heuristics design for many NP-hard problems. In this paper, we investigated the computational complexity for retrieving the backbone in weighted MAX-SAT and developed a new algorithm for solving this problem. We showed that it is intractable to retrieve the full backbone under the assumption that . Moreover, it is intractable to retrieve a fixed fraction of the backbone as well. And then we presented a backbone guided local search (BGLS) with Walksat operator for weighted MAX-SAT. BGLS consists of two phases: the first phase samples the backbone information from local optima and the backbone phase conducts local search under the guideline of backbone. Extensive experimental results on the benchmark showed that BGLS outperforms the existing heuristics in both solution quality and runtime.
Efficient Cryptographic Substitution Box Design Using Travelling Salesman Problem and Chaos
Venue: arXiv cs.CR 2017
Year: 2017

Symmetric encryption has been a standout amongst the most reliable option by which security is accomplished. In modern block symmetric ciphers, the substitution-boxes have been playing a critical role of nonlinear components that drives the actual security of ciphers. In this paper, the travelling salesman problem and piecewise linear chaotic map are explored to synthesize an efficient configuration of 8x8 substitution-box. The proposed anticipated design has the consistency which is justified by the standard performance indexes. The statistical results manifest that the prospective substitution-box is cryptographically more impressive as compared to some recent investigations.
Energy Efficient Adaptive Network Coding Schemes for Satellite Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose novel energy efficient adaptive network coding and modulation schemes for time variant channels. We evaluate such schemes under a realistic channel model for open area environments and Geostationary Earth Orbit (GEO) satellites. Compared to non-adaptive network coding and adaptive rate efficient network-coded schemes for time variant channels, we show that our proposed schemes, through physical layer awareness can be designed to transmit only if a target quality of service (QoS) is achieved. As a result, such schemes can provide remarkable energy savings.
Network Coding Channel Virtualization Schemes for Satellite Multicast Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose two novel schemes to solve the problem of finding a quasi-optimal number of coded packets to multicast to a set of independent wireless receivers suffering different channel conditions. In particular, we propose two network channel virtualization schemes that allow for representing the set of intended receivers in a multicast group to be virtualized as one receiver. Such approach allows for a transmission scheme not only adapted to per-receiver channel variation over time, but to the network-virtualized channel representing all receivers in the multicast group. The first scheme capitalizes on a maximum erasure criterion introduced via the creation of a virtual worst per receiver per slot reference channel of the network. The second scheme capitalizes on a maximum completion time criterion by the use of the worst performing receiver channel as a virtual reference to the network. We apply such schemes to a GEO satellite scenario. We demonstrate the benefits of the proposed schemes comparing them to a per-receiver point-to-point adaptive strategy.
Setting Up Pepper For Autonomous Navigation And Personalized Interaction With Users
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we present our work with the Pepper robot, a service robot from SoftBank Robotics. We had two main goals in this work: improving the autonomy of this robot by increasing its awareness of the environment; and enhance the robot ability to interact with its users. To achieve this goals, we used ROS, a modern open-source framework for developing robotics software, to provide Pepper with state of the art localization and navigation capabilities. Furthermore, we contribute an architecture for effective human interaction based on cloud services. Our architecture improves Pepper speech recognition capabilities by connecting it to the IBM Bluemix Speech Recognition service and enable the robot to recognize its user via an in-house face recognition web-service. We show examples of our successful integration of ROS and IBM services with Pepper's own software. As a result, we were able to make Pepper move autonomously in a environment with humans and obstacles. We were also able to have Pepper execute spoken commands from known users as well as newly introduced users that were enrolled in the robot list of trusted users via a multi-modal interface.
Conditions for the equivalence between IQC and graph separation stability results
Venue: arXiv cs.SY 2017
Year: 2017

This paper provides a link between time-domain and frequency-domain stability results in the literature. Specifically, we focus on the comparison between stability results for a feedback interconnection of two nonlinear systems stated in terms of frequency-domain conditions. While the Integral Quadratic Constrain (IQC) theorem can cope with them via a homotopy argument for the Lurye problem, graph separation results require the transformation of the frequency-domain conditions into truncated time-domain conditions. To date, much of the literature focuses on "hard" factorizations of the multiplier, considering only one of the two frequency-domain conditions. Here it is shown that a symmetric, "doubly-hard" factorization is required to convert both frequency-domain conditions into truncated time-domain conditions. By using the appropriate factorization, a novel comparison between the results obtained by IQC and separation theories is then provided. As a result, we identify under what conditions the IQC theorem may provide some advantage.
Versatile Robust Clustering of Ad Hoc Cognitive Radio Network
Venue: arXiv cs.GT 2017
Year: 2017

Cluster structure in cognitive radio networks facilitates cooperative spectrum sensing, routing and other functionalities. The unlicensed channels, which are available for every member of a group of cognitive radio users, consolidate the group into a cluster, and the availability of unlicensed channels decides the robustness of that cluster against the licensed users' influence. This paper analyses the problem that how to form robust clusters in cognitive radio network, so that more cognitive radio users can get benefits from cluster structure even when the primary users' operation are intense. We provide a formal description of robust clustering problem, prove it to be NP-hard and propose a centralized solution, besides, a distributed solution is proposed to suit the dynamics in the ad hoc cognitive radio network. Congestion game model is adopted to analyze the process of cluster formation, which not only contributes designing the distributed clustering scheme directly, but also provides the guarantee of convergence into Nash Equilibrium and convergence speed. Our proposed clustering solution is versatile to fulfill some other requirements such as faster convergence and cluster size control. The proposed distributed clustering scheme outperforms the related work in terms of cluster robustness, convergence speed and overhead. The extensive simulation supports our claims.
The Power of Waiting for More than One Response in Minimizing the Age-of-Information
Venue: arXiv cs.NI 2017
Year: 2017

The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems. Prior studies on AoI optimization often consider a Push model, which is concerned about when and how to "push" (i.e., generate and transmit) the updated information to the user. In stark contrast, in this paper we introduce a new Pull model, which is more relevant for certain applications (such as the real-time stock quotes service), where a user sends requests to the servers to proactively "pull" the information of interest. Moreover, we propose to employ request replication to reduce the AoI. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process at the servers and exponentially distributed response time, we derive a closedform formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Finally, we conduct numerical simulations to elucidate our theoretical results. Our findings show that waiting for more than one response can significantly reduce the AoI in most scenarios.
Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention
Venue: arXiv cs.LG 2017
Year: 2017

Online reinforcement learning (RL) is increasingly popular for the personalized mobile health (mHealth) intervention. It is able to personalize the type and dose of interventions according to user's ongoing statuses and changing needs. However, at the beginning of online learning, there are usually too few samples to support the RL updating, which leads to poor performances. A delay in good performance of the online learning algorithms can be especially detrimental in the mHealth, where users tend to quickly disengage with the mHealth app. To address this problem, we propose a new online RL methodology that focuses on an effective warm start. The main idea is to make full use of the data accumulated and the decision rule achieved in a former study. As a result, we can greatly enrich the data size at the beginning of online learning in our method. Such case accelerates the online learning process for new users to achieve good performances not only at the beginning of online learning but also through the whole online learning process. Besides, we use the decision rules achieved in a previous study to initialize the parameter in our online RL model for new users. It provides a good initialization for the proposed online RL algorithm. Experiment results show that promising improvements have been achieved by our method compared with the state-of-the-art method.
On Strong Determinacy of Countable Stochastic Games
Venue: arXiv cs.GT 2017
Year: 2017

We study 2-player turn-based perfect-information stochastic games with countably infinite state space. The players aim at maximizing/minimizing the probability of a given event (i.e., measurable set of infinite plays), such as reachability, Büchi, omega-regular or more general objectives. These games are known to be weakly determined, i.e., they have value. However, strong determinacy of threshold objectives (given by an event and a threshold $c \in [0,1]$) was open in many cases: is it always the case that the maximizer or the minimizer has a winning strategy, i.e., one that enforces, against all strategies of the other player, that the objective is satisfied with probability $\ge c$ (resp. $< c$)? We show that almost-sure objectives (where $c=1$) are strongly determined. This vastly generalizes a previous result on finite games with almost-sure tail objectives. On the other hand we show that $\ge 1/2$ (co-)Büchi objectives are not strongly determined, not even if the game is finitely branching. Moreover, for almost-sure reachability and almost-sure Büchi objectives in finitely branching games, we strengthen strong determinacy by showing that one of the players must have a memoryless deterministic (MD) winning strategy.
The pebbling comonad in finite model theory
Venue: arXiv cs.LO 2017
Year: 2017

Pebble games are a powerful tool in the study of finite model theory, constraint satisfaction and database theory. Monads and comonads are basic notions of category theory which are widely used in semantics of computation and in modern functional programming. We show that existential k-pebble games have a natural comonadic formulation. Winning strategies for Duplicator in the k-pebble game for structures A and B are equivalent to morphisms from A to B in the coKleisli category for this comonad. This leads on to comonadic characterisations of a number of central concepts in Finite Model Theory: - Isomorphism in the co-Kleisli category characterises elementary equivalence in the k-variable logic with counting quantifiers. - Symmetric games corresponding to equivalence in full k-variable logic are also characterized. - The treewidth of a structure A is characterised in terms of its coalgebra number: the least k for which there is a coalgebra structure on A for the k-pebbling comonad. - Co-Kleisli morphisms are used to characterize strong consistency, and to give an account of a Cai-Fürer-Immerman construction. - The k-pebbling comonad is also used to give semantics to a novel modal operator. These results lay the basis for some new and promising connections between two areas within logic in computer science which have largely been disjoint: (1) finite and algorithmic model theory, and (2) semantics and categorical structures of computation.
A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems
Venue: arXiv cs.NE 2017
Year: 2017

In this paper, we study various parallelization schemes for the Variable Neighborhood Search (VNS) metaheuristic on a CPU-GPU system via OpenMP and OpenACC. A hybrid parallel VNS method is applied to recent benchmark problem instances for the multi-product dynamic lot sizing problem with product returns and recovery, which appears in reverse logistics and is known to be NP-hard. We report our findings regarding these parallelization approaches and present promising computational results.
Learning Linear Feature Space Transformations in Symbolic Regression
Venue: arXiv cs.NE 2017
Year: 2017

We propose a new type of leaf node for use in Symbolic Regression (SR) that performs linear combinations of feature variables (LCF). These nodes can be handled in three different modes -- an unsynchronized mode, where all LCFs are free to change on their own, a synchronized mode, where LCFs are sorted into groups in which they are forced to be identical throughout the whole individual, and a globally synchronized mode, which is similar to the previous mode but the grouping is done across the whole population. We also present two methods of evolving the weights of the LCFs -- a purely stochastic way via mutation and a gradient-based way based on the backpropagation algorithm known from neural networks -- and also a combination of both. We experimentally evaluate all configurations of LCFs in Multi-Gene Genetic Programming (MGGP), which was chosen as baseline, on a number of benchmarks. According to the results, we identified two configurations which increase the performance of the algorithm.
O$^2$TD: (Near)-Optimal Off-Policy TD Learning
Venue: arXiv cs.LG 2017
Year: 2017

Temporal difference learning and Residual Gradient methods are the most widely used temporal difference based learning algorithms; however, it has been shown that none of their objective functions is optimal w.r.t approximating the true value function $V$. Two novel algorithms are proposed to approximate the true value function $V$. This paper makes the following contributions: (1) A batch algorithm that can help find the approximate optimal off-policy prediction of the true value function $V$. (2) A linear computational cost (per step) near-optimal algorithm that can learn from a collection of off-policy samples. (3) A new perspective of the emphatic temporal difference learning which bridges the gap between off-policy optimality and off-policy stability.
Capacity of Cellular Wireless Network
Venue: arXiv cs.IT 2017
Year: 2017

Earlier definitions of capacity for wireless networks, e.g., transport or transmission capacity, for which exact theoretical results are known, are well suited for ad hoc networks but are not directly applicable for cellular wireless networks, where large-scale basestation (BS) coordination is not possible, and retransmissions/ARQ under the SINR model is a universal feature. In this paper, cellular wireless networks, where both BS locations and mobile user (MU) locations are distributed as independent Poisson point processes are considered, and each MU connects to its nearest BS. With ARQ, under the SINR model, the effective downlink rate of packet transmission is the reciprocal of the expected delay (number of retransmissions needed till success), which we use as our network capacity definition after scaling it with the BS density. Exact characterization of this natural capacity metric for cellular wireless networks is derived. The capacity is shown to first increase polynomially with the BS density in the low BS density regime and then scale inverse exponentially with the increasing BS density. Two distinct upper bounds are derived that are relevant for the low and the high BS density regimes. A single power control strategy is shown to achieve the upper bounds in both the regimes. This result is fundamentally different from the well known capacity results for ad hoc networks, such as transport and transmission capacity that scale as the square root of the (high) BS density. Our results show that the strong temporal correlations of SINRs with PPP distributed BS locations is limiting, and the realizable capacity in cellular wireless networks in high-BS density regime is much smaller than previously thought. A byproduct of our analysis shows that the capacity of the ALOHA strategy with retransmissions is zero.
Peer Truth Serum: Incentives for Crowdsourcing Measurements and Opinions
Venue: arXiv cs.GT 2017
Year: 2017

Modern decision making tools are based on statistical analysis of abundant data, which is often collected by querying multiple individuals. We consider data collection through crowdsourcing, where independent and self-interested agents, non-experts, report measurements, such as sensor readings, opinions, such as product reviews, or answers to human intelligence tasks. Since the accuracy of information is positively correlated with the effort invested in obtaining it, self-interested agents tend to report low-quality data. Therefore, there is a need for incentives that cover the cost of effort, while discouraging random reports. We propose a novel incentive mechanism called Peer Truth Serum that encourages truthful and accurate reporting, showing that it is the unique mechanism to satisfy a combination of desirable properties.
How to Cooperate Locally to Improve Global Privacy in Social Networks? On Amplification of Privacy Preserving Data Aggregation
Venue: arXiv cs.SI 2017
Year: 2017

In many systems privacy of users depends on the number of participants applying collectively some method to protect their security. Indeed, there are numerous already classic results about revealing aggregated data from a set of users. The conclusion is usually as follows: if you have enough friends to "aggregate" the private data, you can safely reveal your private information. Apart from data aggregation, it has been noticed that in a wider context privacy can be often reduced to being hidden in a crowd. Generally, the problems is how to create such crowd. This task may be not easy in some distributed systems, wherein gathering enough "individuals" is hard for practical reasons. Such example are social networks (or similar systems), where users have only a limited number of semi trusted contacts and their aim is to reveal some aggregated data in a privacy preserving manner. This may be particularly problematic in the presence of a strong adversary that can additionally corrupt some users. We show two methods that allow to significantly amplify privacy with only limited number of local operations and very moderate communication overhead. Except theoretical analysis we show experimental results on topologies of real-life social networks to demonstrate that our methods can significantly amplify privacy of chosen aggregation protocols even facing a massive attack of a powerful adversary. We believe however that our results can have much wider applications for improving security of systems based on locally trusted relations.
On Low Complexity Detection for QAM Isomorphic Constellations
Venue: arXiv cs.IT 2017
Year: 2017

Despite of the known gap from the Shannon's capacity, several standards are still employing QAM or star shape constellations, mainly due to the existing low complexity detectors. In this paper, we investigate the low complexity detection for a family of QAM isomorphic constellations. These constellations are known to perform very close to the peak-power limited capacity, outperforming the DVB-S2X standard constellations. The proposed strategy is to first remap the received signals to the QAM constellation using the existing isomorphism and then break the log likelihood ratio computations to two one dimensional PAM constellations. Gains larger than 0.6 dB with respect to QAM can be obtained over the peak power limited channels without any increase in detection complexity. Our scheme also provides a systematic way to design constellations with low complexity one dimensional detectors. Several open problems are discussed at the end of the paper.
Enabling an Anatomic View to Investigate Honeypot Systems: A Survey
Venue: arXiv cs.CR 2017
Year: 2017

A honeypot is a type of security facility deliberately created to be probed, attacked and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behaviour of attackers, and in particular, unknown attacks. For the past 17 years much effort has been invested in the research and development of honeypot based techniques and tools and they have evolved to become an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying multiple honeypot systems, the two essential elements of honeypots - the decoy and the security program - are captured and presented, together with two abstract organizational forms - independent and cooperative - in which these two elements can be integrated. A novel decoy and security program (D-P) based taxonomy is proposed, for the purpose of investigating and classifying the various techniques involved in honeypot systems. An extensive set of honeypot projects and research, which cover the techniques applied in both independent and cooperative honeypots, is surveyed under the taxonomy framework. Finally, the taxonomy is applied to a wide set of tools and systems in order to demonstrate its validity and predict the tendency of honeypot development.