OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic key predistribution in mobile networks resilient to node-capture attacks
Venue: arXiv cs.IT 2019
Year: 2019

We present a comprehensive analysis on connectivity and resilience of secure sensor networks under the widely studied q-composite key predistribution scheme. For network connectivity which ensures that any two sensors can find a path in between for secure communication, we derive the conditions to guarantee connectivity in consideration of (i) node-capture attacks, where the adversary may capture a set of sensors and compromise keys in their memory; (ii) sensor mobility, meaning that sensors can move around so that the network topology may change over time; (iii) physical transmission constraints, under which two sensors have to be within each other's transmission range for communication; (iv) the boundary effect of network fields; and (v) link unreliability, meaning that links are allowed to be unreliable. In contrast, many prior connectivity analyses of secure sensor networks often ignore the above issues. For resilience, although limited studies have presented formal analysis, it is often assumed that the adversary captures a random set of sensors, whereas this paper allows the adversary to capture an arbitrary set of sensors. We present conditions to ensure unassailability and unsplittability in secure sensor networks under the q-composite scheme. Unassailability ensures that an adversary capturing any set consisting of a negligible fraction of sensors can compromise only a negligible fraction of communication links although the adversary may compromise communications between noncaptured nodes which happen to use keys that are shared by captured nodes. Unsplittability means that when a negligible fraction of sensors are captured, almost all of the remaining nodes are still securely connected. Based on the results of connectivity, unassailability and unsplittability, we provide useful guidelines for the design of secure sensor networks.
On resilience and connectivity of secure wireless sensor networks under node capture attacks
Venue: arXiv cs.CR 2019
Year: 2019

Despite much research on probabilistic key predistribution schemes for wireless sensor networks over the past decade, few formal analyses exist that define schemes' resilience to node-capture attacks precisely and under realistic conditions. In this paper, we analyze the resilience of the q-composite key predistribution scheme, which mitigates the node capture vulnerability of the Eschenauer-Gligor scheme in the neighbor discovery phase. We derive scheme parameters to have a desired level of resiliency, and obtain optimal parameters that defend against different adversaries as much as possible. We also show that this scheme can be easily enhanced to achieve the same "perfect resilience" property as in the random pairwise key predistribution for attacks launched after neighbor discovery. Despite considerable attention to this scheme, much prior work explicitly or implicitly uses an incorrect computation for the probability of link compromise under node-capture attacks and ignores real-world transmission constraints of sensor nodes. Moreover, we derive the critical network parameters to ensure connectivity in both the absence and presence of node-capture attacks. We also investigate node replication attacks by analyzing the adversary's optimal strategy.
SDN Enhanced Ethernet VPN for Data Center Interconnect
Venue: arXiv cs.NI 2019
Year: 2019

Ethernet Virtual Private Network (EVPN) is an emerging technology that addresses the networking challenges presented by geo-distributed Data Centers (DCs). One of the major advantages of EVPN over legacy layer 2 VPN solutions is providing All-Active (A-A) mode of operation so that the traffic can truly be multi-homed on Provider Edge (PE) routers. However, A-A mode of operation introduces new challenges. In the case where the Customer Edge (CE) router is multi-homed to one or more PE routers, it is necessary that only one of the PE routers should forward Broadcast, Unknown unicast, and Multicast (BUM) traffic into the DC. The PE router that assumes the primary role for forwarding BUM traffic to the CE device is called the Designated Forwarder (DF). The proposed solution to select the DF in the EVPN standard is based on a distributed algorithm which has a number of drawbacks such as unfairness and intermittent behavior. In this paper, we introduce a Software-Defined Networking (SDN) based architecture for EVPN support, where the SDN controller interacts with EVPN control plane. We demonstrate how our solution mitigates existing problems for DF selection which leads to improved EVPN performance.
Machine Translation in Pronunciation Space
Venue: arXiv cs.CL 2019
Year: 2019

The research in machine translation community focus on translation in text space. However, humans are in fact also good at direct translation in pronunciation space. Some existing translation systems, such as simultaneous machine translation, are inherently more natural and thus potentially more robust by directly translating in pronunciation space. In this paper, we conduct large scale experiments on a self-built dataset with about $20$M En-Zh pairs of text sentences and corresponding pronunciation sentences. We proposed three new categories of translations: $1)$ translating a pronunciation sentence in source language into a pronunciation sentence in target language (P2P-Tran), $2)$ translating a text sentence in source language into a pronunciation sentence in target language (T2P-Tran), and $3)$ translating a pronunciation sentence in source language into a text sentence in target language (P2T-Tran), and compare them with traditional text translation (T2T-Tran). Our experiments clearly show that all $4$ categories of translations have comparable performances, with small and sometimes ignorable differences.
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works conduct the personalized recommendation of tasks to workers via supervised learning methods. However, the majority of them only consider the benefit of either workers or requesters independently. In addition, they cannot handle the dynamic environment and may produce sub-optimal results. To address these issues, we utilize Deep Q-Network (DQN), an RL-based method combined with a neural network to estimate the expected long-term return of recommending a task. DQN inherently considers the immediate and future reward simultaneously and can be updated in real-time to deal with evolving data and dynamic changes. Furthermore, we design two DQNs that capture the benefit of both workers and requesters and maximize the profit of the platform. To learn value functions in DQN effectively, we also propose novel state representations, carefully design the computation of Q values, and predict transition probabilities and future states. Experiments on synthetic and real datasets demonstrate the superior performance of our framework.
Adapting a Container Infrastructure for Autonomous Vehicle Development
Venue: arXiv cs.SE 2019
Year: 2019

In the field of Autonomous Vehicle (AV) development, having a robust yet flexible infrastructure enables code to be continuously integrated and deployed, which in turn accelerates the rapid prototyping process. The platform-agnostic and scalable container infrastructure, often exploited by developers in the cloud domain, presents a viable solution addressing this need in AV development. Developers use tools such as Docker to build containers and Kubernetes to setup container networks. This paper presents a container infrastructure strategy for AV development, discusses the scenarios in which this strategy is useful and performs an analysis on container boundary overhead, and its impact on a Mix Critical System (MCS). An experiment was conducted to compare both operation runtime and communication delay of running a Gaussian Seidel Algorithm with I/O in four different environments: native OS, new container, existing container, and nested container. The comparison reveals that running in containers indeed adds a delay to signal response time, but behaves more deterministically and that nested container does not stack up delays but makes the process less deterministic. With these concerns in mind, the developers may be more informed when setting up the container infrastructure, and take full advantage of the new infrastructure while avoiding some common pitfalls.
Singular points detection with semantic segmentation networks
Venue: arXiv cs.CV 2019
Year: 2019

Singular points detection is one of the most classical and important problem in the field of fingerprint recognition. However, current detection rates of singular points are still unsatisfactory, especially for low-quality fingerprints. Compared with traditional image processing-based detection methods, methods based on deep learning only need the original fingerprint image but not the fingerprint orientation field. In this paper, different from other detection methods based on deep learning, we treat singular points detection as a semantic segmentation problem and just use few data for training. Furthermore, we propose a new convolutional neural network called SinNet to extract the singular regions of interest and then use a blob detection method called SimpleBlobDetector to locate the singular points. The experiments are carried out on the test dataset from SPD2010, and the proposed method has much better performance than the other advanced methods in most aspects. Compared with the state-of-art algorithms in SPD2010, our method achieves an increase of 11% in the percentage of correctly detected fingerprints and an increase of more than 18% in the core detection rate.
Molecular Communication with Anomalous Diffusion in Stochastic Nanonetworks
Venue: arXiv cs.IT 2019
Year: 2019

Molecular communication in nature can incorporate a large number of nano-things in nanonetworks as well as demonstrate how nano-things communicate. This paper presents molecular communication where transmit nanomachines deliver information molecules to a receive nanomachine over an anomalous diffusion channel. By considering a random molecule concentration in a space-time fractional diffusion channel, an analytical expression is derived for the first passage time (FPT) of the molecules. Then, the bit error rate of the lth nearest molecular communication with timing binary modulation is derived in terms of Fox's H-function. In the presence of interfering molecules, the mean and variance of the number of the arrived interfering molecules in a given time interval are presented. Using these statistics, a simple mitigation scheme for timing modulation is provided. The results in this paper provide the network performance on the error probability by averaging over a set of random distances between the communicating links as well as a set of random FPTs caused by the anomalous diffusion of molecules. This result will help in designing and developing molecular communication systems for various design purposes.
REMI: Mining Intuitive Referring Expressions on Knowledge Bases
Venue: arXiv cs.AI 2019
Year: 2019

A referring expression (RE) is a description that identifies a set of instances unambiguously. Mining REs from data finds applications in natural language generation, algorithmic journalism, and data maintenance. Since there may exist multiple REs for a given set of entities, it is common to focus on the most intuitive ones, i.e., the most concise and informative. In this paper we present REMI, a system that can mine intuitive REs on large RDF knowledge bases. Our experimental evaluation shows that REMI finds REs deemed intuitive by users. Moreover we show that REMI is several orders of magnitude faster than an approach based on inductive logic programming.
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking
Venue: arXiv cs.CL 2019
Year: 2019

Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machine learning methods require a large corpus with reliable annotations for the different tasks in the fact-checking process. Having analyzed existing fact-checking corpora, we found that none of them meets these criteria in full. They are either too small in size, do not provide detailed annotations, or are limited to a single domain. Motivated by this gap, we present a new substantially sized mixed-domain corpus with annotations of good quality for the core fact-checking tasks: document retrieval, evidence extraction, stance detection, and claim validation. To aid future corpus construction, we describe our methodology for corpus creation and annotation, and demonstrate that it results in substantial inter-annotator agreement. As baselines for future research, we perform experiments on our corpus with a number of model architectures that reach high performance in similar problem settings. Finally, to support the development of future models, we provide a detailed error analysis for each of the tasks. Our results show that the realistic, multi-domain setting defined by our data poses new challenges for the existing models, providing opportunities for considerable improvement by future systems.
Using Arabic Tweets to Understand Drug Selling Behaviors
Venue: arXiv cs.CY 2019
Year: 2019

Twitter is a popular platform for e-commerce in the Arab region including the sale of illegal goods and services. Social media platforms present multiple opportunities to mine information about behaviors pertaining to both illicit and pharmaceutical drugs and likewise to legal prescription drugs sold without a prescription, i.e., illegally. Recognized as a public health risk, the sale and use of illegal drugs, counterfeit versions of legal drugs, and legal drugs sold without a prescription constitute a widespread problem that is reflected in and facilitated by social media. Twitter provides a crucial resource for monitoring legal and illegal drug sales in order to support the larger goal of finding ways to protect patient safety. We collected our dataset using Arabic keywords. We then categorized the data using four machine learning classifiers. Based on a comparison of the respective results, we assessed the accuracy of each classifier in predicting two important considerations in analysing the extent to which drugs are available on social media: references to drugs for sale and the legality/illegality of the drugs thus advertised. For predicting tweets selling drugs, Support Vector Machine, yielded the highest accuracy rate (96%), whereas for predicting the legality of the advertised drugs, the Naive Bayes, classifier yielded the highest accuracy rate (85%).
Smart City Intelligent System Traffic Congestion Optimization using Internet Of Things
Venue: arXiv cs.NI 2019
Year: 2019

The raising level of traffic imposes a great demand in the growth of intelligent traffic systems. With increase in complexity of alleviation, finding solutions to traffic congestion problem have become one of the challenges. Various optimization techniques have been proposed in literature to meet these challenges. This paper surveys different optimization techniques based on heuristics for automated traffic congestion control. Moreover, an approach based on River Formation Dynamics scheme is introduced to analyze the optimization problem for traffic congestion control and a scheme to extract real time information through Internet of Things is presented for superior efficiency and productivity.
Sequential/Spatial, a Survey of Interactive Information Retrieval Methods for Controlled Experimentation and Evaluation
Venue: arXiv cs.HC 2019
Year: 2019

This survey presents studies that investigated non-spatial (sequential) and spatial information retrieval systems in parallel during a battery of information-seeking tasks with respect to user navigational behaviors, incidental learning, retrieval performance, cognitive abilities & load, direct manipulation of 2D & 3D interfaces, and satisfaction. I consider how information theory has contributed to the concepts of foraging, sense-making, exploration, and how the applied areas of interactive information retrieval (IIR) and cognitive/behavioral psychology have implemented these concepts into architecture, interface design, experimental design, user study, and evaluation methodology.
Using image-extracted features to determine heart rate and blink duration for driver sleepiness detection
Venue: arXiv cs.CV 2019
Year: 2019

Heart rate and blink duration are two vital physiological signals which give information about cardiac activity and consciousness. Monitoring these two signals is crucial for various applications such as driver drowsiness detection. As there are several problems posed by the conventional systems to be used for continuous, long-term monitoring, a remote blink and ECG monitoring system can be used as an alternative. For estimating the blink duration, two strategies are used. In the first approach, pictures of open and closed eyes are fed into an Artificial Neural Network (ANN) to decide whether the eyes are open or close. In the second approach, they are classified and labeled using Linear Discriminant Analysis (LDA). The labeled images are then be used to determine the blink duration. For heart rate variability, two strategies are used to evaluate the passing blood volume: Independent Component Analysis (ICA); and a chrominance based method. Eye recognition yielded 78-92% accuracy in classifying open/closed eyes with ANN and 71-91% accuracy with LDA. Heart rate evaluations had a mean loss of around 16 Beats Per Minute (BPM) for the ICA strategy and 13 BPM for the chrominance based technique.
Mice and Covert Channels
Venue: arXiv cs.CR 2019
Year: 2019

Any secure network is only as secure as its weakest component. With overt channels tightly secured and attackers have started focusing on optical, audible, magnetic, and thermal covert channels to access sensitive systems. In this paper, we present a novel, reliable and bidirectional optical covert channel which uses optical mice. In this channel, the photocell in the mouse is used as a receiver while the LED is used as a transmitter. Our multiple experiments, which use mouse to mouse, mouse to camera and torch to mouse, show that the transmission rate can go as high as 10 bits per second. Additionally, we study the effects of infrared, distance and brightness on mouse input. We also show that infrared mice are susceptible to a similar kind of attack.
Faster Update Time for Turnstile Streaming Algorithms
Venue: arXiv cs.DS 2019
Year: 2019

In this paper, we present a new algorithm for maintaining linear sketches in turnstile streams with faster update time. As an application, we show that $\log n$ \texttt{Count} sketches or \texttt{CountMin} sketches with a constant number of columns (i.e., buckets) can be implicitly maintained in \emph{worst-case} $O(\log^{0.582} n)$ update time using $O(\log n)$ words of space, on a standard word RAM with word-size $w=Θ(\log n)$. The exponent $0.582\approx 2ω/3-1$, where $ω$ is the current matrix multiplication exponent. Due to the numerous applications of linear sketches, our algorithm improves the update time for many streaming problems in turnstile streams, in the high success probability setting, without using more space, including $\ell_2$ norm estimation, $\ell_2$ heavy hitters, point query with $\ell_1$ or $\ell_2$ error, etc. Our algorithm generalizes, with the same update time and space, to maintaining $\log n$ linear sketches, where each sketch partitions the coordinates into $k<\log^{o(1)} n$ buckets using a $c$-wise independent hash function for constant $c$, and maintains the sum of coordinates for each bucket. Moreover, if arbitrary word operations are allowed, the update time can be further improved to $O(\log^{0.187} n)$, where $0.187\approx ω/2-1$. Our update algorithm is adaptive, and it circumvents the non-adaptive cell-probe lower bounds for turnstile streaming algorithms by Larsen, Nelson and Nguy{ê}n (STOC'15). On the other hand, our result also shows that proving unconditional cell-probe lower bound for the update time seems very difficult, even if the space is restricted to be (nearly) the optimum. If $ω=2$, the cell-probe update time of our algorithm would be $\log^{o(1)} n$. Hence, proving any higher lower bound would imply $ω>2$.
Design Considerations for Building Credible Security Testbeds: A Systematic Study of Industrial Control System Use Cases
Venue: arXiv cs.CR 2019
Year: 2019

This paper presents a mapping framework for design factors and implementation process for building credible Industrial Control Systems (ICS) security testbeds. The resilience of ICSs has become a critical concern to operators and governments following widely publicised cyber security events. The inability to apply conventional Information Technology security practice to ICSs further compounds challenges in adequately securing critical systems. To overcome these challenges, and do so without impacting live environments, testbeds for the exploration, development and evaluation of security controls are widely used. However, how a testbed is designed and its attributes, can directly impact not only its viability but also its credibility as a whole. Through a combined systematic and thematic analysis and mapping of ICS security testbed design attributes, this paper suggests that the expertise of human experimenters, design objectives, the implementation approach, architectural coverage, core characteristics, and evaluation methods; are considerations that can help establish or enhance confidence, trustworthiness and acceptance; thus, credibility of ICS security testbeds.
VASTA: A Vision and Language-assisted Smartphone Task Automation System
Venue: arXiv cs.HC 2019
Year: 2019

We present VASTA, a novel vision and language-assisted Programming By Demonstration (PBD) system for smartphone task automation. Development of a robust PBD automation system requires overcoming three key challenges: first, how to make a particular demonstration robust to positional and visual changes in the user interface (UI) elements; secondly, how to recognize changes in the automation parameters to make the demonstration as generalizable as possible; and thirdly, how to recognize from the user utterance what automation the user wishes to carry out. To address the first challenge, VASTA leverages state-of-the-art computer vision techniques, including object detection and optical character recognition, to accurately label interactions demonstrated by a user, without relying on the underlying UI structures. To address the second and third challenges, VASTA takes advantage of advanced natural language understanding algorithms for analyzing the user utterance to trigger the VASTA automation scripts, and to determine the automation parameters for generalization. We run an initial user study that demonstrates the effectiveness of VASTA at clustering user utterances, understanding changes in the automation parameters, detecting desired UI elements, and, most importantly, automating various tasks. A demo video of the system is available here: http://y2u.be/kr2xE-FixjI
Understanding racial bias in health using the Medical Expenditure Panel Survey data
Venue: arXiv cs.LG 2019
Year: 2019

Over the years, several studies have demonstrated that there exist significant disparities in health indicators in the United States population across various groups. Healthcare expense is used as a proxy for health in algorithms that drive healthcare systems and this exacerbates the existing bias. In this work, we focus on the presence of racial bias in health indicators in the publicly available, and nationally representative Medical Expenditure Panel Survey (MEPS) data. We show that predictive models for care management trained using this data inherit this bias. Finally, we demonstrate that this inherited bias can be reduced significantly using simple mitigation techniques.