OpenRaiser/Intern-Atlas huggingface dataset:
Passive Fingerprinting of SCADA in Critical Infrastructure Network without Deep Packet Inspection
Venue: arXiv cs.CR 2016
Year: 2016

We present the first technique of passive fingerprinting for Supervisory Control And Data Acquisition (SCADA) networks without Deep Packet Inspection (DPI) and experience on real environment. Unlike existing work, our method does not rely on the functions of a specific product or DPI of the SCADA protocol. Our inference method, which is based on the intrinsic characteristics of SCADA, first identifies the network port used for the SCADA protocol, then consecutively infers the field devices and master server. We evaluated the effectiveness of our method using two network traces collected from a real environment for a month and a half, three days from different CI respectively. This confirmed the ability of our method to capture most of the SCADA with high F-score nearly 1, except for HMIs connected to master server, and demonstrated the practical applicability of the method.
Multiversion Altruistic Locking
Venue: arXiv cs.DC 2016
Year: 2016

This paper builds on altruistic locking which is an extension of 2PL. It allows more relaxed rules as compared to 2PL. But altruistic locking too enforces some rules which disallow some valid schedules (present in VSR and CSR) to be passed by AL. This paper proposes a multiversion variant of AL which solves this problem. The report also discusses the relationship or comparison between different protocols such as MAL and MV2PL, MAL and AL, MAL and 2PL and so on. This paper also discusses the caveats involved in MAL and where it lies in the Venn diagram of multiversion serializable schedule protocols. Finally, the possible use of MAL in hybrid protocols and the parameters involved in making MAL successful are discussed.
A Generalization of the Directed Graph Layering Problem
Venue: arXiv cs.OH 2016
Year: 2016

The Directed Layering Problem (DLP) solves a step of the widely used layer-based approach to automatically draw directed acyclic graphs. To cater for cyclic graphs, usually a preprocessing step is used that solves the Feedback Arc Set Problem (FASP) to make the graph acyclic before a layering is determined. Here we present the Generalized Layering Problem (GLP), which solves the combination of DLP and FASP simultaneously, allowing general graphs as input. We present an integer programming model and a heuristic to solve the NP-complete GLP and perform thorough evaluations on different sets of graphs and with different implementations for the steps of the layer-based approach. We observe that GLP reduces the number of dummy nodes significantly, can produce more compact drawings, and improves on graphs where DLP yields poor aspect ratios.
Data Analytics using Ontologies of Management Theories: Towards Implementing 'From Theory to Practice'
Venue: arXiv cs.AI 2016
Year: 2016

We explore how computational ontologies can be impactful vis-a-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliche, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.
Causality and Responsibility for Formal Verification and Beyond
Venue: arXiv cs.SE 2016
Year: 2016

The theory of actual causality, defined by Halpern and Pearl, and its quantitative measure - the degree of responsibility - was shown to be extremely useful in various areas of computer science due to a good match between the results it produces and our intuition. In this paper, I describe the applications of causality to formal verification, namely, explanation of counterexamples, refinement of coverage metrics, and symbolic trajectory evaluation. I also briefly discuss recent applications of causality to legal reasoning.
(De-)Composing Causality in Labeled Transition Systems
Venue: arXiv cs.LO 2016
Year: 2016

In this paper we introduce a notion of counterfactual causality in the Halpern and Pearl sense that is compositional with respect to the interleaving of transition systems. The formal framework for reasoning on what caused the violation of a safety property is established in the context of labeled transition systems and Hennessy Milner logic. The compositionality results are devised for non-communicating systems.
Towards a Unified Model of Accountability Infrastructures
Venue: arXiv cs.CY 2016
Year: 2016

Accountability aims to provide explanations for why unwanted situations occurred, thus providing means to assign responsibility and liability. As such, accountability has slightly different meanings across the sciences. In computer science, our focus is on providing explanations for technical systems, in particular if they interact with their physical environment using sensors and actuators and may do serious harm. Accountability is relevant when considering safety, security and privacy properties and we realize that all these incarnations are facets of the same core idea. Hence, in this paper we motivate and propose a model for accountability infrastructures that is expressive enough to capture all of these domains. At its core, this model leverages formal causality models from the literature in order to provide a solid reasoning framework. We show how this model can be instantiated for several real-world use cases.
Magnetic skyrmion-based synaptic devices
Venue: arXiv cs.ET 2016
Year: 2016

Magnetic skyrmions are promising candidates for next-generation information carriers, owing to their small size, topological stability, and ultralow depinning current density. A wide variety of skyrmionic device concepts and prototypes have been proposed, highlighting their potential applications. Here, we report on a bioinspired skyrmionic device with synaptic plasticity. The synaptic weight of the proposed device can be strengthened/weakened by positive/negative stimuli, mimicking the potentiation/depression process of a biological synapse. Both short-term plasticity(STP) and long-term potentiation(LTP) functionalities have been demonstrated for a spiking time-dependent plasticity(STDP) scheme. This proposal suggests new possibilities for synaptic devices for use in spiking neuromorphic computing applications.
Correspondence Insertion for As-Projective-As-Possible Image Stitching
Venue: arXiv cs.CV 2016
Year: 2016

Spatially varying warps are increasingly popular for image alignment. In particular, as-projective-as-possible (APAP) warps have been proven effective for accurate panoramic stitching, especially in cases with significant depth parallax that defeat standard homographic warps. However, estimating spatially varying warps requires a sufficient number of feature matches. In image regions where feature detection or matching fail, the warp loses guidance and is unable to accurately model the true underlying warp, thus resulting in poor registration. In this paper, we propose a correspondence insertion method for APAP warps, with a focus on panoramic stitching. Our method automatically identifies misaligned regions, and inserts appropriate point correspondences to increase the flexibility of the warp and improve alignment. Unlike other warp varieties, the underlying projective regularization of APAP warps reduces overfitting and geometric distortion, despite increases to the warp complexity. Comparisons with recent techniques for parallax-tolerant image stitching demonstrate the effectiveness and simplicity of our approach.
Optimal QoS-Aware Channel Assignment in D2D Communications with Partial CSI
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e., the base station (BS) is assumed to be able to acquire `partial' instantaneous CSI of the cellular and D2D links, as well as, the interference links. In contrast to existing works, multiple D2D links are allowed to share the same channel, and the quality of service (QoS) requirements for both the cellular and D2D links are enforced. We first develop an optimal channel assignment algorithm based on dynamic programming (DP), which enjoys a much lower complexity compared to exhaustive search and will serve as a performance benchmark. To further reduce complexity, we propose a cluster-based sub-optimal channel assignment algorithm. New closed-form expressions for the expected weighted sum-rate and the successful transmission probabilities are also derived. Simulation results verify the effectiveness of the proposed algorithms. Moreover, by comparing different partial CSI scenarios, we observe that the CSI of the D2D communication links and the interference links from the D2D transmitters to the BS significantly affects the network performance, while the CSI of the interference links from the BS to the D2D receivers only has a negligible impact.
Visual Question: Predicting If a Crowd Will Agree on the Answer
Venue: arXiv cs.AI 2016
Year: 2016

Visual question answering (VQA) systems are emerging from a desire to empower users to ask any natural language question about visual content and receive a valid answer in response. However, close examination of the VQA problem reveals an unavoidable, entangled problem that multiple humans may or may not always agree on a single answer to a visual question. We train a model to automatically predict from a visual question whether a crowd would agree on a single answer. We then propose how to exploit this system in a novel application to efficiently allocate human effort to collect answers to visual questions. Specifically, we propose a crowdsourcing system that automatically solicits fewer human responses when answer agreement is expected and more human responses when answer disagreement is expected. Our system improves upon existing crowdsourcing systems, typically eliminating at least 20% of human effort with no loss to the information collected from the crowd.
Interference Power Bound Analysis of a Network of Wireless Robots
Venue: arXiv cs.RO 2016
Year: 2016

We consider a fundamental problem concerning the deployment of a wireless robotic network: to fulfill various end-to-end performance requirements, a "sufficient" number of robotic relays must be deployed to ensure that links are of acceptable quality. Prior work has not addressed how to find this number. We use the properties of Carrier Sense Multiple Access (CSMA) based wireless communication to derive an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy. We focus on SINR-based performance requirements due to their wide applicability. Next, we show that the bound can be improved by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks. Furthermore, we also use the bound on robot count to formulate a lower bound on the number of orthogonal codes required for a high probability of interference free communication. We demonstrate and validate our proposed bounds through simulations.
Optimal Deployment of Resources for Maximizing Impact in Spreading Processes
Venue: arXiv cs.SI 2016
Year: 2016

The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of "influential spreaders" for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings the problem is often characterized by heterogeneous interactions and requires interventions over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions
Venue: arXiv cs.CV 2016
Year: 2016

Image segmentation from referring expressions is a joint vision and language modeling task, where the input is an image and a textual expression describing a particular region in the image; and the goal is to localize and segment the specific image region based on the given expression. One major difficulty to train such language-based image segmentation systems is the lack of datasets with joint vision and text annotations. Although existing vision datasets such as MS COCO provide image captions, there are few datasets with region-level textual annotations for images, and these are often smaller in scale. In this paper, we explore how existing large scale vision-only and text-only datasets can be utilized to train models for image segmentation from referring expressions. We propose a method to address this problem, and show in experiments that our method can help this joint vision and language modeling task with vision-only and text-only data and outperforms previous results.
Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we study the resource allocation and user scheduling problem for a downlink nonorthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme.
Static Trace-Based Deadlock Analysis for Synchronous Mini-Go
Venue: arXiv cs.PL 2016
Year: 2016

We consider the problem of static deadlock detection for programs in the Go programming language which make use of synchronous channel communications. In our analysis, regular expressions extended with a fork operator capture the communication behavior of a program. Starting from a simple criterion that characterizes traces of deadlock-free programs, we develop automata-based methods to check for deadlock-freedom. The approach is implemented and evaluated with a series of examples.
A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices
Venue: arXiv cs.AR 2016
Year: 2016

Endpoint devices for Internet-of-Things not only need to work under extremely tight power envelope of a few milliwatts, but also need to be flexible in their computing capabilities, from a few kOPS to GOPS. Near-threshold(NT) operation can achieve higher energy efficiency, and the performance scalability can be gained through parallelism. In this paper we describe the design of an open-source RISC-V processor core specifically designed for NT operation in tightly coupled multi-core clusters. We introduce instruction-extensions and microarchitectural optimizations to increase the computational density and to minimize the pressure towards the shared memory hierarchy. For typical data-intensive sensor processing workloads the proposed core is on average 3.5x faster and 3.2x more energy-efficient, thanks to a smart L0 buffer to reduce cache access contentions and support for compressed instructions. SIMD extensions, such as dot-products, and a built-in L0 storage further reduce the shared memory accesses by 8x reducing contentions by 3.2x. With four NT-optimized cores, the cluster is operational from 0.6V to 1.2V achieving a peak efficiency of 67MOPS/mW in a low-cost 65nm bulk CMOS technology. In a low power 28nm FDSOI process a peak efficiency of 193MOPS/mW(40MHz, 1mW) can be achieved.
Opinion Manipulation in Social Networks
Venue: arXiv cs.GT 2016
Year: 2016

In this work, we are interested in finding the most efficient use of a budget to promote an opinion by paying agents within a group to supplant their true opinions. We model opinions as continuous scalars ranging from 0 to 1 with 1 (0) representing extremely positive (negative) opinion. We focus on asymmetric confidence between agents. The iterative update of an agent corresponds to the best response to other agents' actions. The resulting confidence matrix can be seen as an equivalent Markov chain. We provide simple and efficient algorithms to solve this problem and we show through an example how to solve the stated problem in practice.
Separating Components of Attention and Surprise
Venue: arXiv cs.HC 2016
Year: 2016

Cognitive processes involved in both allocation of attention during decision making as well as surprise when making mistakes trigger release of the neurotransmitter norepinephrine, which has been shown to be correlated with an increase in pupil dilation, in turn reflecting raised levels of arousal. Extending earlier experiments based on the Attention Network Test (ANT), separating the neural components of alertness and spatial re-orientation from the attention involved in more demanding conflict resolution tasks, we demonstrate that these signatures of attention are so robust that they may be retrieved even when applying low cost eye tracking in an everyday mobile computing context. Furthermore we find that the reaction of surprise elicited when committing mistakes in a decision task, which in the neuroimaging EEG literature have been referred to as a negativity feedback error correction signal, may likewise be retrieved solely based on an increase in pupil dilation.
Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators
Venue: arXiv cs.AI 2016
Year: 2016

An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage. This task is fraught with difficulties and uncertainty, making the knowledge provided by human experts essential for successful assessment. Today, the increasing number of progressively complex systems has led to an urgent need to produce tools that support the expert-led process of system-security assessment. In this research, we use weighted averages (WAs) and ordered weighted averages (OWAs) with evolutionary algorithms (EAs) to create aggregation operators that model parts of the assessment process. We show how individual overall ratings for security components can be produced from ratings of their characteristics, and how these individual overall ratings can be aggregated to produce overall rankings of potential attacks on a system. As well as the identification of salient attacks and weak points in a prospective system, the proposed method also highlights which factors and security components contribute most to a component's difficulty and attack ranking respectively. A real world scenario is used in which experts were asked to rank a set of technical attacks, and to answer a series of questions about the security components that are the subject of the attacks. The work shows how finding good aggregation operators, and identifying important components and factors of a cyber-security problem can be automated. The resulting operators have the potential for use as decision aids for systems designers and cyber-security experts, increasing the amount of assessment that can be achieved with the limited resources available.
Collaborative Delivery with Energy-Constrained Mobile Robots
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of collectively delivering some message from a specified source to a designated target location in a graph, using multiple mobile agents. Each agent has a limited energy which constrains the distance it can move. Hence multiple agents need to collaborate to move the message, each agent handing over the message to the next agent to carry it forward. Given the positions of the agents in the graph and their respective budgets, the problem of finding a feasible movement schedule for the agents can be challenging. We consider two variants of the problem: in non-returning delivery, the agents can stop anywhere; whereas in returning delivery, each agent needs to return to its starting location, a variant which has not been studied before. We first provide a polynomial-time algorithm for returning delivery on trees, which is in contrast to the known (weak) NP-hardness of the non-returning version. In addition, we give resource-augmented algorithms for returning delivery in general graphs. Finally, we give tight lower bounds on the required resource augmentation for both variants of the problem. In this sense, our results close the gap left by previous research.
Dynamic Controllability of Conditional Simple Temporal Networks is PSPACE-complete
Venue: arXiv cs.DS 2016
Year: 2016

Even after the proposal of various solution algorithms, the precise computational complexity of checking whether a Conditional Temporal Network is Dynamically Controllable had still remained widely open. This issue gets settled in this paper which provides constructions, algorithms, and bridging lemmas and arguments to formally prove that: (1) the problem is PSPACE-hard, and (2) the problem lies in PSPACE.
Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems
Venue: arXiv cs.AI 2016
Year: 2016

Autonomous driving has been the subject of increased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where multiple interactions between vehicles and drivers simultaneously occur. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests. In this paper, we present a game theoretic traffic model that can be used to 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment.