OpenRaiser/Intern-Atlas huggingface dataset:
A Framework for the Volumetric Integration of Depth Images
Venue: arXiv cs.CV 2014
Year: 2014

Volumetric models have become a popular representation for 3D scenes in recent years. One of the breakthroughs leading to their popularity was KinectFusion, where the focus is on 3D reconstruction using RGB-D sensors. However, monocular SLAM has since also been tackled with very similar approaches. Representing the reconstruction volumetrically as a truncated signed distance function leads to most of the simplicity and efficiency that can be achieved with GPU implementations of these systems. However, this representation is also memory-intensive and limits the applicability to small scale reconstructions. Several avenues have been explored for overcoming this limitation. With the aim of summarizing them and providing for a fast and flexible 3D reconstruction pipeline, we propose a new, unifying framework called InfiniTAM. The core idea is that individual steps like camera tracking, scene representation and integration of new data can easily be replaced and adapted to the needs of the user. Along with the framework we also provide a set of components for scalable reconstruction: two implementations of camera trackers, based on RGB data and on depth data, two representations of the 3D volumetric data, a dense volume and one based on hashes of subblocks, and an optional module for swapping subblocks in and out of the typically limited GPU memory.
Distributed consensus with mixed time/communication bandwidth performance metrics
Venue: arXiv cs.SY 2014
Year: 2014

In this paper we study the inherent trade-off between time and communication complexity for the distributed consensus problem. In our model, communication complexity is measured as the maximum data throughput (in bits per second) sent through the network at a given instant. Such a notion of communication complexity, referred to as bandwidth complexity, is related to the frequency bandwidth a designer should collectively allocate to the agents if they were to communicate via a wireless channel, which represents an important constraint for dense robotic networks. We prove a lower bound on the bandwidth complexity of the consensus problem and provide a consensus algorithm that is bandwidth-optimal for a wide class of consensus functions. We then propose a distributed algorithm that can trade communication complexity versus time complexity as a function of a tunable parameter, which can be adjusted by a system designer as a function of the properties of the wireless communication channel. We rigorously characterize the tunable algorithm's worst-case bandwidth complexity and show that it compares favorably with the bandwidth complexity of well-known consensus algorithm.
A Rate-Distortion Based Secrecy System with Side Information at the Decoders
Venue: arXiv cs.IT 2014
Year: 2014

A secrecy system with side information at the decoders is studied in the context of lossy source compression over a noiseless broadcast channel. The decoders have access to different side information sequences that are correlated with the source. The fidelity of the communication to the legitimate receiver is measured by a distortion metric, as is traditionally done in the Wyner-Ziv problem. The secrecy performance of the system is also evaluated under a distortion metric. An achievable rate-distortion region is derived for the general case of arbitrarily correlated side information. Exact bounds are obtained for several special cases in which the side information satisfies certain constraints. An example is considered in which the side information sequences come from a binary erasure channel and a binary symmetric channel.
A Survey on Small-Area Planar Graph Drawing
Venue: arXiv cs.CG 2014
Year: 2014

We survey algorithms and bounds for constructing planar drawings of graphs in small area.
Programing implementation of the Quine-McCluskey method for minimization of Boolean expression
Venue: arXiv cs.OH 2014
Year: 2014

A Boolean function is a function that produces a Boolean value output by logical calculation of Boolean inputs. It plays key roles in programing algorithms and design of circuits. Minimization of Boolean function is able to optimize the algorithms and circuits. Quine-McCluskey (QM) method is one of the most powerful techniques to simplify Boolean expressions. Compared to other techniques, QM method is more executable and can handle more variables. In addition, QM method is easier to be implemented in computer programs, which makes it an efficient technique. There are several versions of QM simulation codes online, whereas some of them appear to have limitations of variables numbers or lack the consideration of Dont-Care conditions. Here a QM simulation code based on C programing is introduced. Theoretically it is able to handle any number of variables and has taken the Dont-Care conditions into account.
Explain Images with Multimodal Recurrent Neural Networks
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12, Flickr 8K, and Flickr 30K). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
The Role of Chinese-American Scientists in China-US Scientific Collaboration: A Study in Nanotechnology
Venue: arXiv cs.DL 2014
Year: 2014

In this paper, we use bibliometric methods and social network analysis to analyze the pattern of China-US scientific collaboration on individual level in nanotechnology. Results show that Chinese-American scientists have been playing an important role in China-US scientific collaboration. We find that China-US collaboration in nanotechnology mainly occurs between Chinese and Chinese-American scientists. In the co-authorship network, Chinese-American scientists tend to have higher betweenness centrality. Moreover, the series of polices implemented by the Chinese government to recruit oversea experts seems to contribute a lot to China-US scientific collaboration.
On Benchmarking Intrusion Detection Systems in Virtualized Environments
Venue: arXiv cs.CR 2014
Year: 2014

Modern intrusion detection systems (IDSes) for virtualized environments are deployed in the virtualization layer with components inside the virtual machine monitor (VMM) and the trusted host virtual machine (VM). Such IDSes can monitor at the same time the network and host activities of all guest VMs running on top of a VMM being isolated from malicious users of these VMs. We refer to IDSes for virtualized environments as VMM-based IDSes. In this work, we analyze state-of-the-art intrusion detection techniques applied in virtualized environments and architectures of VMM-based IDSes. Further, we identify challenges that apply specifically to benchmarking VMM-based IDSes focussing on workloads and metrics. For example, we discuss the challenge of defining representative baseline benign workload profiles as well as the challenge of defining malicious workloads containing attacks targeted at the VMM. We also discuss the impact of on-demand resource provisioning features of virtualized environments (e.g., CPU and memory hotplugging, memory ballooning) on IDS benchmarking measures such as capacity and attack detection accuracy. Finally, we outline future research directions in the area of benchmarking VMM-based IDSes and of intrusion detection in virtualized environments in general.
A Framework for Real Time Hardware in the loop Simulation for Control Design
Venue: arXiv cs.SY 2014
Year: 2014

This paper presents a simple framework of low cost Kit which can be used in control education and training courses to support hardware in the loop simulation. The kit shows the student or control engineer the effect of delays, noise, and saturation on the control system. The framework is generic and flexible to give the user the ability to test and simulate any controller on any process. The framework uses Matlab environment which gives the user many tools to build is/her system in a fast and accurate way. Some test cases are presented for using the framework on different controllers.
Using Professional Social Networking as an Innovative Method for Data Extraction: The ICT Alumni Index Case Study
Venue: arXiv cs.CY 2014
Year: 2014

The lack of data regarding Information and Communications Technology sector alumni data is a known problem in several countries including Egypt. It is not clear what entry and senior jobs are occupied by alumni and which countries attract them. This affects the planning, design and execution of both the ICT sector and the Education sector. In this research, a joint team is formulated from the Technology Innovation and Entrepreneurship Center TIEC and the Ministry of Higher Education. This team is undertaking extensive analysis of the structure, distribution and development of the ICT skills and employment.
An Anonymous Communication Scheme based on Ring Signature in VANETs
Venue: arXiv cs.CR 2014
Year: 2014

Vehicular ad hoc networks allow vehicles to connect themselves as networks so that cars could communicate with each other. This paper introduces an anonymous communication scheme providing integrity protection, multi-level privacy and auditability. The scheme is based on a certificateless ring signature proposed in this paper, which is contributed to reduce the length of the signature and simplify the key management. In our scheme, vehicles can compose the anonymous group without the help of road-side infrastructure or central authority. The computation overhead is close to a normal signature scheme, so it is efficient in most application scenarios. We also present a small-scale implementation to show the availability of the prototype system.
Stochastic Discriminative EM
Venue: arXiv cs.LG 2014
Year: 2014

Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for discriminative training of probabilistic generative models belonging to the exponential family. In this work, we introduce and justify this algorithm as a stochastic natural gradient descent method, i.e. a method which accounts for the information geometry in the parameter space of the statistical model. We show how this learning algorithm can be used to train probabilistic generative models by minimizing different discriminative loss functions, such as the negative conditional log-likelihood and the Hinge loss. The resulting models trained by sdEM are always generative (i.e. they define a joint probability distribution) and, in consequence, allows to deal with missing data and latent variables in a principled way either when being learned or when making predictions. The performance of this method is illustrated by several text classification problems for which a multinomial naive Bayes and a latent Dirichlet allocation based classifier are learned using different discriminative loss functions.
Single-Source/Sink Network Error Correction Is as Hard as Multiple-Unicast
Venue: arXiv cs.IT 2014
Year: 2014

We study the problem of communicating over a single-source single-terminal network in the presence of an adversary that may jam a single link of the network. If any one of the edges can be jammed, the capacity of such networks is well understood and follows directly from the connection between the minimum cut and maximum flow in single-source single- terminal networks. In this work we consider networks in which some edges cannot be jammed, and show that determining the network communication capacity is at least as hard as solving the multiple-unicast network coding problem for the error-free case. The latter problem is a long standing open problem.
Performance evaluation of the effect ofnoise power jammer on the mobile bluetooth network
Venue: arXiv cs.NI 2014
Year: 2014

Network jammers can uncover the reliability of the wireless networks, where the position of the jammer node permit the network to cope with jamming leveraging varieties of defence strategies. Wireless Bluetooth network is a short range network, where the Bluetooth transceiver is operating in the 2.4 GHz ISM (industrial, scientific and medical) radio band, and a special type of frequency hopping pattern was included with the specification of the Bluetooth technology to offer the necessity requirement for the matching Radio Frequency RF channels. This paper calculates and evaluates the effect of noise power jammer and follower jammer on the Personal Area Network (PAN) Bluetooth network. The results prove that the effect on the Bluetooth network depends on many factors such as power density, distance and immunity technique, which is used in many Bluetooth networks.
Contrastive Unsupervised Word Alignment with Non-Local Features
Venue: arXiv cs.CL 2014
Year: 2014

Word alignment is an important natural language processing task that indicates the correspondence between natural languages. Recently, unsupervised learning of log-linear models for word alignment has received considerable attention as it combines the merits of generative and discriminative approaches. However, a major challenge still remains: it is intractable to calculate the expectations of non-local features that are critical for capturing the divergence between natural languages. We propose a contrastive approach that aims to differentiate observed training examples from noises. It not only introduces prior knowledge to guide unsupervised learning but also cancels out partition functions. Based on the observation that the probability mass of log-linear models for word alignment is usually highly concentrated, we propose to use top-n alignments to approximate the expectations with respect to posterior distributions. This allows for efficient and accurate calculation of expectations of non-local features. Experiments show that our approach achieves significant improvements over state-of-the-art unsupervised word alignment methods.
Low cost page quality factors to detect web spam
Venue: arXiv cs.IR 2014
Year: 2014

Web spam is a big challenge for quality of search engine results. It is very important for search engines to detect web spam accurately. In this paper we present 32 low cost quality factors to classify spam and ham pages on real time basis. These features can be divided in to three categories: (i) URL features, (ii) Content features, and (iii) Link features. We developed a classifier using Resilient Back-propagation learning algorithm of neural network and obtained good accuracy. This classifier can be applied to search engine results on real time because calculation of these features require very little CPU resources.
A Framework for Analysis and Comparison of Dynamic Malware Analysis Tools
Venue: arXiv cs.CR 2014
Year: 2014

Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially overcome these deceits by observing the actual behaviour of the code execution. In this regard, various methods, techniques and tools have been proposed. However, because of the diverse concepts and strategies used in the implementation of these methods and tools, security researchers and malware analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic malware analysis tools. The framework will assist the researchers and analysts to recognize the tools implementation strategy, analysis approach, system wide analysis support and its overall handling of binaries, helping them to select a suitable and effective one for their study and analysis.
On the Optimization of BitTorrent-Like Protocols for Interactive On-Demand Streaming Systems
Venue: arXiv cs.NI 2014
Year: 2014

This paper proposes two novel optimized BitTorrent-like protocols for interactive multimedia streaming: the Simple Interactive Streaming Protocol (SISP) and the Exclusive Interactive Streaming Protocol (EISP). The former chiefly seeks a trade-off between playback continuity and data diversity, while the latter is mostly focused on playback continuity. To assure a thorough and up-to-date approach, related work is carefully examined and important open issues, concerning the design of BitTorrent-like algorithms, are analyzed as well. Through simulations, in a variety of near-real file replication scenarios, the novel protocols are evaluated using distinct performance metrics. Among the major findings, the final results show that the two novel proposals are efficient and, besides, focusing on playback continuity ends up being the best design concept to achieve high quality of service. Lastly, avenues for further research are included at the end of this paper as well.
An Aerial Image Recognition Framework using Discrimination and Redundancy Quality Measure
Venue: arXiv cs.CV 2014
Year: 2014

Aerial image categorization plays an indispensable role in remote sensing and artificial intelligence. In this paper, we propose a new aerial image categorization framework, focusing on organizing the local patches of each aerial image into multiple discriminative subgraphs. The subgraphs reflect both the geometric property and the color distribution of an aerial image. First, each aerial image is decomposed into a collection of regions in terms of their color intensities. Thereby region connected graph (RCG), which models the connection between the spatial neighboring regions, is constructed to encode the spatial context of an aerial image. Second, a subgraph mining technique is adopted to discover the frequent structures in the RCGs constructed from the training aerial images. Thereafter, a set of refined structures are selected among the frequent ones toward being highly discriminative and low redundant. Lastly, given a new aerial image, its sub-RCGs corresponding to the refined structures are extracted. They are further quantized into a discriminative vector for SVM classification. Thorough experimental results validate the effectiveness of the proposed method. In addition, the visualized mined subgraphs show that the discriminative topologies of each aerial image are discovered.
Role of Subgraphs in Epidemics over Finite-Size Networks under the Scaled SIS Process
Venue: arXiv cs.SI 2014
Year: 2014

In previous work, we developed the scaled SIS process, which models the dynamics of SIS epidemics over networks. With the scaled SIS process, we can consider networks that are finite-sized and of arbitrary topology (i.e., we are not restricted to specific classes of networks). We derived for the scaled SIS process a closed-form expression for the time-asymptotic probability distribution of the states of all the agents in the network. This closed-form solution of the equilibrium distribution explicitly exhibits the underlying network topology through its adjacency matrix. This paper determines which network configuration is the most probable. We prove that, for a range of epidemics parameters, this combinatorial problem leads to a submodular optimization problem, which is exactly solvable in polynomial time. We relate the most-probable configuration to the network structure, in particular, to the existence of high density subgraphs. Depending on the epidemics parameters, subset of agents may be more likely to be infected than others; these more-vulnerable agents form subgraphs that are denser than the overall network. We illustrate our results with a 193 node social network and the 4941 node Western US power grid under different epidemics parameters.
Newton-Ellipsoid Method and its Polynomiography
Venue: arXiv cs.NA 2014
Year: 2014

We introduce a new iterative root-finding method for complex polynomials, dubbed {\it Newton-Ellipsoid} method. It is inspired by the Ellipsoid method, a classical method in optimization, and a property of Newton's Method derived in \cite{kalFTA}, according to which at each complex number a half-space can be found containing a root. Newton-Ellipsoid method combines this property, bounds on zeros, together with the plane-cutting properties of the Ellipsoid Method. We present computational results for several examples, as well as corresponding polynomiography. Polynomiography refers to algorithmic visualization of root-finding. Newton's method is the first member of the infinite family of iterations, the {\it basic family}. We also consider general versions of this ellipsoid approach where Newton's method is replaced by a higher-order member of the family such as Halley's method.
Asymptotic Expansions for Gaussian Channels with Feedback under a Peak Power Constraint
Venue: arXiv cs.IT 2014
Year: 2014

This paper investigates the asymptotic expansion for the size of block codes defined for the additive white Gaussian noise (AWGN) channel with feedback under the following setting: A peak power constraint is imposed on every transmitted codeword, and the average error probability of decoding the transmitted message is non-vanishing as the blocklength increases. It is well-known that the presence of feedback does not increase the first-order asymptotics (i.e., capacity) in the asymptotic expansion for the AWGN channel. The main contribution of this paper is a self-contained proof of an upper bound on the asymptotic expansion for the AWGN channel with feedback. Combined with existing achievability results for the AWGN channel, our result implies that the presence of feedback does not improve the second- and third-order asymptotics. An auxiliary contribution is a proof of the strong converse for the parallel Gaussian channels with feedback under a peak power constraint.
S.A.T.E.P. : Synchronous-Asynchronous Tele-education Platform
Venue: arXiv cs.CY 2014
Year: 2014

S.A.T.E.P. means Synchronous Asynchronous Tele education Platform is a software application for educational purposes, with a lot of parametrizing features written entirely from scratch. It aims at the training and examination of computer skills, a platform that can be adjusted to the needs of each lesson. In the application the trainer and the administrator can define the number of the lectures and upload files for each one of them. Furthermore, he can insert, modify and delete questions which are used for evaluation tests but also for the trainees examinations. The trainee can read and download the files of each lesson and also test his knowledge on what he has studied through a series of questions. A chat module where registered users as well as system administrator can discuss and solve questions is also developed.