OpenRaiser/Intern-Atlas huggingface dataset:
Minimum Eccentric Connectivity Index for Graphs with Fixed Order and Fixed Number of Pending Vertices
Venue: arXiv cs.DM 2018
Year: 2018

The eccentric connectivity index of a connected graph $G$ is the sum over all vertices $v$ of the product $d_{G}(v) e_{G}(v)$, where $d_{G}(v)$ is the degree of $v$ in $G$ and $e_{G}(v)$ is the maximum distance between $v$ and any other vertex of $G$. This index is helpful for the prediction of biological activities of diverse nature, a molecule being modeled as a graph where atoms are represented by vertices and chemical bonds by edges. We characterize those graphs which have the smallest eccentric connectivity index among all connected graphs of a given order $n$. Also, given two integers $n$ and $p$ with $p\leq n-1$, we characterize those graphs which have the smallest eccentric connectivity index among all connected graphs of order $n$ with $p$ pending vertices.
Towards JointUD: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes our submission to CoNLL 2018 UD Shared Task. We have extended an LSTM-based neural network designed for sequence tagging to additionally generate character-level sequences. The network was jointly trained to produce lemmas, part-of-speech tags and morphological features. Sentence segmentation, tokenization and dependency parsing were handled by UDPipe 1.2 baseline. The results demonstrate the viability of the proposed multitask architecture, although its performance still remains far from state-of-the-art.
Loop Patterns: Extension of Kleene Star Operator for More Expressive Pattern Matching against Arbitrary Data Structures
Venue: arXiv cs.PL 2018
Year: 2018

The Kleene star operator is an important pattern construct for representing a pattern that repeats multiple times. Due to its simplicity and usefulness, it is imported into various pattern-matching systems other than regular expressions. For example, Mathematica has a similar pattern construct called the repeated pattern. However, they have the following limitations: (i) We cannot change the pattern repeated depending on the current repeat count, and (ii) we cannot apply them to arbitrary data structures such as trees and graphs other than lists. This paper proposes the loop patterns that overcome these limitations. This paper presents numerous working examples and formal semantics of the loop patterns. The examples in this paper are coded in the Egison programming language, which features the customizable non-linear pattern-matching facility for non-free data types.
Using phase instead of optical flow for action recognition
Venue: arXiv cs.CV 2018
Year: 2018

Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective, the Eulerian model of dynamics does not track, but describes local changes. For video, an Eulerian phase-based motion representation, using complex steerable filters, has been successfully employed recently for motion magnification and video frame interpolation. Inspired by these previous works, here, we proposes learning Eulerian motion representations in a deep architecture for action recognition. We learn filters in the complex domain in an end-to-end manner. We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction. We propose a phase-information extraction module, based on these complex filters, that can be used in any network architecture for extracting Eulerian representations. We experimentally analyze the added value of Eulerian motion representations, as extracted by our proposed phase extraction module, and compare with existing motion representations based on optical flow, on the UCF101 dataset.
Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes
Venue: arXiv cs.LO 2018
Year: 2018

The maximum reachability probabilities in a Markov decision process can be computed using value iteration (VI). Recently, simulation-based heuristic extensions of VI have been introduced, such as bounded real-time dynamic programming (BRTDP), which often manage to avoid explicit analysis of the whole state space while preserving guarantees on the computed result. In this paper, we introduce a new class of such heuristics, based on Monte Carlo tree search (MCTS), a technique celebrated in various machine-learning settings. We provide a spectrum of algorithms ranging from MCTS to BRTDP. We evaluate these techniques and show that for larger examples, where VI is no more applicable, our techniques are more broadly applicable than BRTDP with only a minor additional overhead.
A Comparison of Handcrafted and Deep Neural Network Feature Extraction for Classifying Optical Coherence Tomography (OCT) Images
Venue: arXiv cs.CV 2018
Year: 2018

Optical Coherence Tomography allows ophthalmologist to obtain cross-section imaging of eye retina. Assisted with digital image analysis methods, effective disease detection could be performed. Various methods exist to extract feature from OCT images. The proposed study aims to compare the effectiveness of handcrafted and deep neural network features. The evaluated dataset consist of 32339 instances distributed in four classes, namely CNV, DME, DRUSEN, and NORMAL. The methods are Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), DenseNet-169, and ResNet50. As a result, the deep neural network based methods outperformed the handcrafted feature with 88% and 89% accuracy for DenseNet and ResNet compared to 50 % and 42 % for HOG and LBP respectively. The deep neural network based methods also demonstrated better result on the under represented class.
A Global Alignment Kernel based Approach for Group-level Happiness Intensity Estimation
Venue: arXiv cs.CV 2018
Year: 2018

With the progress in automatic human behavior understanding, analysing the perceived affect of multiple people has been recieved interest in affective computing community. Unlike conventional facial expression analysis, this paper primarily focuses on analysing the behaviour of multiple people in an image. The proposed method is based on support vector regression with the combined global alignment kernels (GAKs) to estimate the happiness intensity of a group of people. We first exploit Riesz-based volume local binary pattern (RVLBP) and deep convolutional neural network (CNN) based features for characterizing facial images. Furthermore, we propose to use the GAK for RVLBP and deep CNN features, respectively for explicitly measuring the similarity of two group-level images. Specifically, we exploit the global weight sort scheme to sort the face images from group-level image according to their spatial weights, making an efficient data structure to GAK. Lastly, we propose Multiple kernel learning based on three combination strategies for combining two respective GAKs based on RVLBP and deep CNN features, such that enhancing the discriminative ability of each GAK. Intensive experiments are performed on the challenging group-level happiness intensity database, namely HAPPEI. Our experimental results demonstrate that the proposed approach achieves promising performance for group happiness intensity analysis, when compared with the recent state-of-the-art methods.
SPASS: Scientific Prominence Active Search System with Deep Image Captioning Network
Venue: arXiv cs.LG 2018
Year: 2018

Planetary exploration missions with Mars rovers are complicated, which generally require elaborated task planning by human experts, from the path to take to the images to capture. NASA has been using this process to acquire over 22 million images from the planet Mars. In order to improve the degree of automation and thus efficiency in this process, we propose a system for planetary rovers to actively search for prominence of prespecified scientific features in captured images. Scientists can prespecify such search tasks in natural language and upload them to a rover, on which the deployed system constantly captions captured images with a deep image captioning network and compare the auto-generated captions to the prespecified search tasks by certain metrics so as to prioritize those images for transmission. As a beneficial side effect, the proposed system can also be deployed to ground-based planetary data systems as a content-based search engine.
Optimal Partition of a Tree with Social Distance
Venue: arXiv cs.DS 2018
Year: 2018

We study the problem to find a partition of \textcolor{black}{a} graph $G$ with maximum social welfare based on social distance between vertices in $G$, called MaxSWP. This problem is known to be NP-hard in general. In this paper, we first give a complete characterization of optimal partitions of trees with small diameters. Then, by utilizing these results, we show that MaxSWP can be solved in linear time for trees. Moreover, we show that MaxSWP is NP-hard even for 4-regular graphs.
Constructing Urban Tourism Space Digitally: A Study of Airbnb Listings in Two Berlin Neighborhoods
Venue: arXiv cs.CY 2018
Year: 2018

Over the past decade, Airbnb has emerged as the most popular platform for renting out single rooms or whole apartments. The impact of Airbnb listings on local neighborhoods has been controversially discussed in many cities around the world. The platform's widespread adoption led to changes in urban life, and in particular urban tourism. We argue that urban tourism space can no longer be understood as a fixed, spatial entity. Instead, we follow a constructionist approach and argue that urban tourism space is (re-)produced digitally and collaboratively on online platforms such as Airbnb. We relate our work to a CSCW research direction that is concerned with the role of digital technologies in the production and appropriation of urban space and use the concept of representations as a theoretical lens for our empirical study. In that study, we qualitatively analyzed how the two Berlin neighborhoods Kreuzkölln and City West are digitally constructed by Airbnb hosts in their listing descriptions. Moreover, we quantitatively investigated to what extend mentioned places differ between Airbnb hosts and visitBerlin, the city's destination management organization (DMO). In our qualitative analysis, we found that hosts primarily focus on facilities and places in close proximity to their apartment. In the traditional urban tourism hotspot City West, hosts referred to many places also mentioned by the DMO. In the neighborhood of Kreuzkölln, in contrast, hosts reframed everyday places such as parks or an immigrant food market as the must sees in the area. We discuss how Airbnb hosts contribute to the discursive production of urban neighborhoods and thus co-produce them as tourist destinations. With the emergence of online platforms such as Airbnb, power relations in the construction of tourism space might shift from DMOs towards local residents who are now producing tourism space collaboratively.
Longitudinal Safety Analysis For Heterogeneous Platoon Of Automated And Human Vehicles
Venue: arXiv cs.SY 2018
Year: 2018

With the recent advancement in environmental sensing, vehicle control and vehicle-infrastructure cooperation technologies, more and more autonomous driving companies start to put their intelligent cars into road test. But in the near future, we will face a heterogeneous traffic with both intelligent connected vehicles and human vehicles. In this paper, we investigated the impacts of four collision avoidance algorithms under different intelligent connected vehicles market penetration rate. A customized simulation platform is built, in which a platoon can be initiated with many key parameters. For every short time interval, the dynamics of vehicles are updated and input in a kinematics model. If a collision occurs, the energy loss is calculated to represent the crash severity. Four collision avoidance algorithms are chosen and compared in terms of the crash rate and severity at different market penetration rate and different locations of the platoon. The results generate interesting debates on the issues of heterogeneous platoon safety.
Towards Practical Software Stack Decoding of Polar Codes
Venue: arXiv cs.IT 2018
Year: 2018

The successive cancellation list decoding algorithm for polar codes yields near-optimal decoding performance at the cost of high implementation complexity. The successive cancellation stack algorithm has been shown to provide similar decoding performance at a much lower computational complexity, but software implementations report a sub-par T/P performance. In this technical report, the benefits of the fast simplified successive cancellation list decoder are extended to the stack algorithm, resulting in a throughput increase by two orders of magnitude over the traditional stack decoder.
Energy-efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes an energy-efficient counting rule for distributed detection by ordering sensor transmissions in wireless sensor networks. In the counting rule-based detection in an $N-$sensor network, the local sensors transmit binary decisions to the fusion center, where the number of all $N$ local-sensor detections are counted and compared to a threshold. In the ordering scheme, sensors transmit their unquantized statistics to the fusion center in a sequential manner; highly informative sensors enjoy higher priority for transmission. When sufficient evidence is collected at the fusion center for decision making, the transmissions from the sensors are stopped. The ordering scheme achieves the same error probability as the optimum unconstrained energy approach (which requires observations from all the $N$ sensors) with far fewer sensor transmissions. The scheme proposed in this paper improves the energy efficiency of the counting rule detector by ordering the sensor transmissions: each sensor transmits at a time inversely proportional to a function of its observation. The resulting scheme combines the advantages offered by the counting rule (efficient utilization of the network's communication bandwidth, since the local decisions are transmitted in binary form to the fusion center) and ordering sensor transmissions (bandwidth efficiency, since the fusion center need not wait for all the $N$ sensors to transmit their local decisions), thereby leading to significant energy savings. As a concrete example, the problem of target detection in large-scale wireless sensor networks is considered. Under certain conditions the ordering-based counting rule scheme achieves the same detection performance as that of the original counting rule detector with fewer than $N/2$ sensor transmissions; in some cases, the savings in transmission approaches $(N-1)$.
Resource-driven Substructural Defeasible Logic
Venue: arXiv cs.AI 2018
Year: 2018

Linear Logic and Defeasible Logic have been adopted to formalise different features relevant to agents: consumption of resources, and reasoning with exceptions. We propose a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects, and we discuss the design choices for the framework.
Learning Scripts as Hidden Markov Models
Venue: arXiv cs.CL 2018
Year: 2018

Scripts have been proposed to model the stereotypical event sequences found in narratives. They can be applied to make a variety of inferences including filling gaps in the narratives and resolving ambiguous references. This paper proposes the first formal framework for scripts based on Hidden Markov Models (HMMs). Our framework supports robust inference and learning algorithms, which are lacking in previous clustering models. We develop an algorithm for structure and parameter learning based on Expectation Maximization and evaluate it on a number of natural datasets. The results show that our algorithm is superior to several informed baselines for predicting missing events in partial observation sequences.
Massively Parallel Dynamic Programming on Trees
Venue: arXiv cs.DS 2018
Year: 2018

Dynamic programming is a powerful technique that is, unfortunately, often inherently sequential. That is, there exists no unified method to parallelize algorithms that use dynamic programming. In this paper, we attempt to address this issue in the Massively Parallel Computations (MPC) model which is a popular abstraction of MapReduce-like paradigms. Our main result is an algorithmic framework to adapt a large family of dynamic programs defined over trees. We introduce two classes of graph problems that admit dynamic programming solutions on trees. We refer to them as "(polylog)-expressible" and "linear-expressible" problems. We show that both classes can be parallelized in $O(\log n)$ rounds using a sublinear number of machines and a sublinear memory per machine. To achieve this result, we introduce a series of techniques that can be plugged together. To illustrate the generality of our framework, we implement in $O(\log n)$ rounds of MPC, the dynamic programming solution of graph problems such as minimum bisection, $k$-spanning tree, maximum independent set, longest path, etc., when the input graph is a tree.
Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm
Venue: arXiv cs.CR 2018
Year: 2018

We propose a monospectral image encryption method in which the multispectral color image acquisition by using heterogeneous monospectral cameras. Because the captured monospectral elemental images (EIs) belongs to grayscale image, it is means that the captured EIs can be directly encrypted by the proposed encoding method. Subsequently, the linear cellular automata (CA) and hyperchaotic encoding algorithm are employed to encrypt the captured EIs. Different from previous methods, the proposed method can directly encrypt the multispectral color information rather than having to divide into three color channels (R, G and B), thereby, the proposed method can greatly reduce the encryption calculation.
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions
Venue: arXiv cs.CV 2018
Year: 2018

In-depth scene descriptions and question answering tasks have greatly increased the scope of today's definition of scene understanding. While such tasks are in principle open ended, current formulations primarily focus on describing only the current state of the scenes under consideration. In contrast, in this paper, we focus on the future states of the scenes which are also conditioned on actions. We posit this as a question answering task, where an answer has to be given about a future scene state, given observations of the current scene, and a question that includes a hypothetical action. Our solution is a hybrid model which integrates a physics engine into a question answering architecture in order to anticipate future scene states resulting from object-object interactions caused by an action. We demonstrate first results on this challenging new problem and compare to baselines, where we outperform fully data-driven end-to-end learning approaches.
Characterizing the Power Cost of Virtualization Environments
Venue: arXiv cs.NI 2018
Year: 2018

Virtualization is a key building block of next-generation mobile networks. It can be implemented through two main approaches: traditional virtual machines and lighter-weight containers. Our objective in this paper is to compare these approaches and study the power consumption they are associated with. To this end, we perform a large set of real-world measurements, using both synthetic workloads and real-world applications, and use them to model the relationship between the resource usage of the hosted application and the power consumption of both virtual machines and containers hosting it. We find that containers incur substantially lower power consumption than virtual machines, and that such consumption increases more slowly with the application load.
Threshold-Based Heuristics for Trust Inference in a Social Network
Venue: arXiv cs.SI 2018
Year: 2018

Trust among the users of a social network plays a pivotal role in item recommendation, particularly for the cold start users. Due to the sparse nature of these networks, trust information between any two users may not be always available. To infer the missing trust values, one well-known approach is path based trust estimation, which suggests a user to believe all of its neighbors in the network. In this context, we propose two threshold-based heuristics to overcome the limitation of computation for the path based trust inference. It uses the propagation phenomena of trust and decides a threshold value to select a subset of users for trust propagation. While the first heuristic creates the inferred network considering only the subset of users, the second one is able to preserve the density of the inferred network coming from all users selection. We implement the heuristics and analyze the inferred networks with two real-world datasets. We observe that the proposed threshold based heuristic can recover up to 70 \% of the paths with much less time compared to its deterministic counterpart. We also show that the heuristic based inferred trust is capable of preserving the recommendation accuracy.
A note on reducing the computation time for minimum distance and equivalence check of binary linear codes
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we show the usability of the Gray code with constant weight words for computing linear combinations of codewords. This can lead to a big improvement of the computation time for finding the minimum distance of a code. We have also considered the usefulness of combinatorial $2$-$(t,k,1)$ designs when there are memory limitations to the number of objects (linear codes in particular) that can be tested for equivalence.
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Venue: arXiv cs.CV 2018
Year: 2018

Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammograms to train and validate our model, obtaining an accuracy of 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.
Real-time force control of an SEA-based body weight support unit with the 2-DOF control structure
Venue: arXiv cs.RO 2018
Year: 2018

Body weight support (BWS) is a fundamental technique in rehabilitation. Along with the dramatic progressing of rehabilitation science and engineering, BWS is quickly evolving with new initiatives and has attracted deep research effort in recent years. We have built up a novel gravity offloading system, in which the patient is allowed to move freely in the three-dimensional Cartesian space and receives support against gravity. Thus, the patients, especially for those that suffer from neurological injury such as stroke or spinal cord injury, can focus their residual motor control capabilities on essential therapeutic trainings of balance and gait. The real-time force control performance is critical for the BWS unit to provide suitable support and avoid disturbance. In this work, we have re-designed our BWS unit with a series elastic actuation structure to improve the human-robot interaction performance. Further, the 2 degrees of freedom (2-DOF) control approach was taken for accurate and robust BWS force control. Both simulation and experimental results have validated the efficacy of the BWS design and real-time control methods.