OpenRaiser/Intern-Atlas huggingface dataset:
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
Venue: arXiv cs.LG 2016
Year: 2016

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., $O(d)$) and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified.
CNN-aware Binary Map for General Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin.
Heuristic with elements of tabu search for Truck and Trailer Routing Problem
Venue: arXiv cs.AI 2016
Year: 2016

Vehicle Routing Problem is a well-known problem in logistics and transportation, and the variety of such problems is explained by the fact that it occurs in many real-life situations. It is an NP-hard combinatorial optimization problem and finding an exact optimal solution is practically impossible. In this work, Site-Dependent Truck and Trailer Routing Problem with hard and soft Time Windows and Split Deliveries is considered (SDTTRPTWSD). In this article, we develop a heuristic with the elements of Tabu Search for solving SDTTRPTWSD. The heuristic uses the concept of neighborhoods and visits infeasible solutions during the search. A greedy heuristic is applied to construct an initial solution.
DynIMS: A Dynamic Memory Controller for In-memory Storage on HPC Systems
Venue: arXiv cs.PF 2016
Year: 2016

In order to boost the performance of data-intensive computing on HPC systems, in-memory computing frameworks, such as Apache Spark and Flink, use local DRAM for data storage. Optimizing the memory allocation to data storage is critical to delivering performance to traditional HPC compute jobs and throughput to data-intensive applications sharing the HPC resources. Current practices that statically configure in-memory storage may leave inadequate space for compute jobs or lose the opportunity to utilize more available space for data-intensive applications. In this paper, we explore techniques to dynamically adjust in-memory storage and make the right amount of space for compute jobs. We have developed a dynamic memory controller, DynIMS, which infers memory demands of compute tasks online and employs a feedback-based control model to adapt the capacity of in-memory storage. We test DynIMS using mixed HPCC and Spark workloads on a HPC cluster. Experimental results show that DynIMS can achieve up to 5X performance improvement compared to systems with static memory allocations.
Lower Bounds for Protrusion Replacement by Counting Equivalence Classes
Venue: arXiv cs.DS 2016
Year: 2016

Garnero et al. [SIAM J. Discrete Math. 2015, 29(4):1864--1894] recently introduced a framework based on dynamic programming to make applications of the protrusion replacement technique constructive and to obtain explicit upper bounds on the involved constants. They show that for several graph problems, for every boundary size $t$ one can find an explicit set $\mathcal{R}_t$ of representatives. Any subgraph $H$ with a boundary of size $t$ can be replaced with a representative $H' \in \mathcal{R}_t$ such that the effect of this replacement on the optimum can be deduced from $H$ and $H'$ alone. Their upper bounds on the size of the graphs in $\mathcal{R}_t$ grow triple-exponentially with $t$. In this paper we complement their results by lower bounds on the sizes of representatives, in terms of the boundary size $t$. For example, we show that each set of planar representatives $\mathcal{R}_t$ for Independent Set or Dominating Set contains a graph with $Ω(2^t / \sqrt{4t})$ vertices. This lower bound even holds for sets that only represent the planar subgraphs of bounded pathwidth. To obtain our results we provide a lower bound on the number of equivalence classes of the canonical equivalence relation for Independent Set on $t$-boundaried graphs. We also find an elegant characterization of the number of equivalence classes in general graphs, in terms of the number of monotone functions of a certain kind. Our results show that the number of equivalence classes is at most $2^{2^t}$, improving on earlier bounds of the form $(t+1)^{2^t}$.
k-rAC - a Fine-Grained k-Resilient Access Control Scheme for Distributed Hash Tables
Venue: arXiv cs.CR 2016
Year: 2016

Distributed Hash Tables (DHT) are a common architecture for decentralized applications and, therefore, would be suited for privacy-aware applications. However, currently existing DHTs allow every peer to access any index. To build privacy-aware applications, we need to control this access. In this paper, we present k-rAC, a privacy-aware fine-grained AC for DHTs. For authentication, we present three different mechanisms based on public-key cryptography, zero-knowledge-proofs, and cryptographic hashes. For authorization, we use distributed AC lists. The security of our approach is based on k-resilience. We show that our approach introduces an acceptable overhead and discuss its suitability for different scenarios.
Machine Learning Techniques for Stackelberg Security Games: a Survey
Venue: arXiv cs.GT 2016
Year: 2016

The present survey aims at presenting the current machine learning techniques employed in security games domains. Specifically, we focused on papers and works developed by the Teamcore of University of Southern California, which deepened different directions in this field. After a brief introduction on Stackelberg Security Games (SSGs) and the poaching setting, the rest of the work presents how to model a boundedly rational attacker taking into account her human behavior, then describes how to face the problem of having attacker's payoffs not defined and how to estimate them and, finally, presents how online learning techniques have been exploited to learn a model of the attacker.
Maximizing the Strong Triadic Closure in Split Graphs and Proper Interval Graphs
Venue: arXiv cs.DS 2016
Year: 2016

In social networks the {\sc Strong Triadic Closure} is an assignment of the edges with strong or weak labels such that any two vertices that have a common neighbor with a strong edge are adjacent. The problem of maximizing the number of strong edges that satisfy the strong triadic closure was recently shown to be NP-complete for general graphs. Here we initiate the study of graph classes for which the problem is solvable. We show that the problem admits a polynomial-time algorithm for two unrelated classes of graphs: proper interval graphs and trivially-perfect graphs. To complement our result, we show that the problem remains NP-complete on split graphs, and consequently also on chordal graphs. Thus we contribute to define the first border between graph classes on which the problem is polynomially solvable and on which it remains NP-complete.
Contextual RNN-GANs for Abstract Reasoning Diagram Generation
Venue: arXiv cs.CV 2016
Year: 2016

Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-RNN-GANs), where both the generator and the discriminator modules are based on contextual history (modeled as RNNs) and the adversarial discriminator guides the generator to produce realistic images for the particular time step in the image sequence. We evaluate the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning, where it performs competitively with 10th-grade human performance but there is still scope for interesting improvements as compared to college-grade human performance. We also evaluate our model on a standard video next-frame prediction task, achieving improved performance over comparable state-of-the-art.
The Generalized Reed-Muller codes in a modular group algebra
Venue: arXiv cs.IT 2016
Year: 2016

First we study some properties of the modular group algebra $\mathbb{F}_{p^r}[G]$ where $G$ is the additive group of a Galois ring of characteristic $p^r$ and $\mathbb{F}_{p^r}$ is the field of $p^r$ elements. Secondly a description of the Generalized Reed-Muller codes over $\mathbb{F}_{p^r}$ in $\mathbb{F}_{p^r}[G]$ is presented.
Dynamic Models of Appraisal Networks Explaining Collective Learning
Venue: arXiv cs.SI 2016
Year: 2016

This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality. Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science.
Numerical Approach to Maximize SNR for CDMA Systems
Venue: arXiv cs.IT 2016
Year: 2016

Signal to Noise Ratio (SNR) is an important index for wireless communications. There are many methods for increasing SNR. In CDMA systems, spreading sequences are used. To increase SNR, we have to improve spreading sequences. In classical approaches, the expression of SNR is not differentiable in terms of the parameter of the spreading sequences even in no fading situations. Thus, we express it as the differentiable form and construct the non-linear programing for maximizing SNR. In particular, we solve the problem of maximizing SNR numerically by obtaining spreading sequences whose SNR is guaranteed to be high.
ERA Revisited: Theoretical and Experimental Evaluation
Venue: arXiv cs.DC 2016
Year: 2016

Efficient construction of the suffix tree given an input text is an active area of research from the time it was first introduced. Both theoretical computer scientists and engineers tackled the problem. In this paper we focus on the fastest practical suffix tree construction algorithm to date, ERA. We first provide a theoretical analysis of the algorithm assuming the uniformly random text as an input and using the PEM model of computation with respect to the lower bounds. Secondly, we empirically confirm the theoretical results in different test scenarios exposing the critical terms. Thirdly, we discuss the fundamental characteristics of the input text where the fastest suffix tree construction algorithms in practice fail. This paper serves as a foundation for further research in the parallel text indexing area.
Clustered Planarity with Pipes
Venue: arXiv cs.DS 2016
Year: 2016

We study the version of the C-Planarity problem in which edges connecting the same pair of clusters must be grouped into pipes, which generalizes the Strip Planarity problem. We give algorithms to decide several families of instances for the two variants in which the order of the pipes around each cluster is given as part of the input or can be chosen by the algorithm.
Predicting the consequence of action in digital control state spaces
Venue: arXiv cs.LG 2016
Year: 2016

The objective of this dissertation is to shed light on some fundamental impediments in learning control laws in continuous state spaces. In particular, if one wants to build artificial devices capable to learn motor tasks the same way they learn to classify signals and images, one needs to establish control rules that do not necessitate comparisons between quantities of the surrounding space. We propose, in that context, to take inspiration from the "end effector control" principle, as suggested by neuroscience studies, as opposed to the "displacement control" principle used in the classical control theory.
Revisiting 802.11 Rate Adaptation from Energy Consumption's Perspective
Venue: arXiv cs.NI 2016
Year: 2016

Rate adaptation in 802.11 WLANs has received a lot of attention from the research community, with most of the proposals aiming at maximising throughput based on network conditions. Considering energy consumption, an implicit assumption is that optimality in throughput implies optimality in energy efficiency, but this assumption has been recently put into question. In this paper, we address via analysis and experimentation the relation between throughput performance and energy efficiency in multi-rate 802.11 scenarios. We demonstrate the trade-off between these performance figures, confirming that they may not be simultaneously optimised, and analyse their sensitivity towards the energy consumption parameters of the device. Our results provide the means to design novel rate adaptation schemes that takes energy consumption into account.
Internet Scale Research Studies using SDL-RX
Venue: arXiv cs.CY 2016
Year: 2016

Medical research is one area where collecting data is usually hard and expensive. With the launch of ResearchKit, Apple and Sage Bionetworks made large-scale personal data collection increasingly popular via simple text-based survey apps running on mobile phones. But such surveys can be a barrier in terms of usability and richness of the data being collected. In this paper, we present SDL-R X , a powerful software library designed for ResearchKit that enables study-specific, personalized, and rich visual surveys, for both iOS and Android platforms.
Energy consistent framework for continuously evolving 3D crack propagation
Venue: arXiv cs.CE 2016
Year: 2016

This paper presents a formulation for brittle fracture in 3D elastic solids within the context of configurational mechanics. The local form of the first law of thermodynamics provides a condition for equilibrium of the crack front. The direction of the crack propagation is shown to be given by the direction of the configurational forces on the crack front that maximise the local dissipation. The evolving crack front is continuously resolved by the finite element mesh, without the need for face splitting or the use of enrichment techniques. A monolithic solution strategy is adopted, solving simultaneously for both the material displacements (i.e. crack extension) and the spatial displacements, is adopted. In order to trace the dissipative loading path, an arc-length procedure is developed that controls the incremental crack area growth. In order to maintain mesh quality, smoothing of the mesh is undertaken as a continuous process, together with face flipping, node merging and edge splitting where necessary. Hierarchical basis functions of arbitrary polynomial order are adopted to increase the order of approximation without the need to change the finite element mesh. Performance of the formulation is demonstrated by means of three representative numerical simulations, demonstrating both accuracy and robustness.
Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model
Venue: arXiv cs.CV 2016
Year: 2016

Microscopic pedestrian studies consider detailed interaction of pedestrians to control their movement in pedestrian traffic flow. The tools to collect the microscopic data and to analyze microscopic pedestrian flow are still very much in its infancy. The microscopic pedestrian flow characteristics need to be understood. Manual, semi manual and automatic image processing data collection systems were developed. It was found that the microscopic speed resemble a normal distribution with a mean of 1.38 m/second and standard deviation of 0.37 m/second. The acceleration distribution also bear a resemblance to the normal distribution with an average of 0.68 m/ square second. A physical based microscopic pedestrian simulation model was also developed. Both Microscopic Video Data Collection and Microscopic Pedestrian Simulation Model generate a database called NTXY database. The formulations of the flow performance or microscopic pedestrian characteristics are explained. Sensitivity of the simulation and relationship between the flow performances are described. Validation of the simulation using real world data is then explained through the comparison between average instantaneous speed distributions of the real world data with the result of the simulations. The simulation model is then applied for some experiments on a hypothetical situation to gain more understanding of pedestrian behavior in one way and two way situations, to know the behavior of the system if the number of elderly pedestrian increases and to evaluate a policy of lane-like segregation toward pedestrian crossing and inspects the performance of the crossing. It was revealed that the microscopic pedestrian studies have been successfully applied to give more understanding to the behavior of microscopic pedestrians flow, predict the theoretical and practical situation and evaluate some design policies before its implementation.
Modeling Language Change in Historical Corpora: The Case of Portuguese
Venue: arXiv cs.CL 2016
Year: 2016

This paper presents a number of experiments to model changes in a historical Portuguese corpus composed of literary texts for the purpose of temporal text classification. Algorithms were trained to classify texts with respect to their publication date taking into account lexical variation represented as word n-grams, and morphosyntactic variation represented by part-of-speech (POS) distribution. We report results of 99.8% accuracy using word unigram features with a Support Vector Machines classifier to predict the publication date of documents in time intervals of both one century and half a century. A feature analysis is performed to investigate the most informative features for this task and how they are linked to language change.
Quality of Service (QoS) and Security Provisioning in Cooperative Mobile Ad Hoc Networks (MANETs)
Venue: arXiv cs.NI 2016
Year: 2016

Cooperative communication can improve communication quality in wireless communication networks through strategic relay selection. However, wireless cooperative communication networks are vulnerable to the attacks initiated on relays. Although applying authentication protocols can secure cooperative communication when the selected relay is malicious, better system throughput could be obtained without executing authentication protocol when the selected relay is free from attacker's attack. In this paper, a game theoretic approach is proposed to quantitatively analyze the attacking strategies of the attacker who chooses one relay to attack so as to make rational decision on relay selection and extent of applying authentication protocols, which reaches the trade-off between system security requirement and quality of service (QoS) in wireless cooperative communication networks.
System Identification of NN-based Model Reference Control of RUAV during Hover
Venue: arXiv cs.SY 2016
Year: 2016

UAV control system is a huge and complex system, and to design and test a UAV control system is time-cost and money-cost. This paper considered the simulation of identification of a nonlinear system dynamics using artificial neural networks approach. This experiment develops a neural network model of the plant that we want to control. In the control design stage, experiment uses the neural network plant model to design (or train) the controller. We use Matlab to train the network and simulate the behavior. This chapter provides the mathematical overview of MRC technique and neural network architecture to simulate nonlinear identification of UAV systems. MRC provides a direct and effective method to control a complex system without an equation-driven model. NN approach provides a good framework to implement MEC by identifying complicated models and training a controller for it.
Augmenting Mobile Phone Interaction with Face-Engaged Gestures
Venue: arXiv cs.HC 2016
Year: 2016

The movement of a user's face, easily detected by a smartphone's front camera, is an underexploited input modality for mobile interactions. We introduce three sets of face-engaged interaction techniques for augmenting the traditional mobile inputs, which leverages the combination of the head movements with touch gestures and device motions, all sensed via the phone's built-in sensors. We systematically present the space of design considerations for mobile interactions using one or more of the three input modalities (i.e., touch, motion, and head). The additional affordances of the proposed techniques expand the mobile interaction vocabulary, and can facilitate unique usage scenarios such as one-hand or touch-free interaction. An initial evaluation was conducted and users had positive reactions to the new techniques, indicating the promise of an intuitive and convenient user experience.
A Linear Acceleration Theorem for 2D Cellular Automata on all Complete Neighborhoods
Venue: arXiv cs.FL 2016
Year: 2016

Linear acceleration theorems are known for most computational models. Although such results have been proved for two-dimensional cellular automata working on specific neighborhoods, no general construction was known. We present here a technique of linear acceleration for all two-dimensional languages recognized by cellular automata working on complete neighborhoods.