OpenRaiser/Intern-Atlas huggingface dataset:
A machine vision meta-algorithm for automated recognition of underwater objects using sidescan sonar imagery
Venue: arXiv cs.CV 2019
Year: 2019

This paper details a new method to recognize and detect underwater objects in real-time sidescan sonar data imagery streams, with case-studies of applications for underwater archeology, and ghost fishing gear retrieval. We first synthesize images from sidescan data, apply geometric and radiometric corrections, then use 2D feature detection algorithms to identify point clouds of descriptive visual microfeatures such as corners and edges in the sonar images. We then apply a clustering algorithm on the feature point clouds to group feature sets into regions of interest, reject false positives, yielding a georeferenced inventory of objects.
Variable Record Table: A Run-time Solution for Mitigating Buffer Overflow Attack
Venue: arXiv cs.CR 2019
Year: 2019

We present a novel approach to mitigate buffer overflow attack using Variable Record Table (VRT). Dedicated memory space is used to automatically record base and bound information of variables extracted during runtime. We instrument frame pointer and function(s) related registers to decode variable memory space in stack and heap. We have modified Simplescalar/PISA simulator to extract variables space of six (6) benchmark suites from MiBench. We have tested 290 small C programs (MIT corpus suite) having 22 different buffer overflow vulnerabilities in stack and heap. Experimental results show that our approach can detect buffer overflow attack with zero instruction overhead with the memory space requirement up to 13Kb to maintain VRT for a program with 324 variables.
Active Learning for Risk-Sensitive Inverse Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assuming that humans act according to a random cost with respect to a set of subjectively distorted distributions instead of a fixed one. Such assumption provides the additional flexibility to model human's risk preferences, represented by a risk envelope, in safe-critical tasks. However, like other learning from demonstration techniques, RS-IRL could also suffer inefficient learning due to redundant demonstrations. Inspired by the concept of active learning, this research derives a probabilistic disturbance sampling scheme to enable an RS-IRL agent to query expert support that is likely to expose unrevealed boundaries of the expert's risk envelope. Experimental results confirm that our approach accelerates the convergence of RS-IRL algorithms with lower variance while still guaranteeing unbiased convergence.
Machine Learning based IoT Edge Node Security Attack and Countermeasures
Venue: arXiv cs.CR 2019
Year: 2019

Advances in technology have enabled tremendous progress in the development of a highly connected ecosystem of ubiquitous computing devices collectively called the Internet of Things (IoT). Ensuring the security of IoT devices is a high priority due to the sensitive nature of the collected data. Physically Unclonable Functions (PUFs) have emerged as critical hardware primitive for ensuring the security of IoT nodes. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. Extant approaches to malicious PUF modeling assume that a priori knowledge and physical access to the PUF architecture is available for malicious attack on the IoT node. However, many IoT networks make the underlying assumption that the PUF architecture is sufficiently tamper-proof, both physically and mathematically. In this work, we show that knowledge of the underlying PUF structure is not necessary to clone a PUF. We present a novel non-invasive, architecture independent, machine learning attack for strong PUF designs with a cloning accuracy of 93.5% and improvements of up to 48.31% over an alternative, two-stage brute force attack model. We also propose a machine-learning based countermeasure, discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96.01%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.
Leyenda: An Adaptive, Hybrid Sorting Algorithm for Large Scale Data with Limited Memory
Venue: arXiv cs.DB 2019
Year: 2019

Sorting is the one of the fundamental tasks of modern data management systems. With Disk I/O being the most-accused performance bottleneck and more computation-intensive workloads, it has come to our attention that in heterogeneous environment, performance bottleneck may vary among different infrastructure. As a result, sort kernels need to be adaptive to changing hardware conditions. In this paper, we propose Leyenda, a hybrid, parallel and efficient Radix Most-Significant-Bit (MSB) MergeSort algorithm, with utilization of local thread-level CPU cache and efficient disk/memory I/O. Leyenda is capable of performing either internal or external sort efficiently, based on different I/O and processing conditions. We benchmarked Leyenda with three different workloads from Sort Benchmark, targeting three unique use cases, including internal, partially in-memory and external sort, and we found Leyenda to outperform GNU's parallel in-memory quick/merge sort implementations by up to three times. Leyenda is also ranked the second best external sort algorithm on ACM 2019 SIGMOD programming contest and forth overall.
Dead on Arrival: An Empirical Study of The Bluetooth 5.1 Positioning System
Venue: arXiv cs.NI 2019
Year: 2019

The recently released Bluetooth 5.1 specification introduces fine-grained positioning capabilities in this wireless technology, which is deemed essential to context-/location-based Internet of Things (IoT) applications. In this paper, we evaluate experimentally, for the first time, the accuracy of a positioning system based on the Angle of Arrival (AoA) mechanism adopted by the Bluetooth standard. We first scrutinize the fidelity of angular detection and then assess the feasibility of using angle information from multiple fixed receivers to determine the position of a device. Our results reveal that angular detection is limited to a restricted range. On the other hand, even in a simple deployment with only two antennas per receiver, the AoA-based positioning technique can achieve sub-meter accuracy; yet attaining localization within a few centimeters remains a difficult endeavor. We then demonstrate that a malicious device may be able to easily alter the truthfulness of the measured AoA, by tampering with the packet structure. To counter this protocol weakness, we propose simple remedies that are missing in the standard, but which can be adopted with little effort by manufacturers, to secure the Bluetooth 5.1 positioning system.
Vertical Industries Requirements Analysis & Targeted KPIs for Advanced 5G Trials
Venue: arXiv cs.NI 2019
Year: 2019

Just before the commercial roll out of European 5G networks, 5G trials in realistic environments have been recently initiated all around Europe, as pat of the Phase 3 projects of 5GPPP H2020 program [1]. The goal is to showcase 5G's capabilities and to convince stakeholders about its value adding business potential. The approach is to offer advanced 5G connectivity to real vertical industries and showcase how it enables them to overcome existing 4G network limitations and other long standing issues. The 5G EVE H2020 5GPPP project [2] offers cutting edge 5G end to end facilities (in 4 countries) to diversified vertical industry experimenters. The objective is to understand the needs of prominent industries across Europe and to offer tailor made 5G experience to each and every one of them. This paper contributes to the understanding of vertical services' needs, by offering a thorough and concise vertical requirements analysis methodology, including an examination of the 4G limitations. It also provides real life values for the targeted KPIs of three vertical sectors namely, Smart Industry (4.0), Smart Cities / Health and Smart Energy, while assisting market roll out by prioritizing their connectivity needs.
Learning to Generate Questions with Adaptive Copying Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Automatic question generation is an important problem in natural language processing. In this paper we propose a novel adaptive copying recurrent neural network model to tackle the problem of question generation from sentences and paragraphs. The proposed model adds a copying mechanism component onto a bidirectional LSTM architecture to generate more suitable questions adaptively from the input data. Our experimental results show the proposed model can outperform the state-of-the-art question generation methods in terms of BLEU and ROUGE evaluation scores.
A Signal Separation Method for Exceeding the Channel Capacity of BPSK Input
Venue: arXiv cs.IT 2019
Year: 2019

The field of the coded modulations witnessed its golden era as the simulated achievable bit rates (ABRs) were getting close to the channel capacities of the finite alphabet inputs. However, when working at a single channel, the previous researches are mostly restricted in scope of single input and single output. In contrast, for achieving higher spectral efficiency, the present authors propose a method that builds upon the parallel transmission of two signals that are separated at the receiver. The signal separation is enabled by, so called, the double mapping modulation working through Hamming- to Euclidean space. Consequently, the ABR summation of the two signals is found higher the channel capacity of BPSK input. Both the theoretical- and simulation result confirm this approach.
Value of Information in Probabilistic Logic Programs
Venue: arXiv cs.AI 2019
Year: 2019

In medical decision making, we have to choose among several expensive diagnostic tests such that the certainty about a patient's health is maximized while remaining within the bounds of resources like time and money. The expected increase in certainty in the patient's condition due to performing a test is called the value of information (VoI) for that test. In general, VoI relates to acquiring additional information to improve decision-making based on probabilistic reasoning in an uncertain system. This paper presents a framework for acquiring information based on VoI in uncertain systems modeled as Probabilistic Logic Programs (PLPs). Optimal decision-making in uncertain systems modeled as PLPs have already been studied before. But, acquiring additional information to further improve the results of making the optimal decision has remained open in this context. We model decision-making in an uncertain system with a PLP and a set of top-level queries, with a set of utility measures over the distributions of these queries. The PLP is annotated with a set of atoms labeled as "observable"; in the medical diagnosis example, the observable atoms will be results of diagnostic tests. Each observable atom has an associated cost. This setting of optimally selecting observations based on VoI is more general than that considered by any prior work. Given a limited budget, optimally choosing observable atoms based on VoI is intractable in general. We give a greedy algorithm for constructing a "conditional plan" of observations: a schedule where the selection of what atom to observe next depends on earlier observations. We show that, preempting the algorithm anytime before completion provides a usable result, the result improves over time, and, in the absence of a well-defined budget, converges to the optimal solution.
On the Strong Equivalences of LPMLN Programs
Venue: arXiv cs.LO 2019
Year: 2019

By incorporating the methods of Answer Set Programming (ASP) and Markov Logic Networks (MLN), LPMLN becomes a powerful tool for non-monotonic, inconsistent and uncertain knowledge representation and reasoning. To facilitate the applications and extend the understandings of LPMLN, we investigate the strong equivalences between LPMLN programs in this paper, which is regarded as an important property in the field of logic programming. In the field of ASP, two programs P and Q are strongly equivalent, iff for any ASP program R, the programs P and Q extended by R have the same stable models. In other words, an ASP program can be replaced by one of its strong equivalent without considering its context, which helps us to simplify logic programs, enhance inference engines, construct human-friendly knowledge bases etc. Since LPMLN is a combination of ASP and MLN, the notions of strong equivalences in LPMLN is quite different from that in ASP. Firstly, we present the notions of p-strong and w-strong equivalences between LPMLN programs. Secondly, we present a characterization of the notions by generalizing the SE-model approach in ASP. Finally, we show the use of strong equivalences in simplifying LPMLN programs, and present a sufficient and necessary syntactic condition that guarantees the strong equivalence between a single LPMLN rule and the empty program.
RestKB: A Library of Commonsense Knowledge about Dining at a Restaurant
Venue: arXiv cs.LO 2019
Year: 2019

This paper presents a library of commonsense knowledge, RestKB, developed in modular action language ALM and containing background knowledge relevant to the understanding of restaurant narratives, including stories that describe exceptions to the normal unfolding of such scenarios. We highlight features that KR languages must possess in order to be able to express pertinent knowledge, and expand action language ALM as needed. We show that encoding the knowledge base in ALM facilitates its piecewise construction and testing, and improves the generality and quality of the captured information, in comparison to an initial ASP encoding. The knowledge base was used in a system for reasoning about stereotypical activities, evaluated on the restaurant domain.
Generating Local Search Neighborhood with Synthesized Logic Programs
Venue: arXiv cs.NE 2019
Year: 2019

Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems. Their performance depends strongly on the search space landscape, as defined by a cost function and the selected neighborhood operators. In this paper we present a logic programming based framework, named Noodle, designed to generate bespoke Local Search neighborhoods tailored to specific discrete optimization problems. The proposed system consists of a domain specific language, which is inspired by logic programming, as well as a genetic programming solver, based on the grammar evolution algorithm. We complement the description with a preliminary experimental evaluation, where we synthesize efficient neighborhood operators for the traveling salesman problem, some of which reproduce well-known results.
Distributed Answer Set Coloring: Stable Models Computation via Graph Coloring
Venue: arXiv cs.DC 2019
Year: 2019

Answer Set Programming (ASP) is a famous logic language for knowledge representation, which has been really successful in the last years, as witnessed by the great interest into the development of efficient solvers for ASP. Yet, the great request of resources for certain types of problems, as the planning ones, still constitutes a big limitation for problem solving. Particularly, in the case the program is grounded before the resolving phase, an exponential blow up of the grounding can generate a huge ground file, infeasible for single machines with limited resources, thus preventing even the discovering of a single non-optimal solution. To address this problem, in this paper we present a distributed approach to ASP solving, exploiting distributed computation benefits in order to overcome the just explained limitations. The here presented tool, which is called Distributed Answer Set Coloring (DASC), is a pure solver based on the well-known Graph Coloring algorithm. DASC is part of a bigger project aiming to bring logic programming into a distributed system, started in 2017 by Federico Igne with mASPreduce and continued in 2018 by Pietro Totis with a distributed grounder. In this paper we present a low level implementation of the Graph Coloring algorithm, via the Boost and MPI libraries for C++. Finally, we provide a few results of the very first working version of our tool, at the moment without any strong optimization or heuristic.
Quasi-3D Thermal Simulation of Quench Propagation in Superconducting Magnets
Venue: arXiv cs.CE 2019
Year: 2019

To deal with the multi-scale nature of the quench propagation problem in superconducting magnets, this work presents a quasi-three-dimensional (Q3D) approach combining a two-dimensional finite-element method (FEM) in the transversal cross-section of the magnet for resolving the geometrical details, with a one-dimensional spectral-element method based on orthogonal polynomials in longitudinal direction for accurately and efficiently representing the quench phenomena. The Q3D formulation is elaborated and the idea is illustrated on a thermal benchmark problem. Finally, the method is validated against a conventional 3D simulation carried out by a commercial software. In terms of computational efficiency, it is shown that the proposed Q3D approach is superior to the conventional 3D FEM.
Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment (KEMA) and a dual alignedto-generalized encoders (AGE) model are employed to represent the feature. Then, a support vector machine (SVM) is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods.
Hypergraph partitions
Venue: arXiv cs.DS 2019
Year: 2019

We suggest a reduction of the combinatorial problem of hypergraph partitioning to a continuous optimization problem.
Intelligent Active Queue Management Using Explicit Congestion Notification
Venue: arXiv cs.NI 2019
Year: 2019

As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.
An efficient numerical method for a long-term simulation of heat and mass transfer: the case of an insulated rammed earth wall
Venue: arXiv cs.CE 2019
Year: 2019

Innovative numerical scheme studied in this work enables to overcome two main limitations of Building Performance Simulation (BPS) programs as high computational cost and the choice of a very fine numerical grid. The method, called Super-Time-Stepping (STS), is novel to the state-of-the-art of building simulations, but has already proved to be sufficiently efficient in recent studies from anisotropic heat conduction in astrophysics (Meyer et al. 2014). The given research is focused on employment of this adopted numerical method to model drying of a rammed earth wall with an additional insulation layer. The results show considerable advantage of the STS method compared to standard Euler explicit scheme. It is possible to choose at least 100 times bigger time-steps to maintain high accuracy and to cut computational cost by more than 92% in the same time.
Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers
Venue: arXiv cs.CV 2019
Year: 2019

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones, etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layers and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.
Efficient Computation of Multi-Modal Public Transit Traffic Assignments using ULTRA
Venue: arXiv cs.DS 2019
Year: 2019

We study the problem of computing public transit traffic assignments in a multi-modal setting: Given a public transit timetable, an additional unrestricted transfer mode (in our case walking), and a set of origin-destination pairs, we aim to compute the utilization of every vehicle in the network. While it has been shown that considering unrestricted transfers can significantly improve journeys, computing such journeys efficiently remains algorithmically challenging. Since traffic assignments require the computation of millions of shortest paths, using a multi-modal network has previously not been feasible. A recently proposed approach (ULTRA) enables efficient algorithms with UnLimited TRAnsfers at the cost of a short preprocessing phase. In this work we combine the ULTRA approach with a state-of-the-art assignment algorithm, making multi-modal assignments practical. Careful algorithm engineering results in a new public transit traffic assignment algorithm that even outperforms the algorithm it builds upon, while enabling unlimited walking which has not been considered previously. We conclude our work with an extensive evaluation of the algorithm, showing its versatility and efficiency. On our real world instance, the algorithm computes over 15 million unique journeys in less than 17 seconds.