OpenRaiser/Intern-Atlas huggingface dataset:
Linear Exponential Comonads without Symmetry
Venue: arXiv cs.LO 2017
Year: 2017

The notion of linear exponential comonads on symmetric monoidal categories has been used for modelling the exponential modality of linear logic. In this paper we introduce linear exponential comonads on general (possibly non-symmetric) monoidal categories, and show some basic results on them.
Investigating the Application of Common-Sense Knowledge-Base for Identifying Term Obfuscation in Adversarial Communication
Venue: arXiv cs.IR 2017
Year: 2017

Word obfuscation or substitution means replacing one word with another word in a sentence to conceal the textual content or communication. Word obfuscation is used in adversarial communication by terrorist or criminals for conveying their messages without getting red-flagged by security and intelligence agencies intercepting or scanning messages (such as emails and telephone conversations). ConceptNet is a freely available semantic network represented as a directed graph consisting of nodes as concepts and edges as assertions of common sense about these concepts. We present a solution approach exploiting vast amount of semantic knowledge in ConceptNet for addressing the technically challenging problem of word substitution in adversarial communication. We frame the given problem as a textual reasoning and context inference task and utilize ConceptNet's natural-language-processing tool-kit for determining word substitution. We use ConceptNet to compute the conceptual similarity between any two given terms and define a Mean Average Conceptual Similarity (MACS) metric to identify out-of-context terms. The test-bed to evaluate our proposed approach consists of Enron email dataset (having over 600000 emails generated by 158 employees of Enron Corporation) and Brown corpus (totaling about a million words drawn from a wide variety of sources). We implement word substitution techniques used by previous researches to generate a test dataset. We conduct a series of experiments consisting of word substitution methods used in the past to evaluate our approach. Experimental results reveal that the proposed approach is effective.
IoTScanner: Detecting and Classifying Privacy Threats in IoT Neighborhoods
Venue: arXiv cs.CR 2017
Year: 2017

In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As a result, owners and users of physical space are threatened to lose control over their digital environments. In this work, we propose the idea of an IoTScanner. The IoTScanner integrates a range of radios to allow local reconnaissance of existing wireless infrastructure and participating nodes. It enumerates such devices, identifies connection patterns, and provides valuable insights for technical support and home users alike. Using our IoTScanner, we attempt to classify actively streaming IP cameras from other non-camera devices using simple heuristics. We show that our classification approach achieves a high accuracy in an IoT setting consisting of a large number of IoT devices. While related work usually focuses on detecting either the infrastructure, or eavesdropping on traffic from a specific node, we focus on providing a general overview of operations in all observed networks. We do not assume prior knowledge of used SSIDs, preshared passwords, or similar.
(Mathematical) Logic for Systems Biology (Invited Paper)
Venue: arXiv cs.LO 2017
Year: 2017

We advocates here the use of (mathematical) logic for systems biology, as a unified framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including a sequent calculus presentation enjoying cut-elimination and focusing), and should come with (certified) proof tools. Beyond providing a reliable framework, this allows the adequate encodings of our biological systems. We present two candidate logics (two modal extensions of linear logic, called HyLL and SELL), along with biological examples. The examples we have considered so far are very simple ones-coming with completely formal (interactive) proofs in Coq. Future works includes using automatic provers, which would extend existing automatic provers for linear logic. This should enable us to specify and study more realistic examples in systems biology, biomedicine (diagnosis and prognosis), and eventually neuroscience.
Temporal scale selection in time-causal scale space
Venue: arXiv cs.CV 2017
Year: 2017

When designing and developing scale selection mechanisms for generating hypotheses about characteristic scales in signals, it is essential that the selected scale levels reflect the extent of the underlying structures in the signal. This paper presents a theory and in-depth theoretical analysis about the scale selection properties of methods for automatically selecting local temporal scales in time-dependent signals based on local extrema over temporal scales of scale-normalized temporal derivative responses. Specifically, this paper develops a novel theoretical framework for performing such temporal scale selection over a time-causal and time-recursive temporal domain as is necessary when processing continuous video or audio streams in real time or when modelling biological perception. For a recently developed time-causal and time-recursive scale-space concept defined by convolution with a scale-invariant limit kernel, we show that it is possible to transfer a large number of the desirable scale selection properties that hold for the Gaussian scale-space concept over a non-causal temporal domain to this temporal scale-space concept over a truly time-causal domain. Specifically, we show that for this temporal scale-space concept, it is possible to achieve true temporal scale invariance although the temporal scale levels have to be discrete, which is a novel theoretical construction.
On the Performance of Network Parallel Training in Artificial Neural Networks
Venue: arXiv cs.AI 2017
Year: 2017

Artificial Neural Networks (ANNs) have received increasing attention in recent years with applications that span a wide range of disciplines including vital domains such as medicine, network security and autonomous transportation. However, neural network architectures are becoming increasingly complex and with an increasing need to obtain real-time results from such models, it has become pivotal to use parallelization as a mechanism for speeding up network training and deployment. In this work we propose an implementation of Network Parallel Training through Cannon's Algorithm for matrix multiplication. We show that increasing the number of processes speeds up training until the point where process communication costs become prohibitive; this point varies by network complexity. We also show through empirical efficiency calculations that the speedup obtained is superlinear.
The Medial Axis of a Multi-Layered Environment and its Application as a Navigation Mesh
Venue: arXiv cs.CG 2017
Year: 2017

Path planning for walking characters in complicated virtual environments is a fundamental task in simulations and games. A navigation mesh is a data structure that allows efficient path planning. The Explicit Corridor Map (ECM) is a navigation mesh based on the medial axis. It enables path planning for disk-shaped characters of any radius. In this paper, we formally extend the medial axis (and therefore the ECM) to 3D environments in which characters are constrained to walkable surfaces. Typical examples of such environments are multi-storey buildings, train stations, and sports stadiums. We give improved definitions of a walkable environment (WE: a description of walkable surfaces in 3D) and a multi-layered environment (MLE: a subdivision of a WE into connected layers). We define the medial axis of such environments based on projected distances on the ground plane. For an MLE with $n$ boundary vertices and $k$ connections, we show that the medial axis has size $O(n)$, and we present an improved algorithm that constructs the medial axis in $O(n \log n \log k)$ time. The medial axis can be annotated with nearest-obstacle information to obtain the ECM navigation mesh. Our implementations show that the ECM can be computed efficiently for large 2D and multi-layered environments, and that it can be used to compute paths within milliseconds. This enables simulations of large virtual crowds of heterogeneous characters in real-time.
Structure of a Parabolic Partial Differential Equation on Graphs and Digital spaces. Solution of PDE on Digital Spaces: a Klein Bottle, a Projective Plane, a 4D Sphere and a Moebius Band
Venue: arXiv cs.DM 2017
Year: 2017

This paper studies the structure of a parabolic partial differential equation on graphs and digital n-dimensional manifolds, which are digital models of continuous n-manifolds. Conditions for the existence of solutions of equations are determined and investigated. Numerical solutions of the equation on a Klein bottle, a projective plane, a 4D sphere and a Moebius strip are presented.
Distributed Framework for Optimal Demand Distribution in Self-Balancing Microgrid
Venue: arXiv cs.SY 2017
Year: 2017

This study focusses on self-balancing microgrids to smartly utilize and prevent overdrawing of available power capacity of the grid. A distributed framework for automated distribution of optimal power demand is proposed, where all building in a microgrid dynamically and simultaneously adjusts their own power consumption to reach their individual optimal power demands while cooperatively striving to maintain the overall grid stable. Emphasis has been given to aspects of algorithm that yields lower time of convergence and is demonstrated through quantitative and qualitative analysis of simulation results.
An Implementation of SCADA Network Security Testbed
Venue: arXiv cs.CR 2017
Year: 2017

The security of industrial network has become an increasing concern in industry infrastructure operation. Motivated by on-going collaborations with Fortinet Corp., a security company, this project implements a testbed for supervisory control and data acquisition (SCADA) network security research by software emulation. Concepts about SCADA and Modbus protocol are reviewed in the report. Besides Modbus, vulnerabilities about several other industrial protocols are also studied for this project. In this report, a typical tank system following Modbus protocol is built as a testbed. Both attack and defense toolkits are introduced to emulate the attack and protection of the Modbus network. The emulation platform is also capable of entrapping hackers and gathering their activity data.
A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge
Venue: arXiv cs.CL 2017
Year: 2017

For argumentation mining, there are several sub-tasks such as argumentation component type classification, relation classification. Existing research tends to solve such sub-tasks separately, but ignore the close relation between them. In this paper, we present a joint framework incorporating logical relation between sub-tasks to improve the performance of argumentation structure generation. We design an objective function to combine the predictions from individual models for each sub-task and solve the problem with some constraints constructed from background knowledge. We evaluate our proposed model on two public corpora and the experiment results show that our model can outperform the baseline that uses a separate model significantly for each sub-task. Our model also shows advantages on component-related sub-tasks compared to a state-of-the-art joint model based on the evidence graph.
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Most successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses the first-order statistics of frame-level action predictions. In this paper, we explore the advantages of using higher-order correlations; specifically, we introduce Higher-order Kernel (HOK) descriptors generated from the late fusion of CNN classifier scores from all the frames in a sequence. To generate these descriptors, we use the idea of kernel linearization. Specifically, a similarity kernel matrix, which captures the temporal evolution of deep classifier scores, is first linearized into kernel feature maps. The HOK descriptors are then generated from the higher-order co-occurrences of these feature maps, and are then used as input to a video-level classifier. We provide experiments on two fine-grained action recognition datasets and show that our scheme leads to state-of-the-art results.
Software Architectures for Robotics Systems: A Systematic Mapping Study
Venue: arXiv cs.SE 2017
Year: 2017

Software architecture related issues are important for robotic systems. Architecture centric development and evolution of software for robotic systems has been attracting researchers attention for more than two decades. The objective of this work is to systematically identify, taxonomically classify and holistically map existing solutions, research progress and trends that influence architecture-driven modeling, development and evolution of robotic software. We carried out a Systematic Mapping Study to identify and analyze the relevant literature based on 56 peer-reviewed papers. We extract and synthesize the data from selected papers to taxonomically classify the existing research and systematically map the solutions, frameworks, notations and evaluation methods to highlight the role of software architecture in robotic systems. We have identified eight distinct research themes that support architectural solutions to enable operations, evolution and development specific activities of robotic software. The research in this area has progressed from object oriented to component based and now to service driven robotics representing different architectural generations. The reported solutions have exploited model-driven, service oriented and reverse engineering techniques since 2005. An emerging trend is cloud robotics that exploits the foundations of service driven architectures to support an interconnected web of robots. The results of this SMS facilitate knowledge transfer, benefiting researchers and practitioners, focused on exploiting software architecture to model, develop and evolve robotic systems.
Proving Linearizability Using Partial Orders (Extended Version)
Venue: arXiv cs.PL 2017
Year: 2017

Linearizability is the commonly accepted notion of correctness for concurrent data structures. It requires that any execution of the data structure is justified by a linearization --- a linear order on operations satisfying the data structure's sequential specification. Proving linearizability is often challenging because an operation's position in the linearization order may depend on future operations. This makes it very difficult to incrementally construct the linearization in a proof. We propose a new proof method that can handle data structures with such future-dependent linearizations. Our key idea is to incrementally construct not a single linear order of operations, but a partial order that describes multiple linearizations satisfying the sequential specification. This allows decisions about the ordering of operations to be delayed, mirroring the behaviour of data structure implementations. We formalise our method as a program logic based on rely-guarantee reasoning, and demonstrate its effectiveness by verifying several challenging data structures: the Herlihy-Wing queue, the TS queue and the Optimistic set.
The Parallel Distributed Image Search Engine (ParaDISE)
Venue: arXiv cs.IR 2017
Year: 2017

Image retrieval is a complex task that differs according to the context and the user requirements in any specific field, for example in a medical environment. Search by text is often not possible or optimal and retrieval by the visual content does not always succeed in modelling high-level concepts that a user is looking for. Modern image retrieval techniques consist of multiple steps and aim to retrieve information from large--scale datasets and not only based on global image appearance but local features and if possible in a connection between visual features and text or semantics. This paper presents the Parallel Distributed Image Search Engine (ParaDISE), an image retrieval system that combines visual search with text--based retrieval and that is available as open source and free of charge. The main design concepts of ParaDISE are flexibility, expandability, scalability and interoperability. These concepts constitute the system, able to be used both in real-world applications and as an image retrieval research platform. Apart from the architecture and the implementation of the system, two use cases are described, an application of ParaDISE in retrieval of images from the medical literature and a visual feature evaluation for medical image retrieval. Future steps include the creation of an open source community that will contribute and expand this platform based on the existing parts.
A Hybrid DOS-Tolerant PKC-Based Key Management System for WSNs
Venue: arXiv cs.CR 2017
Year: 2017

Security is a critical and vital task in wireless sensor networks, therefore different key management systems have been proposed, many of which are based on symmetric cryptography. Such systems are very energy efficient, but they lack some other desirable characteristics. On the other hand, systems based on public key cryptography have those desirable characteristics, but they consume more energy. Recently based on authenticated messages from base station a new PKC based key agreement protocol was proposed. We show this method is susceptible to a form of denial of service attack where resources of the network can be exhausted with bogus messages. Then, we propose two different improvements to solve this vulnerability. Simulation results show that these new protocols retain desirable characteristics of the basic method and solve its deficiencies.
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Venue: arXiv cs.CV 2017
Year: 2017

Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal. The majority of existing work relies on manually-provided ILD ROIs to extract sampled 2D image patches from CT slices and, from there, performs patch-based ILD categorization. Acquiring manual ROIs is labor intensive and serves as a bottleneck towards fully-automated CT imaging ILD screening over large-scale populations. Furthermore, despite the considerable high frequency of more than one ILD pattern on a single CT slice, previous works are only designed to detect one ILD pattern per slice or patch. To tackle these two critical challenges, we present multi-label deep convolutional neural networks (CNNs) for detecting ILDs from holistic CT slices (instead of ROIs or sub-images). Conventional single-labeled CNN models can be augmented to cope with the possible presence of multiple ILD pattern labels, via 1) continuous-valued deep regression based robust norm loss functions or 2) a categorical objective as the sum of element-wise binary logistic losses. Our methods are evaluated and validated using a publicly available database of 658 patient CT scans under five-fold cross-validation, achieving promising performance on detecting four major ILD patterns: Ground Glass, Reticular, Honeycomb, and Emphysema. We also investigate the effectiveness of a CNN activation-based deep-feature encoding scheme using Fisher vector encoding, which treats ILD detection as spatially-unordered deep texture classification.
Design and Analysis of Stability-Guaranteed PUFs
Venue: arXiv cs.CR 2017
Year: 2017

The lack of stability is one of the major limitations that constrains PUF from being put in widespread practical use. In this paper, we propose a weak PUF and a strong PUF that are both completely stable with 0% intra-distance. These PUFs are called Locally Enhanced Defectivity (LED)PUF. The source of randomness of a LEDPUF is extracted from locally enhance defectivity without affecting other parts of the chip. A LEDPUF is a pure functional PUF that does not require any kinds of correction schemes as conventional parametric PUFs do. A weak LEDPUF is constructed by forming arrays of Directed Self Assembly (DSA) random connections is presented, and the strong LEDPUF is implemented by using the weak LEDPUF as the key of a keyed-hash message authentication code (HMAC). Our simulation and statistical results show that the entropy of the weak LEDPUF bits is close to ideal, and the inter-distances of both weak and strong LEDPUFs are about 50%, which means that these LEDPUFs are not only stable but also unique. We develop a new unified framework for evaluating the level of security of PUFs, based on password security, by using information theoretic tools of guesswork. The guesswork model allows to quantitatively compare, with a single unified metric, PUFs with varying levels of stability, bias and available side information. In addition, it generalizes other measures to evaluate the security level such as min-entropy and mutual information. We evaluate guesswork-based security of some measured SRAM and Ring Oscillator PUFs as an example and compare them with LEDPUF to show that stability has a more severe impact on the PUF security than biased responses. Furthermore, we find the guesswork of three new problems: Guesswork under probability of attack failure, the guesswork of idealized version of a message authentication code, and the guesswork of strong PUFs that are used for authentication.
Antenna Deployment Method for MIMO Radar under the Situation of Multiple Interference Regions
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, considering multiple interference regions simultaneously, an optimal antenna deployment problem for distributed Multi-Input Multi-Output (MIMO) radar is investigated. The optimal antenna deployment problem is solved by proposing an antenna deployment method based on Multi-Objective Particle Swarm Optimization (MOPSO). Firstly, we construct a multi-objective optimization problem for MIMO radar antenna deployment by choosing the interference power densities of different regions as objective functions. Then, to obtain the optimal deployment result without wasting time and computational resources, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the MOPSO optimization process efficiently when the optimal antenna deployment algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.