OpenRaiser/Intern-Atlas huggingface dataset:
Recurrent Neural Networks based Obesity Status Prediction Using Activity Data
Venue: arXiv cs.LG 2018
Year: 2018

Obesity is a serious public health concern world-wide, which increases the risk of many diseases, including hypertension, stroke, and type 2 diabetes. To tackle this problem, researchers across the health ecosystem are collecting diverse types of data, which includes biomedical, behavioral and activity, and utilizing machine learning techniques to mine hidden patterns for obesity status improvement prediction. While existing machine learning methods such as Recurrent Neural Networks (RNNs) can provide exceptional results, it is challenging to discover hidden patterns of the sequential data due to the irregular observation time instances. Meanwhile, the lack of understanding of why those learning models are effective also limits further improvements on their architectures. Thus, in this work, we develop a RNN based time-aware architecture to tackle the challenging problem of handling irregular observation times and relevant feature extractions from longitudinal patient records for obesity status improvement prediction. To improve the prediction performance, we train our model using two data sources: (i) electronic medical records containing information regarding lab tests, diagnoses, and demographics; (ii) continuous activity data collected from popular wearables. Evaluations of real-world data demonstrate that our proposed method can capture the underlying structures in users' time sequences with irregularities, and achieve an accuracy of 77-86% in predicting the obesity status improvement.
Bias Amplification in Artificial Intelligence Systems
Venue: arXiv cs.AI 2018
Year: 2018

As Artificial Intelligence (AI) technologies proliferate, concern has centered around the long-term dangers of job loss or threats of machines causing harm to humans. All of this concern, however, detracts from the more pertinent and already existing threats posed by AI today: its ability to amplify bias found in training datasets, and swiftly impact marginalized populations at scale. Government and public sector institutions have a responsibility to citizens to establish a dialogue with technology developers and release thoughtful policy around data standards to ensure diverse representation in datasets to prevent bias amplification and ensure that AI systems are built with inclusion in mind.
Post-disaster 4G/5G Network Rehabilitation using Drones: Solving Battery and Backhaul Issues
Venue: arXiv cs.NI 2018
Year: 2018

Drone-based communications is a novel and attractive area of research in cellular networks. It provides several degrees of freedom in time (available on demand), space (mobile) and it can be used for multiple purposes (self-healing, offloading, coverage extension or disaster recovery). This is why the wide deployment of drone-based communications has the potential to be integrated in the 5G standard. In this paper, we utilize a grid of drones to provide cellular coverage to disaster-struck regions where the terrestrial infrastructure is totally damaged due to earthquake, flood, etc. We propose solutions for the most challenging issues facing drone networks which are limited battery energy and limited backhauling. Our proposed solution based mainly on using three types of drones; tethered backhaul drone (provides high capacity backhauling), untethered powering drone (provides on the fly battery charging) and untethered communication drone (provides cellular connectivity). Hence, an optimization problem is formulated to minimize the energy consumption of drones in addition to determining the placement of these drones and guaranteeing a minimum rate for the users. The simulation results show that we can provide unlimited cellular service to the disaster-affected region under certain conditions with a guaranteed minimum rate for each user.
Uncertainty Aware AI ML: Why and How
Venue: arXiv cs.AI 2018
Year: 2018

This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios. Furthermore, the paper defines uncertainty-awareness and lays out the challenges along with surveying some promising research directions. A theoretical demonstration illustrates how two emerging uncertainty-aware ML and AI technologies could be integrated and be of value for a route planning operation.
Probabilistic Logic Programming with Beta-Distributed Random Variables
Venue: arXiv cs.AI 2018
Year: 2018

We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains. Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.
Decentralized Optimal Merging Control for Connected and Automated Vehicles
Venue: arXiv cs.SY 2018
Year: 2018

This paper addresses the optimal control of Connected and Automated Vehicles (CAVs) arriving from two roads at a merging point where the objective is to jointly minimize the travel time and energy consumption of each CAV. The solution guarantees that a speed-dependent safety constraint is always satisfied, both at the merging point and everywhere within a control zone which precedes it. We first analyze the case of no active constraints and prove that under certain conditions the safety constraint remains inactive, thus significantly simplifying the determination of an explicit decentralized solution. When these conditions do not apply, an explicit solution is still obtained that includes intervals over which the safety constraint is active. Our analysis allows us to study the tradeoff between the two objective function components (travel time and energy within the control zone). Simulation examples are included to compare the performance of the optimal controller to a baseline with human-driven vehicles with results showing improvements in both metrics.
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches. To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches.
A Distributed Algorithm for Solving Linear Algebraic Equations Over Random Networks
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we consider the problem of solving linear algebraic equations of the form $Ax=b$ among multi agents which seek a solution by using local information in presence of random communication topologies. The equation is solved by $m$ agents where each agent only knows a subset of rows of the partitioned matrix $[A,b]$. We formulate the problem such that this formulation does not need the distribution of random interconnection graphs. Therefore, this framework includes asynchronous updates or unreliable communication protocols without B-connectivity assumption. We apply the random Krasnoselskii-Mann iterative algorithm which converges almost surely and in mean square to a solution of the problem for any matrices $A$ and $b$ and any initial conditions of agents' states. We demonestrate that the limit point to which the agents' states converge is determined by the unique solution of a convex optimization problem regardless of the distribution of random communication graphs. Eventually, we show by two numerical examples that the rate of convergence of the algorithm cannot be guaranteed.
The Urban Last Mile Problem: Autonomous Drone Delivery to Your Balcony
Venue: arXiv cs.RO 2018
Year: 2018

Drone delivery has been a hot topic in the industry in the past few years. However, existing approaches either focus on rural areas or rely on centralized drop-off locations from where the last mile delivery is performed. In this paper we tackle the problem of autonomous last mile delivery in urban environments using an off-the-shelf drone. We build a prototype system that is able to fly to the approximate delivery location using GPS and then find the exact drop-off location using visual navigation. The drop-off location could, e.g., be on a balcony or porch, and simply needs to be indicated by a visual marker on the wall or window. We test our system components in simulated environments, including the visual navigation and collision avoidance. Finally, we deploy our drone in a real-world environment and show how it can find the drop-off point on a balcony. To stimulate future research in this topic we open source our code.
On the Constant Price of Anarchy Conjecture
Venue: arXiv cs.GT 2018
Year: 2018

We study Nash equilibria and the price of anarchy in the classic model of Network Creation Games introduced by Fabrikant et al. In this model every agent (node) buys links at a prefixed price $α> 0$ in order to get connected to the network formed by all the $n$ agents. In this setting, the reformulated tree conjecture states that for $α> n$, every Nash equilibrium network is a tree. Moreover, Demaine et al. conjectured that the price of anarchy for this model is constant. Since it was shown that the price of anarchy for trees is constant, if the tree conjecture were true, then the price of anarchy would be constant for $α> n$. Up to now it has been proved that the \PoA is constant $(i)$ in the \emph{lower range}, for $α= O(n^{1-δ})$ with $δ\geq \frac{1}{\log n}$ and $(ii)$ in the \emph{upper range}, for $α> 4n-13$. In contrast, the best upper bound known for the price of anarchy for the remaining range is $2^{O(\sqrt{\log n})}$. In this paper we give new insights into the structure of the Nash equilibria for $α> n$ and we enlarge the range of the parameter $α$ for which the price of anarchy is constant. Specifically, we prove that the price of anarchy is constant for $α> n(1+ε)$ by showing that every equilibrium of diameter greater than some prefixed constant is a tree.
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements
Venue: arXiv cs.LG 2018
Year: 2018

We study the problem of inferring readers' identities and estimating their level of text comprehension from observations of their eye movements during reading. We develop a generative model of individual gaze patterns (scanpaths) that makes use of lexical features of the fixated words. Using this generative model, we derive a Fisher-score representation of eye-movement sequences. We study whether a Fisher-SVM with this Fisher kernel and several reference methods are able to identify readers and estimate their level of text comprehension based on eye-tracking data. While none of the methods are able to estimate text comprehension accurately, we find that the SVM with Fisher kernel excels at identifying readers.
Aspects on Finding the Optimal Practical Programming Exercise for MOOCs
Venue: arXiv cs.SE 2018
Year: 2018

Massive Open Online Courses (MOOCs) focus on manifold subjects, ranging from social sciences over languages to technical skills, and use different means to train the respective skills. MOOCs that are teaching programming skills aim to incorporate practical exercises into the course corpus to give students the hands-on experience necessary for understanding and mastering programming. These exercises, apart from technical challenges, come with a series of questions to be addressed, for example: which fraction of the participants' time should they take (compared to video lectures and other course activities), which difficulty should be aimed for, how much guidance should be offered and how much repetition should be incorporated? The perceived difficulty of a task depends on previous knowledge, supplied hints, the required time for solving and the number of failed attempts the participant made. Furthermore, the detail and accuracy of the problem description, the restrictiveness of the applied test cases and the preparation provided specifically for a given exercise also influence the perceived difficulty of a task. In this paper, we explore the data of three programming courses to find criteria for optimal practical programming exercises. Based on over 3 million executions and scoring runs of participants' task submissions, we aim to deduct exercise difficulty, student patterns in approaching the tasks and potential flaws in task descriptions and preparatory videos. We compare our findings to in class trainings and traditional, mostly video and quiz based MOOCs. Finally, we propose approaches and methods to improve programming courses for participants as well as instructors.
Conducting Feasibility Studies for Knowledge Based Systems
Venue: arXiv cs.AI 2018
Year: 2018

This paper describes how to carry out a feasibility study for a potential knowledge based system application. It discusses factors to be considered under three headings: the business case, the technical feasibility, and stakeholder issues. It concludes with a case study of a feasibility study for a KBS to guide surgeons in diagnosis and treatment of thyroid conditions.
Deep Domain Adaptation under Deep Label Scarcity
Venue: arXiv cs.LG 2018
Year: 2018

The goal behind Domain Adaptation (DA) is to leverage the labeled examples from a source domain so as to infer an accurate model in a target domain where labels are not available or in scarce at the best. A state-of-the-art approach for the DA is due to (Ganin et al. 2016), known as DANN, where they attempt to induce a common representation of source and target domains via adversarial training. This approach requires a large number of labeled examples from the source domain to be able to infer a good model for the target domain. However, in many situations obtaining labels in the source domain is expensive which results in deteriorated performance of DANN and limits its applicability in such scenarios. In this paper, we propose a novel approach to overcome this limitation. In our work, we first establish that DANN reduces the original DA problem into a semi-supervised learning problem over the space of common representation. Next, we propose a learning approach, namely TransDANN, that amalgamates adversarial learning and transductive learning to mitigate the detrimental impact of limited source labels and yields improved performance. Experimental results (both on text and images) show a significant boost in the performance of TransDANN over DANN under such scenarios. We also provide theoretical justification for the performance boost.
Accelerating Test Automation through a Domain Specific Language
Venue: arXiv cs.SE 2018
Year: 2018

Test automation involves the automatic execution of test scripts instead of being manually run. This significantly reduces the amount of manual effort needed and thus is of great interest to the software testing industry. There are two key problems in the existing tools and methods for test automation - a) Creating an automation test script is essentially a code development task, which most testers are not trained on; and b) the automation test script is seldom readable, making the task of maintenance an effort intensive process. We present the Accelerating Test Automation Platform (ATAP) which is aimed at making test automation accessible to non-programmers. ATAP allows the creation of an automation test script through a domain specific language based on English. The English-like test scripts are automatically converted to machine executable code using Selenium WebDriver. ATAP's English-like test script makes it easy for non-programmers to author. The functional flow of an ATAP script is easy to understand as well thus making maintenance simpler (you can understand the flow of the test script when you revisit it many months later). ATAP has been built around the Eclipse ecosystem and has been used in a real-life testing project. We present the details of the implementation of ATAP and the results from its usage in practice.
Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
Venue: arXiv cs.CL 2018
Year: 2018

Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.
Data-compression for Parametrized Counting Problems on Sparse graphs
Venue: arXiv cs.DS 2018
Year: 2018

We study the concept of \emph{compactor}, which may be seen as a counting-analogue of kernelization in counting parameterized complexity. For a function $F:Σ^*\to \Bbb{N}$ and a parameterization $κ: Σ^*\to \Bbb{N}$, a compactor $({\sf P},{\sf M})$ consists of a polynomial-time computable function ${\sf P}$, called \emph{condenser}, and a computable function ${\sf M}$, called \emph{extractor}, such that $F={\sf M}\circ {\sf P}$, and the condensing ${\sf P}(x)$ of $x$ has length at most $s(κ(x))$, for any input $x\in Σ^*.$ If $s$ is a polynomial function, then the compactor is said to be of polynomial-size. Although the study on counting-analogue of kernelization is not unprecedented, it has received little attention so far. We study a family of vertex-certified counting problems on graphs that are MSOL-expressible; that is, for an MSOL-formula $ϕ$ with one free set variable to be interpreted as a vertex subset, we want to count all $A\subseteq V(G)$ where $|A|=k$ and $(G,A)\models ϕ.$ In this paper, we prove that every vertex-certified counting problems on graphs that is \emph{MSOL-expressible} and \emph{treewidth modulable}, when parameterized by $k$, admits a polynomial-size compactor on $H$-topological-minor-free graphs with condensing time $O(k^2n^2)$ and decoding time $2^{O(k)}.$ This implies the existence of an {\sf FPT}-algorithm of running time $O(n^2k^2)+2^{O(k)}.$ All aforementioned complexities are under the Uniform Cost Measure (UCM) model where numbers can be stored in constant space and arithmetic operations can be done in constant time.
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network
Venue: arXiv cs.CV 2018
Year: 2018

Image super-resolution and denoising are two important tasks in image processing that can lead to improvement in image quality. Image super-resolution is the task of mapping a low resolution image to a high resolution image whereas denoising is the task of learning a clean image from a noisy input. We propose and train a single deep learning network that we term as SuRDCNN (super-resolution and denoising convolutional neural network), to perform these two tasks simultaneously . Our model nearly replicates the architecture of existing state-of-the-art deep learning models for super-resolution and denoising. We use the proven strategy of residual learning, as supported by state-of-the-art networks in this domain. Our trained SuRDCNN is capable of super-resolving image in the presence of Gaussian noise, Poisson noise or any random combination of both of these noises.
Privacy in Index Coding: $k$-Limited-Access Schemes
Venue: arXiv cs.IT 2018
Year: 2018

In the traditional index coding problem, a server employs coding to send messages to $n$ clients within the same broadcast domain. Each client already has some messages as side information and requests a particular unknown message from the server. All clients learn the coding matrix so that they can decode and retrieve their requested data. Our starting observation is that, learning the coding matrix can pose privacy concerns: it may enable a client to infer information about the requests and side information of other clients. In this paper, we mitigate this privacy concern by allowing each client to have limited access to the coding matrix. In particular, we design coding matrices so that each client needs only to learn some of (and not all) the rows to decode her requested message. By means of two different privacy metrics, we first show that this approach indeed increases the level of privacy. Based on this, we propose the use of $k$-limited-access schemes: given an index coding scheme that employs $T$ transmissions, we create a $k$-limited-access scheme with $T_k\geq T$ transmissions, and with the property that each client needs at most $k$ transmissions to decode her message. We derive upper and lower bounds on $T_k$ for all values of $k$, and develop deterministic designs for these schemes, which are universal, i.e., independent of the coding matrix. We show that our schemes are order-optimal when either $k$ or $n$ is large. Moreover, we propose heuristics that complement the universal schemes for the case when both $n$ and $k$ are small.
Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation
Venue: arXiv cs.CV 2018
Year: 2018

The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video. Synthetic data often does not generalize to real videos, while unsupervised methods require heuristic losses. Proxy tasks can overcome these issues, and start by training a network for a task for which annotation is easier or which can be trained unsupervised. The trained network is then fine-tuned for the original task using small amounts of ground truth data. Here, we investigate frame interpolation as a proxy task for optical flow. Using real movies, we train a CNN unsupervised for temporal interpolation. Such a network implicitly estimates motion, but cannot handle untextured regions. By fine-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fields. Using this unsupervised pre-training, our network outperforms similar architectures that were trained supervised using synthetic optical flow.