OpenRaiser/Intern-Atlas huggingface dataset:
Development of System Architecture for E-Government Cloud Platforms
Venue: arXiv cs.CY 2016
Year: 2016

Requirements and criteria for selection of cloud platform and platform visualization are stated by which optimal cloud products will be chosen for the Republic of Kazakhstan e-Government considering quality-price ratio, and also the framework of information and communication architecture will be introduced.
A New Approach to Modeling and Analyzing Security of Networked Systems
Venue: arXiv cs.CR 2016
Year: 2016

Modeling and analyzing security of networked systems is an important problem in the emerging Science of Security and has been under active investigation. In this paper, we propose a new approach towards tackling the problem. Our approach is inspired by the {\em shock model} and {\em random environment} techniques in the Theory of Reliability, while accommodating security ingredients. To the best of our knowledge, our model is the first that can accommodate a certain degree of {\em adaptiveness of attacks}, which substantially weakens the often-made independence and exponential attack inter-arrival time assumptions. The approach leads to a stochastic process model with two security metrics, and we attain some analytic results in terms of the security metrics.
Exclusivity Regularized Machine
Venue: arXiv cs.LG 2016
Year: 2016

It has been recognized that the diversity of base learners is of utmost importance to a good ensemble. This paper defines a novel measurement of diversity, termed as exclusivity. With the designed exclusivity, we further propose an ensemble model, namely Exclusivity Regularized Machine (ERM), to jointly suppress the training error of ensemble and enhance the diversity between bases. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to effectively and efficiently seek the optimal solution of ERM. Theoretical analysis on convergence and global optimality of the proposed algorithm, as well as experiments are provided to reveal the efficacy of our method and show its superiority over state-of-the-art alternatives in terms of accuracy and efficiency.
Multi-Sensor Control for Multi-Target Tracking Using Cauchy-Schwarz Divergence
Venue: arXiv cs.SY 2016
Year: 2016

The paper addresses the problem of multi-sensor control for multi-target tracking via labelled random finite sets (RFS) in the sensor network systems. Based on an information theoretic divergence measure, namely Cauchy-Schwarz (CS) divergence which admits a closed form solution for GLMB densities, we propose two novel multi-sensor control approaches in the framework of generalized Covariance Intersection (GCI). The first joint decision making (JDM) method is optimal and can achieve overall good performance, while the second independent decision making (IDM) method is suboptimal as a fast realization with smaller amount of computations. Simulation in challenging situation is presented to verify the effectiveness of the two proposed approaches.
Perspectives on Software-Defined Networks: interviews with five leading scientists from the networking community
Venue: arXiv cs.NI 2016
Year: 2016

Software defined Networks (SDNs) have drawn much attention both from academia and industry over the last few years. Despite the fact that underlying ideas already exist through areas such as P2P applications and active networks (e.g. virtual topologies and dynamic changes of the network via software), only now has the technology evolved to a point where it is possible to scale the implementations, which justifies the high interest in SDNs nowadays. In this article, the JISA Editors invite five leading scientists from three continents (Raouf Boutaba, David Hutchison, Raj Jain, Ramachandran Ramjee, and Christian Esteve Rothenberg) to give their opinions about what is really new in SDNs. The interviews cover whether big telecom and data center companies need to consider using SDNs, if the new paradigm is changing the way computer networks are understood and taught, and what are the open issues on the topic.
Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform
Venue: arXiv cs.DC 2016
Year: 2016

Using \textit{multiple streams} can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Prior work focuses a lot on GPUs but little is known about the performance impact on (Intel Xeon) Phi. In this work, we apply multiple streams into six real-world applications on Phi. We then systematically evaluate the performance benefits of using multiple streams. The evaluation work is performed at two levels: the microbenchmarking level and the real-world application level. Our experimental results at the microbenchmark level show that data transfers and kernel execution can be overlapped on Phi, while data transfers in both directions are performed in a serial manner. At the real-world application level, we show that both overlappable and non-overlappable applications can benefit from using multiple streams (with an performance improvement of up to 24\%). We also quantify how task granularity and resource granularity impact the overall performance. Finally, we present a set of heuristics to reduce the search space when determining a proper task granularity and resource granularity. To conclude, our evaluation work provides lots of insights for runtime and architecture designers when using multiple streams on Phi.
Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
Venue: arXiv cs.CL 2016
Year: 2016

ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies on a Random Forest algorithm and nine unsupervised corpus-based features. We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are present, ROOT9 achieves an F1 score of 90.7%, against a baseline of 57.2% (vector cosine). When the classification is binary, ROOT9 achieves the following results against the baseline: hypernyms-co-hyponyms 95.7% vs. 69.8%, hypernyms-random 91.8% vs. 64.1% and co-hyponyms-random 97.8% vs. 79.4%. In order to compare the performance with the state-of-the-art, we have also evaluated ROOT9 in subsets of the Weeds et al. (2014) datasets, proving that it is in fact competitive. Finally, we investigated whether the system learns the semantic relation or it simply learns the prototypical hypernyms, as claimed by Levy et al. (2015). The second possibility seems to be the most likely, even though ROOT9 can be trained on negative examples (i.e., switched hypernyms) to drastically reduce this bias.
Multi-Band Image Fusion Based on Spectral Unmixing
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a multi-band image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the likelihoods of the observations. The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem. The joint fusion and unmixing problem is then formulated as maximizing the joint posterior distribution with respect to the endmember signatures and abundance maps, This optimization problem is attacked with an alternating optimization strategy. The two resulting sub-problems are convex and are solved efficiently using the alternating direction method of multipliers. Experiments are conducted for both synthetic and semi-real data. Simulation results show that the proposed unmixing based fusion scheme improves both the abundance and endmember estimation comparing with the state-of-the-art joint fusion and unmixing algorithms.
Usability Comparison of Mouse, Touch and Tangible Inputs for 3D Data Manipulation
Venue: arXiv cs.HC 2016
Year: 2016

We evaluate the performance and usability of mouse-based, touch-based, and tangible interaction for manipulating objects in a 3D virtual environment. This comparison is a step toward a better understanding of the limitations and benefits of these existing interaction techniques, with the ultimate goal of facilitating the integration of different 3D data exploration environments into a single interaction continuum. For this purpose we analyze participants' performance in 3D manipulation using a docking task. We measured completion times, docking precision, as well as subjective criteria such as fatigue, workload, and preference. Our results show that the three input modalities provide similar levels of precision but require different interaction times. We also discuss our qualitative observations as well as people's preferences and put our findings into context of the practical application domain of 3D data analysis environments.
All binomial identities are orderable
Venue: arXiv cs.DM 2016
Year: 2016

The main result of this paper is to show that all binomial identities are orderable. This is a natural statement in the combinatorial theory of finite sets, which can also be applied in distributed computing to derive new strong bounds on the round complexity of the weak symmetry breaking task. Furthermore, we introduce the notion of a fundamental binomial identity and find an infinite family of values, other than the prime powers, for which no fundamental binomial identity can exist.
Reasoning about Algebraic Data Types with Abstractions
Venue: arXiv cs.LO 2016
Year: 2016

Reasoning about functions that operate over algebraic data types is an important problem for a large variety of applications. One application of particular interest is network applications that manipulate or reason about complex message structures, such as XML messages. This paper presents a decision procedure for reasoning about algebraic data types using abstractions that are provided by catamorphisms: fold functions that map instances of algebraic data types to values in a decidable domain. We show that the procedure is sound and complete for a class of catamorphisms that satisfy a generalized sufficient surjectivity condition. Our work extends a previous decision procedure that unrolls catamorphism functions until a solution is found. We use the generalized sufficient surjectivity condition to address an incompleteness in the previous unrolling algorithm (and associated proof). We then propose the categories of monotonic and associative catamorphisms, which we argue provide a more intuitive inclusion test than the generalized sufficient surjectivity condition. We use these notions to address two open problems from previous work: (1) we provide a bound, with respect to formula size, on the number of unrollings necessary for completeness, showing that it is linear for monotonic catamorphisms and exponentially small for associative catamorphisms, and (2) we demonstrate that associative catamorphisms can be combined within a formula while preserving completeness. Our combination results extend the set of problems that can be reasoned about using the catamorphism-based approach. We also describe an implementation of the approach, called RADA, which accepts formulas in an extended version of the SMT-LIB 2.0 syntax. The procedure is quite general and is central to the reasoning infrastructure for Guardol, a domain-specific language for reasoning about network guards.
Synchronization in Networks of Identical Systems via Pinning: Application to Distributed Secondary Control of Microgrids
Venue: arXiv cs.SY 2016
Year: 2016

Motivated by the need for fast synchronized operation of power microgrids, we analyze the problem of single and multiple pinning in networked systems. We derive lower and upper bounds on the algebraic connectivity of the network with respect to the reference signal. These bounds are utilized to devise a suboptimal algorithm with polynomial complexity to find a suitable set of nodes to pin the network effectively and efficiently. The results are applied to secondary voltage pinning control design for a microgrid in islanded operation mode. Comparisons with existing single and multiple pinning strategies clearly demonstrates the efficacy of the obtained results.
Rich Image Captioning in the Wild
Venue: arXiv cs.CV 2016
Year: 2016

We present an image caption system that addresses new challenges of automatically describing images in the wild. The challenges include high quality caption quality with respect to human judgments, out-of-domain data handling, and low latency required in many applications. Built on top of a state-of-the-art framework, we developed a deep vision model that detects a broad range of visual concepts, an entity recognition model that identifies celebrities and landmarks, and a confidence model for the caption output. Experimental results show that our caption engine outperforms previous state-of-the-art systems significantly on both in-domain dataset (i.e. MS COCO) and out of-domain datasets.
An active attack on a distributed Group Key Exchange system
Venue: arXiv cs.CR 2016
Year: 2016

In this work, we introduce an active attack on a Group Key Exchange protocol by Burmester and Desmedt. The attacker obtains a copy of the shared key, which is created in a collaborative manner with the legal users in a communication group.
Content Replication in Large Distributed Caches
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we consider the algorithmic task of content replication and request routing in a distributed caching system consisting of a central server and a large number of caches, each with limited storage and service capabilities. We study a time-slotted system where in each time-slot, a large batch of requests has to be matched to a large number of caches, where each request can be served by any cache which stores the requested content. All requests which cannot be served by the caches are served by fetching the requested content from the central server. The goal is to minimize the transmission rate from the central server. We use a novel mapping between our content replication problem and the Knapsack problem to prove a lower bound on the transmission rate for any content replication policy. Using insights obtained from the mapping, we propose a content replication policy - Knapsack Storage - which achieves this lower bound. While it intuitively makes sense to replicate the more popular contents on a larger number of caches, surprisingly, in certain cases, the Knapsack Storage policy chooses not to replicate the most popular contents on the caches at all.
An Approximation Algorithm for Path Computation and Function Placement in SDNs
Venue: arXiv cs.NI 2016
Year: 2016

We consider the task of computing (combined) function mapping and routing for requests in Software-Defined Networks (SDNs). Function mapping refers to the assignment of nodes in the substrate network to various processing stages that requests must undergo. Routing refers to the assignment of a path in the substrate network that begins in a source node of the request, traverses the nodes that are assigned functions for this request, and ends in a destination of the request. The algorithm either rejects a request or completely serves a request, and its goal is to maximize the sum of the benefits of the served requests. The solution must abide edge and vertex capacities. We follow the framework suggested by Even for the specification of the processing requirements and routing of requests via processing-and-routing graphs (PR-graphs). In this framework, each request has a demand, a benefit, and PR-graph. Our main result is a randomized approximation algorithm for path computation and function placement with the following guarantee. Let $m$ denote the number of links in the substrate network, $\eps$ denote a parameter such that $0< \eps <1$, and $\opt_f$ denote the maximum benefit that can be attained by a fractional solution (one in which requests may be partly served and flow may be split along multiple paths). Let $\cmin$ denote the minimum edge capacity, and let $\dmax$ denote the maximum demand. Let $\Deltamax$ denote an upper bound on the number of processing stages a request undergoes. If $\cmin/(\Deltamax\cdot\dmax)=Ω((\log m)/\eps^2)$, then with probability at least $1-\frac{1}{m}-\textit{exp}(-Ω(\eps^2\cdot \opt_f /(\bmax \cdot \dmax)))$, the algorithm computes a $(1-\eps)$-approximate solution.
Enhanced computation method of topological smoothing on shared memory parallel machines
Venue: arXiv cs.DC 2016
Year: 2016

To prepare images for better segmentation, we need preprocessing applications, such as smoothing, to reduce noise. In this paper, we present an enhanced computation method for smoothing 2D object in binary case. Unlike existing approaches, proposed method provides a parallel computation and better memory management, while preserving the topology (number of connected components) of the original image by using homotopic transformations defined in the framework of digital topology. We introduce an adapted parallelization strategy called split, distribute and merge (SDM) strategy which allows efficient parallelization of a large class of topological operators. To achieve a good speedup and better memory allocation, we cared about task scheduling and managing. Distributed work during smoothing process is done by a variable number of threads. Tests on 2D grayscale image (512*512), using shared memory parallel machine (SMPM) with 8 CPU cores (2 Xeon E5405 running at frequency of 2 GHz), showed an enhancement of 5.2 with cache success rate of 70%.
Parallel image thinning through topological operators on shared memory parallel machines
Venue: arXiv cs.DC 2016
Year: 2016

In this paper, we present a concurrent implementation of a powerful topological thinning operator. This operator is able to act directly over grayscale images without modifying their topology. We introduce an adapted parallelization methodology which combines split, distribute and merge (SDM) strategy and mixed parallelism techniques (data and thread parallelism). The introduced strategy allows efficient parallelization of a large class of topological operators including, mainly, λ-leveling, skeletonization and crest restoring algorithms. To achieve a good speedup, we cared about coordination of threads. Distributed work during thinning process is done by a variable number of threads. Tests on 2D grayscale image (512*512), using shared memory parallel machine (SMPM) with 8 CPU cores (2x Xeon E5405 running at frequency of 2 GHz), showed an enhancement of 6.2 with a maximum achieved cadency of 125 images/s using 8 threads.
Möbius Invariants of Shapes and Images
Venue: arXiv cs.CV 2016
Year: 2016

Identifying when different images are of the same object despite changes caused by imaging technologies, or processes such as growth, has many applications in fields such as computer vision and biological image analysis. One approach to this problem is to identify the group of possible transformations of the object and to find invariants to the action of that group, meaning that the object has the same values of the invariants despite the action of the group. In this paper we study the invariants of planar shapes and images under the Möbius group $\mathrm{PSL}(2,\mathbb{C})$, which arises in the conformal camera model of vision and may also correspond to neurological aspects of vision, such as grouping of lines and circles. We survey properties of invariants that are important in applications, and the known Möbius invariants, and then develop an algorithm by which shapes can be recognised that is Möbius- and reparametrization-invariant, numerically stable, and robust to noise. We demonstrate the efficacy of this new invariant approach on sets of curves, and then develop a Möbius-invariant signature of grey-scale images.
Multi-Metric Energy Efficient Routing in Mobile Ad-Hoc Networks
Venue: arXiv cs.NI 2016
Year: 2016

Increasing network lifetime by reducing energy consumption across the network is one of the major concerns while designing routing protocols for Mobile Ad-Hoc Networks. In this paper, we investigate the main reasons that lead to energy depletion and we introduce appropriate routing metrics in the routing decision scheme to mitigate their effect and increase the network lifetime. For our routing scheme, we take into consideration multiple layer parameters, such as MAC queue utilization, node degree and residual energy. We integrate our multi-metric routing scheme into OLSR, a standard MANET proactive routing protocol. We evaluate via simulations in NS3 the protocol modifications under a range of different static and mobile scenarios. The main observations are that in static and low mobility scenarios our modified routing protocol leads to a significant increase (5\%-20\%) in network lifetime compared to standard OLSR and slightly better performance in terms of Packet Delivery Ratio (PDR).
Minimal Gated Unit for Recurrent Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN's applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU's properties theoretically and empirically.
Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles
Venue: arXiv cs.RO 2016
Year: 2016

In literature, several approaches are trying to make the UAVs fly autonomously i.e., by extracting perspective cues such as straight lines. However, it is only available in well-defined human made environments, in addition to many other cues which require enough texture information. Our main target is to detect and avoid frontal obstacles from a monocular camera using a quad rotor Ar.Drone 2 by exploiting optical flow as a motion parallax, the drone is permitted to fly at a speed of 1 m/s and an altitude ranging from 1 to 4 meters above the ground level. In general, detecting and avoiding frontal obstacle is a quite challenging problem because optical flow has some limitation which should be taken into account i.e. lighting conditions and aperture problem.
A Stratified Analysis of Bayesian Optimization Methods
Venue: arXiv cs.LG 2016
Year: 2016

Empirical analysis serves as an important complement to theoretical analysis for studying practical Bayesian optimization. Often empirical insights expose strengths and weaknesses inaccessible to theoretical analysis. We define two metrics for comparing the performance of Bayesian optimization methods and propose a ranking mechanism for summarizing performance within various genres or strata of test functions. These test functions serve to mimic the complexity of hyperparameter optimization problems, the most prominent application of Bayesian optimization, but with a closed form which allows for rapid evaluation and more predictable behavior. This offers a flexible and efficient way to investigate functions with specific properties of interest, such as oscillatory behavior or an optimum on the domain boundary.