OpenRaiser/Intern-Atlas huggingface dataset:
A framework of learning controller with Lyapunov-based constraint and application
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we focus on the problem about direct way to design a stable controller for nonlinear system. A framework of learning controller with Lyapunov-based constraint is proposed, which is intended to transform designing and analyis of a controller to straightforward way to make controller by solving an optimization with the Lyapunov constraint, and which can be a novel way to design a global stability guaranteed controller directly. Firstly, an optimization problem subject to Lyapunov-based constraints is formulated, in which the tracking error is the objective function to minimize. Secondly, the controller combines with PID and feedforward is given in form of neural networks, Finally, the solution of controller method to the optimization problem is analyzed, in which we leverage some deep learning technologies to boost the capbility of solution. The results of two simulations of 2 order linear and nonlinear systems demonstrate that the method proposed has some high performance in speed of convergence, tracking error and smoothness and amplitude of control output. The results of simulation to the nonlinear system with disturbance, noise, uncertainty of parameters and the difference of reference output demostrate that our method has high performance in robustness and generalization.
Generating Automated and Online Test Oracles for Simulink Models with Continuous and Uncertain Behaviors
Venue: arXiv cs.SE 2019
Year: 2019

Test automation requires automated oracles to assess test outputs. For cyber physical systems (CPS), oracles, in addition to be automated, should ensure some key objectives: (i) they should check test outputs in an online manner to stop expensive test executions as soon as a failure is detected; (ii) they should handle time- and magnitude-continuous CPS behaviors; (iii) they should provide a quantitative degree of satisfaction or failure measure instead of binary pass/fail outputs; and (iv) they should be able to handle uncertainties due to CPS interactions with the environment. We propose an automated approach to translate CPS requirements specified in a logic-based language into test oracles specified in Simulink -- a widely-used development and simulation language for CPS. Our approach achieves the objectives noted above through the identification of a fragment of Signal First Order logic (SFOL) to specify requirements, the definition of a quantitative semantics for this fragment and a sound translation of the fragment into Simulink. The results from applying our approach on 11 industrial case studies show that: (i) our requirements language can express all the 98 requirements of our case studies; (ii) the time and effort required by our approach are acceptable, showing potentials for the adoption of our work in practice, and (iii) for large models, our approach can dramatically reduce the test execution time compared to when test outputs are checked in an offline manner.
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles
Venue: arXiv cs.AI 2019
Year: 2019

Spatial puzzles composed of rigid objects, flexible strings and holes offer interesting domains for reasoning about spatial entities that are common in the human daily-life's activities. The goal of this work is to investigate the automated solution of this kind of puzzles adapting an algorithm that combines Answer Set Programming (ASP) with Markov Decision Process (MDP), algorithm oASP(MDP), to use heuristics accelerating the learning process. ASP is applied to represent the domain as an MDP, while a Reinforcement Learning algorithm (Q-Learning) is used to find the optimal policies. In this work, the heuristics were obtained from the solution of relaxed versions of the puzzles. Experiments were performed on deterministic, non-deterministic and non-stationary versions of the puzzles. Results show that the proposed approach can accelerate the learning process, presenting an advantage when compared to the non-heuristic versions of oASP(MDP) and Q-Learning.
ABC: A Cryptocurrency-Focused Threat Modeling Framework
Venue: arXiv cs.CR 2019
Year: 2019

Cryptocurrencies are an emerging economic force, but there are concerns about their security. This is due, in part, to complex collusion cases and new threat vectors that could be missed by conventional security assessment strategies. To address these issues, we propose ABC, an Asset-Based Cryptocurrency-focused threat modeling framework capable of identifying such risks. ABC's key innovation is the use of collusion matrices. A collusion matrix forces a threat model to cover a large space of threat cases while simultaneously manages this process to prevent it from being overly complex. Moreover, ABC derives a system-specific threat categories that account for the financial aspects and the new asset types that cryptocurrencies introduce. We demonstrate that ABC is effective by conducting a user study and by presenting real-world use cases. The user study showed that around 71$\%$ of those who used ABC were able to identify financial security threats, as compared to only 13$\%$ of participants who used the popular framework STRIDE. The use cases further attest to the usefulness of ABC's tools for both cryptocurrency-based systems, as well as a cloud native security technology. This shows the potential of ABC as an effective security assessment technique for various types of large-scale distributed systems.
The Ethics of AI Ethics -- An Evaluation of Guidelines
Venue: arXiv cs.AI 2019
Year: 2019

Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems - and how the effectiveness in the demands of AI ethics can be improved.
A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors
Venue: arXiv cs.SE 2019
Year: 2019

Code comprehension has been recently investigated from physiological and cognitive perspectives through the use of medical imaging. Floyd et al (i.e., the original study) used fMRI to classify the type of comprehension tasks performed by developers and relate such results to their expertise. We replicate the original study using lightweight biometrics sensors which participants (28 undergrads in computer science) wore when performing comprehension tasks on source code and natural language prose. We developed machine learning models to automatically identify what kind of tasks developers are working on leveraging their brain-, heart-, and skin-related signals. The best improvement over the original study performance is achieved using solely the heart signal obtained through a single device (BAC 87% vs. 79.1%). Differently from the original study, we were not able to observe a correlation between the participants' expertise and the classifier performance (tau = 0.16, p = 0.31). Our findings show that lightweight biometric sensors can be used to accurately recognize comprehension tasks opening interesting scenarios for research and practice.
On Boosting Semantic Street Scene Segmentation with Weak Supervision
Venue: arXiv cs.CV 2019
Year: 2019

Training convolutional networks for semantic segmentation requires per-pixel ground truth labels, which are very time consuming and hence costly to obtain. Therefore, in this work, we research and develop a hierarchical deep network architecture and the corresponding loss for semantic segmentation that can be trained from weak supervision, such as bounding boxes or image level labels, as well as from strong per-pixel supervision. We demonstrate that the hierarchical structure and the simultaneous training on strong (per-pixel) and weak (bounding boxes) labels, even from separate datasets, constantly increases the performance against per-pixel only training. Moreover, we explore the more challenging case of adding weak image-level labels. We collect street scene images and weak labels from the immense Open Images dataset to generate the OpenScapes dataset, and we use this novel dataset to increase segmentation performance on two established per-pixel labeled datasets, Cityscapes and Vistas. We report performance gains up to +13.2% mIoU on crucial street scene classes, and inference speed of 20 fps on a Titan V GPU for Cityscapes at 512 x 1024 resolution. Our network and OpenScapes dataset are shared with the research community.
A Study on Smart Online Frame Forging Attacks against Video Surveillance System
Venue: arXiv cs.CR 2019
Year: 2019

Video Surveillance Systems (VSS) have become an essential infrastructural element of smart cities by increasing public safety and countering criminal activities. A VSS is normally deployed in a secure network to prevent access from unauthorized personnel. Compared to traditional systems that continuously record video regardless of the actions in the frame, a smart VSS has the capability of capturing video data upon motion detection or object detection, and then extracts essential information and send to users. This increasing design complexity of the surveillance system, however, also introduces new security vulnerabilities. In this work, a smart, real-time frame duplication attack is investigated. We show the feasibility of forging the video streams in real-time as the camera's surroundings change. The generated frames are compared constantly and instantly to identify changes in the pixel values that could represent motion detection or changes in light intensities outdoors. An attacker (intruder) can remotely trigger the replay of some previously duplicated video streams manually or automatically, via a special quick response (QR) code or when the face of an intruder appears in the camera field of view. A detection technique is proposed by leveraging the real-time electrical network frequency (ENF) reference database to match with the power grid frequency.
Certifying Safety when Implementing Consensus
Venue: arXiv cs.DC 2019
Year: 2019

Ensuring the correctness of distributed system implementations remains a challenging and largely unaddressed problem. In this paper we present a protocol that can be used to certify the safety of consensus implementations. Our proposed protocol is efficient both in terms of the number of additional messages sent and their size, and is designed to operate correctly in the presence of $n-1$ nodes failing in an $n$ node distributed system (assuming fail-stop failures). We also comment on how our construction might be generalized to certify other protocols and invariants.
AgentBuddy: A Contextual Bandit based Decision Support System for Customer Support Agents
Venue: arXiv cs.LG 2019
Year: 2019

In this short paper, we present early insights from a Decision Support System for Customer Support Agents (CSAs) serving customers of a leading accounting software. The system is under development and is designed to provide suggestions to CSAs to make them more productive. A unique aspect of the solution is the use of bandit algorithms to create a tractable human-in-the-loop system that can learn from CSAs in an online fashion. In addition to discussing the ML aspects, we also bring out important insights we gleaned from early feedback from CSAs. These insights motivate our future work and also might be of wider interest to ML practitioners.
Localizing an Unknown Number of mmW Transmitters Under Path Loss Model Uncertainties
Venue: arXiv cs.IT 2019
Year: 2019

This work estimates the position and the transmit power of multiple co-channel wireless transmitters under model uncertainties. The model uncertainties include the number of the targets and the parameters of the path-loss model which enable the system to cope with changes in the weather conditions and in mmW ranges. The problem is solved by an unbiased estimator. The underlying complicated optimization problem has a combinatorial nature that selects the best grid points as the location of the targets. The combinatorial problem is converted to a convex form by means of l1-regularization, which enables locating off-grid targets. Simulations show that the proposed algorithm solves the problem with very high accuracy in the absence of noise and shadowing.
Secrecy Outage of SWIPT in the Presence of Cooperating Eavesdroppers
Venue: arXiv cs.IT 2019
Year: 2019

This work investigates the secrecy performance for a simultaneous wireless information and power transfer system that operates in the presence of cooperating eavesdroppers. The multi-antenna access point's transmission is used for information-decoding by a multi-antenna node and for energy-harvesting (EH) by multiple single-antenna nodes. However, some of the nodes authorized for EH only attempt to eavesdrop on the ongoing information transmission by utilizing a generalized on-off power splitting architecture. We derive a closed-form expression for the secrecy outage probability of the considered Multiple-Input-Multiple-Output Multiple-Eavesdroppers system. Theoretical and simulation results are provided to validate the derived results.
RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance
Venue: arXiv cs.CV 2019
Year: 2019

For enterprise, personal and societal applications, there is now an increasing demand for automated authentication of identity from images using computer vision. However, current authentication technologies are still vulnerable to presentation attacks. We present RoPAD, an end-to-end deep learning model for presentation attack detection that employs unsupervised adversarial invariance to ignore visual distractors in images for increased robustness and reduced overfitting. Experiments show that the proposed framework exhibits state-of-the-art performance on presentation attack detection on several benchmark datasets.
Self-triggered Control for Safety Critical Systems using Control Barrier Functions
Venue: arXiv cs.SY 2019
Year: 2019

We propose a real-time control strategy that combines self-triggered control with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF). Similar to related works proposing CLF-CBF-based controllers, the computation of the controller is achieved by solving a Quadratic Program (QP). However, we propose a Zeroth-Order Hold (ZOH) implementation of the controller that overcomes the main limitations of traditional approaches based on periodic controllers, i.e., unnecessary controller updates and potential violations of the safety constraints. Central to our approach is the novel notion of safe period, which enforces a strong safety guarantee for implementing ZOH control. In addition, we prove that the system does not exhibit a Zeno behavior as it approaches the desired equilibrium.
The parameters of a family of linear codes
Venue: arXiv cs.IT 2019
Year: 2019

A large family of linear codes with flexible parameters from almost bent functions and perfect nonlinear functions are constructed and their parameters are determined. Some constructed linear codes and their related codes are optimal in the sense that they meet certain bounds on linear codes. Applications of these codes in secret sharing schemes and combinational designs are presented.
Molecular Information Delivery in Porous Media
Venue: arXiv cs.IT 2019
Year: 2019

Information delivery via molecular signals is abundant in nature and potentially useful for industry sensing. Many propagation channels (e.g., tissue membranes and catalyst beds) contain porous medium materials and the impact this has on communication performance is not well understood. Here, communication through realistic porous channels is investigated for the first time via statistical breakthrough curves. Assuming that the number of arrived molecules can be approximated as a Gaussian random variable and using fully resolved computational fluid dynamics results for the breakthrough curves, the numerical results for the throughput, mutual information, error probability, and information diversity gain are presented. Using these numerical results, the unique characteristics of the porous medium channel are revealed.
Probability Mass Functions for which Sources have the Maximum Minimum Expected Length
Venue: arXiv cs.IT 2019
Year: 2019

Let $\mathcal{P}_n$ be the set of all probability mass functions (PMFs) $(p_1,p_2,\ldots,p_n)$ that satisfy $p_i>0$ for $1\leq i \leq n$. Define the minimum expected length function $\mathcal{L}_D :\mathcal{P}_n \rightarrow \mathbb{R}$ such that $\mathcal{L}_D (P)$ is the minimum expected length of a prefix code, formed out of an alphabet of size $D$, for the discrete memoryless source having $P$ as its source distribution. It is well-known that the function $\mathcal{L}_D$ attains its maximum value at the uniform distribution. Further, when $n$ is of the form $D^m$, with $m$ being a positive integer, PMFs other than the uniform distribution at which $\mathcal{L}_D$ attains its maximum value are known. However, a complete characterization of all such PMFs at which the minimum expected length function attains its maximum value has not been done so far. This is done in this paper.
LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices
Venue: arXiv cs.CV 2019
Year: 2019

With the increasing computational power of today's workstations, real-time physically-based rendering is within reach, rapidly gaining attention across a variety of domains. These have expeditiously applied to medicine, where it is a powerful tool for intuitive 3D data visualization. Embedded devices such as optical see-through head-mounted displays (OST HMDs) have been a trend for medical augmented reality. However, leveraging the obvious benefits of physically-based rendering remains challenging on these devices because of limited computational power, memory usage, and power consumption. We navigate the compromise between device limitations and image quality to achieve reasonable rendering results by introducing a novel light field that can be sampled in real-time on embedded devices. We demonstrate its applications in medicine and discuss limitations of the proposed method. An open-source version of this project is available at https://github.com/lorafib/LumiPath which provides full insight on implementation and exemplary demonstrational material.
The Web is missing an essential part of infrastructure: an Open Web Index
Venue: arXiv cs.IR 2019
Year: 2019

A proposal for building an index of the Web that separates the infrastructure part of the search engine - the index - from the services part that will form the basis for myriad search engines and other services utilizing Web data on top of a public infrastructure open to everyone.
DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning
Venue: arXiv cs.NE 2019
Year: 2019

The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed. Further, ride-sharing allows sharing costs and, hence, reduces the congestion and emission by making better use of vehicle capacities. In this work, we develop a distributed model-free, DeepPool, that uses deep Q-network (DQN) techniques to learn optimal dispatch policies by interacting with the environment. Further, DeepPool efficiently incorporates travel demand statistics and deep learning models to manage dispatching vehicles for improved ride sharing services. Using real-world dataset of taxi trip records in New York City, DeepPool performs better than other strategies, proposed in the literature, that do not consider ride sharing or do not dispatch the vehicles to regions where the future demand is anticipated. Finally, DeepPool can adapt rapidly to dynamic environments since it is implemented in a distributed manner in which each vehicle solves its own DQN individually without coordination.
Rolling-Shutter-Aware Differential SfM and Image Rectification
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two consecutive frames despite of Rolling Shutter (RS) artifacts. In particular, we show that under constant velocity assumption, the errors induced by the rolling shutter effect can be easily rectified by a linear scaling operation on each optical flow. We further propose a 9-point algorithm to recover the relative pose of a rolling shutter camera that undergoes constant acceleration motion. We demonstrate that the dense depth maps recovered from the relative pose of the RS camera can be used in a RS-aware warping for image rectification to recover high-quality Global Shutter (GS) images. Experiments on both synthetic and real RS images show that our RS-aware differential SfM algorithm produces more accurate results on relative pose estimation and 3D reconstruction from images distorted by RS effect compared to standard SfM algorithms that assume a GS camera model. We also demonstrate that our RS-aware warping for image rectification method outperforms state-of-the-art commercial software products, i.e. Adobe After Effects and Apple Imovie, at removing RS artifacts.
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
Venue: arXiv cs.CL 2019
Year: 2019

This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rule-based, (ii) deep learning and (iii) transfer learning systems for the task of NER on brain imaging reports with a focus on records from patients with stroke. We explore the strengths and weaknesses of each approach, develop rules and train on a common dataset, and evaluate each system's performance on common test sets of Scottish radiology reports from two sources (brain imaging reports in ESS -- Edinburgh Stroke Study data collected by NHS Lothian as well as radiology reports created in NHS Tayside). Our comparison shows that a hand-crafted system is the most accurate way to automatically label EHR, but machine learning approaches can provide a feasible alternative where resources for a manual system are not readily available.
Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative
Venue: arXiv cs.CV 2019
Year: 2019

State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA). We present a novel interactive method called just-enough interaction (JEI), which adds a fast correction step to the automated layered optimal graph segmentation of multiple objects and surfaces (LOGISMOS). After LOGISMOS segmentation in knee MRI, the JEI user interaction does not modify boundary surfaces of the bones and cartilages directly. Local costs of underlying graph nodes are modified instead and the graph is re-optimized, providing globally optimal corrected results. Significant performance improvement ($p \ll 0.001$) was observed when comparing JEI-corrected results to the automated. The algorithm was extended from 3D JEI to longitudinal multi-3D (4D) JEI allowing simultaneous visualization and interaction of multiple-time points of the same patient.