OpenRaiser/Intern-Atlas huggingface dataset:
ITCM: A Real Time Internet Traffic Classifier Monitor
Venue: arXiv cs.NI 2015
Year: 2015

The continual growth of high speed networks is a challenge for real-time network analysis systems. The real time traffic classification is an issue for corporations and ISPs (Internet Service Providers). This work presents the design and implementation of a real time flow-based network traffic classification system. The classifier monitor acts as a pipeline consisting of three modules: packet capture and pre-processing, flow reassembly, and classification with Machine Learning (ML). The modules are built as concurrent processes with well defined data interfaces between them so that any module can be improved and updated independently. In this pipeline, the flow reassembly function becomes the bottleneck of the performance. In this implementation, was used a efficient method of reassembly which results in a average delivery delay of 0.49 seconds, approximately. For the classification module, the performances of the K-Nearest Neighbor (KNN), C4.5 Decision Tree, Naive Bayes (NB), Flexible Naive Bayes (FNB) and AdaBoost Ensemble Learning Algorithm are compared in order to validate our approach.
A General Utility Optimization Framework for Energy Harvesting Based Wireless Communications
Venue: arXiv cs.IT 2015
Year: 2015

In recent years, wireless communication systems are expected to achieve more cost-efficient and sustainable operations by replacing conventional fixed power supplies such as batteries with energy harvesting (EH) devices, which could provide electric energy from renewable energy sources (e.g., solar and wind). Such EH power supplies, however, are random and instable in nature, and as a result impose new challenges on reliable communication design and have triggered substantial research interests in EH based wireless communications. Building upon existing works, in this article, we develop a general optimization framework to maximize the utility of EH wireless communication systems. Our framework encapsulates a variety of design problems, such as throughput maximization and outage probability minimization in single-user and multiuser setups, and provides useful guidelines to the practical design of general EH based communication systems with different assumptions over the knowledge of time-varying wireless channels and EH rates at the transmitters.
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data
Venue: arXiv cs.LG 2015
Year: 2015

High-content screening uses large collections of unlabeled cell image data to reason about genetics or cell biology. Two important tasks are to identify those cells which bear interesting phenotypes, and to identify sub-populations enriched for these phenotypes. This exploratory data analysis usually involves dimensionality reduction followed by clustering, in the hope that clusters represent a phenotype. We propose the use of stacked de-noising auto-encoders to perform dimensionality reduction for high-content screening. We demonstrate the superior performance of our approach over PCA, Local Linear Embedding, Kernel PCA and Isomap.
Secure Transmission in Wireless Sensor Networks Data Using Linear Kolmogorov Watermarking Technique
Venue: arXiv cs.CR 2015
Year: 2015

In Wireless sensor networks (WSNs), All communications between different nodes are sent out in a broadcast fashion. These networks are used in a variety of applications including military, environmental, and smart spaces. Sensors are susceptible to various types of attack, such as data modification, data insertion and deletion, or even physical capture and sensor replacement. Hence security becomes important issue in WSNs. However given the fact that sensors are resources constrained, hence the traditional intensive security algorithms are not well suited for WSNs. This makes traditional security techniques, based on data encryption, not very suitable for WSNs. This paper proposes Linear Kolmogorov watermarking technique for secure data communication in WSNs. We provide a security analysis to show the robustness of the proposed techniques against various types of attacks. This technique is robust against data deletion, packet replication and Sybil attacks
A Design of a Fast Parallel-Pipelined Implementation of AES: Advanced Encryption Standard
Venue: arXiv cs.CR 2015
Year: 2015

The Advanced Encryption Standard (AES) algorithm is a symmetric block cipher which operates on a sequence of blocks each consists of 128, 192 or 256 bits. Moreover, the cipher key for the AES algorithm is a sequence of 128, 192 or 256 bits. AES algorithm has many sources of parallelism. In this paper, a design of parallel AES on the multiprocessor platform is presented. While most of the previous designs either use pipelined parallelization or take advantage of the Mix_Column parallelization, our design is based on combining pipelining of rounds and parallelization of Mix_Column and Add_Round_Key transformations. This model is divided into two levels: the first is pipelining different rounds, while the second is through parallelization of both the Add_Round_Key and the Mix_Column transformations. Previous work proposed for pipelining AES algorithm was based on using nine stages, while, we propose the use of eleven stages in order to exploit the sources of parallelism in both initial and final round. This enhances the system performance compared to previous designs. Using two-levels of parallelization benefits from the highly independency of Add_Round_Key and Mix_Column/ Inv_Mix_Colum transformations. The analysis shows that the parallel implementation of the AES achieves a better performance. The analysis shows that using pipeline increases significantly the degree of improvement for both encryption and decryption by approximately 95%. Moreover, parallelizing Add_Round_Key and Mix_Column/ Inv_Mix_Column transformations increases the degree of improvement by approximately 98%. This leads to the conclusion that the proposed design is scalable and is suitable for real-time applications.
On Achievable Rates for Long-Haul Fiber-Optic Communications
Venue: arXiv cs.IT 2015
Year: 2015

Lower bounds on mutual information (MI) of long-haul optical fiber systems for hard-decision and soft-decision decoding are studied. Ready-to-use expressions to calculate the MI are presented. Extensive numerical simulations are used to quantify how changes in the optical transmitter, receiver, and channel affect the achievable transmission rates of the system. Special emphasis is put to the use of different quadrature amplitude modulation formats, channel spacings, digital back-propagation schemes and probabilistic shaping. The advantages of using MI over the prevailing $Q$-factor as a figure of merit of coded optical systems are also highlighted.
Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines
Venue: arXiv cs.AI 2015
Year: 2015

Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating investigation leads that help identify systems faults, and extends our previous work in this area by leveraging Restricted Boltzmann Machines (RBMs) and contrastive divergence learning to analyse changes in historical feature data. This allows us to heuristically identify the root cause of a fault, and demonstrate an improvement to the state of the art by showing feature data can be predicted heuristically beyond a single instance to include entire sequences of information.
Market Mechanism Design for Profitable On-Demand Transport Services
Venue: arXiv cs.CY 2015
Year: 2015

On-demand transport services in the form of dial-a-ride and taxis are crucial parts of the transport infrastructure in all major cities. However, not all on-demand transport services are equal. In particular, not-for-profit dial-a-ride services with coordinated drivers significantly differ from profit-motivated taxi services with uncoordinated drivers. As such, there are two key threads of research for efficient scheduling, routing, and pricing for passengers: dial-a-ride services (first thread); and taxi services (second thread). Unfortunately, there has been only limited development of algorithms for joint optimization of scheduling, routing, and pricing; largely due to the widespread assumption of fixed pricing. In this paper, we introduce another thread: profit-motivated on-demand transport services with coordinated drivers. To maximize provider profits and the efficiency of the service, we propose a new market mechanism for this new thread of on-demand transport services, where passengers negotiate with the service provider. In contrast to previous work, our mechanism jointly optimizes scheduling, routing, and pricing. Ultimately, we demonstrate that our approach can lead to higher profits, compared with standard fixed price approaches, while maintaining comparable efficiency.
A General Model for MAC Protocol Selection in Wireless Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

Wireless Sensor Networks (WSNs) are being deployed for different applications, each having its own structure, goals and requirements. Medium access control (MAC) protocols play a significant role in WSNs and hence should be tuned to the applications. However, there is no for selecting MAC protocols for different situations. Therefore, it is hard to decide which MAC protocol is good for a given situation. Having a precise model for each MAC protocol, on the other hand, is almost impossible. Using the intuition that the protocols in the same behavioral category perform similarly, our goal in this paper is to introduce a general model that selects the protocol(s) that satisfy the given requirements from the category that performs better for a given context. We define the Combined Performance Function (CPF) to demonstrate the performance of different categories protocols for different contexts. Having the general model, we then discuss the model scalability for adding new protocols, categories, requirements, and performance criteria. Considering energy consumption and delay as the initial performance criteria of the model, we focus on deriving mathematical models for them. The results extracted from CPF are the same as the well-known rule of thumb for the MAC protocols that verifies our model. We validate our models with the help of simulation study. We also implemented the current CPF model in a web page to make the model online and useful.
Handoff Rate and Coverage Analysis in Multi-tier Heterogeneous Networks
Venue: arXiv cs.NI 2015
Year: 2015

This paper analyzes the impact of user mobility in multi-tier heterogeneous networks. We begin by obtaining the handoff rate for a mobile user in an irregular cellular network with the access point locations modeled as a homogeneous Poisson point process. The received signal-to-interference-ratio (SIR) distribution along with a chosen SIR threshold is then used to obtain the probability of coverage. To capture potential connection failures due to mobility, we assume that a fraction of handoffs result in such failures. Considering a multi-tier network with orthogonal spectrum allocation among tiers and the maximum biased average received power as the tier association metric, we derive the probability of coverage for two cases: 1) the user is stationary (i.e., handoffs do not occur, or the system is not sensitive to handoffs); 2) the user is mobile, and the system is sensitive to handoffs. We derive the optimal bias factors to maximize the coverage. We show that when the user is mobile, and the network is sensitive to handoffs, both the optimum tier association and the probability of coverage depend on the user's speed; a speed-dependent bias factor can then adjust the tier association to effectively improve the coverage, and hence system performance, in a fully-loaded network.
Clustering attributed graphs: models, measures and methods
Venue: arXiv cs.SI 2015
Year: 2015

Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.
A Sharp Restricted Isometry Constant Bound of Orthogonal Matching Pursuit
Venue: arXiv cs.IT 2015
Year: 2015

We shall show that if the restricted isometry constant (RIC) $δ_{s+1}(A)$ of the measurement matrix $A$ satisfies $$ δ_{s+1}(A) < \frac{1}{\sqrt{s + 1}}, $$ then the greedy algorithm Orthogonal Matching Pursuit(OMP) will succeed. That is, OMP can recover every $s$-sparse signal $x$ in $s$ iterations from $b = Ax$. Moreover, we shall show the upper bound of RIC is sharp in the following sense. For any given $s \in \N$, we shall construct a matrix $A$ with the RIC $$ δ_{s+1}(A) = \frac{1}{\sqrt{s + 1}} $$ such that OMP may not recover some $s$-sparse signal $x$ in $s$ iterations.
Optimized Training for Net Energy Maximization in Multi-Antenna Wireless Energy Transfer over Frequency-Selective Channel
Venue: arXiv cs.IT 2015
Year: 2015

This paper studies the training design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of "strongest" sub-bands with largest antenna sum-power gains and sends their indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A training design problem is formulated and optimally solved, which takes into account the channel training overhead by maximizing the net harvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal training design. Finally, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme that optimally balances the diversity and beamforming gains achieved in MISO WET systems with limited-energy training.
Estimates for the growth of inverse determinant sums of quasi-orthogonal and number field lattices
Venue: arXiv cs.IT 2015
Year: 2015

Inverse determinant sums appear naturally as a tool for analyzing performance of space-time codes in Rayleigh fading channels. This work will analyze the growth of inverse determinant sums of a family of quasi-orthogonal codes and will show that the growths are in logarithmic class. This is considerably lower than that of comparable number field codes.
Frequency Selective Compressed Sensing
Venue: arXiv cs.IT 2015
Year: 2015

In this paper the authors describe the problem of acquisition of interfered signals and formulate a filtering problem. A frequency-selective compressed sensing technique is proposed as a solution to this problem. Signal acquisition is critical in facilitating frequency-selective compressed sensing. The authors propose a filtering compressed sensing parameter, which allows to assess if a given acquisition process makes frequency-selective compressed sensing possible for a given filtering problem. A numerical experiment which shows how the described method works in practice is conducted.
The Hunt for a Red Spider: Conjunctive Query Determinacy Is Undecidable
Venue: arXiv cs.DB 2015
Year: 2015

We solve a well known, long-standing open problem in relational databases theory, showing that the conjunctive query determinacy problem (in its "unrestricted" version) is undecidable.
Optimization of Energy Harvesting MISO Communication System with Feedback
Venue: arXiv cs.IT 2015
Year: 2015

Optimization of a point-to-point (p2p) multipleinput single-output (MISO) communication system is considered when both the transmitter (TX) and the receiver (RX) have energy harvesting (EH) capabilities. The RX is interested in feeding back the channel state information (CSI) to the TX to help improve the transmission rate. The objective is to maximize the throughput by a deadline, subject to the EH constraints at the TX and the RX. The throughput metric considered is an upper bound on the ergodic rate of the MISO channel with beamforming and limited feedback. Feedback bit allocation and transmission policies that maximize the upper bound on the ergodic rate are obtained. Tools from majorization theory are used to simplify the formulated optimization problems. Optimal policies obtained for the modified problem outperform the naive scheme in which no intelligent management of energy is performed.
Quantifying Scripts: Defining metrics of characters for quantitative and descriptive analysis
Venue: arXiv cs.CL 2015
Year: 2015

Analysis of scripts plays an important role in paleography and in quantitative linguistics. Especially in the field of digital paleography quantitative features are much needed to differentiate glyphs. We describe an elaborate set of metrics that quantify qualitative information contained in characters and hence indirectly also quantify the scribal features. We broadly divide the metrics into several categories and describe each individual metric with its underlying qualitative significance. The metrics are largely derived from the related area of gesture design and recognition. We also propose several novel metrics. The proposed metrics are soundly grounded on the principles of handwriting production and handwriting analysis. These computed metrics could serve as descriptors for scripts and also be used for comparing and analyzing scripts. We illustrate some quantitative analysis based on the proposed metrics by applying it to the paleographic evolution of the medieval Tamil script from Brahmi. We also outline future work.
Some New Results on Binary Relations
Venue: arXiv cs.DM 2015
Year: 2015

It is well known that if a function from set A to set B has a right inverse then the function is a surjection and the right inverse is an injection. For finite sets, the number of functions, injections, and surjections can also be counted. Relations generalize functions: do similar results exist for relations? This paper proves several new results concerning binary relations. For finite sets, we derive formulas for the number of right total, right unique, left total, and left unique relations. We also provide formulas that count the number of relations that are both right unique and left unique; right unique and right total; and left unique and left total. We conclude by discussing the probability that a relation selected at random is right unique or right total.
$NP/CLP$ Equivalence: A Phenomenon Hidden Among Sparsity Models for Information Processing
Venue: arXiv cs.IT 2015
Year: 2015

It is proved in this paper that to every underdetermined linear system $Ax=b$ there corresponds a constant $p(A,b)>0$ such that every solution to the $l_p$-norm minimization problem also solves the $l_0$-norm minimization problem whenever $0<p<p(A,b)$. This phenomenon is named $NP/CLP$ equivalence.
The ModelCC Model-Driven Parser Generator
Venue: arXiv cs.PL 2015
Year: 2015

Syntax-directed translation tools require the specification of a language by means of a formal grammar. This grammar must conform to the specific requirements of the parser generator to be used. This grammar is then annotated with semantic actions for the resulting system to perform its desired function. In this paper, we introduce ModelCC, a model-based parser generator that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual syntax and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a reference resolution mechanism so that ModelCC is able to instantiate abstract syntax graphs, rather than mere abstract syntax trees.
High Speed Data Transmission over GI-MMF Using Mode Group Division Multiplexing
Venue: arXiv cs.NI 2015
Year: 2015

We transmitted 4x28 Gbit/s over 5-km standard GI-MMF using four different mode groups in combination with direct detection and on-off-keying format (OOK). Due to the square-law detection in the receiver MIMO processing is not effective. Therefore proper mode group selective multiplexing and de-multiplexing is essential.
Finding Volunteers' Engagement Profiles in Human Computation for Citizen Science Projects
Venue: arXiv cs.HC 2015
Year: 2015

Human computation is a computing approach that draws upon human cognitive abilities to solve computational tasks for which there are so far no satisfactory fully automated solutions even when using the most advanced computing technologies available. Human computation for citizen science projects consists in designing systems that allow large crowds of volunteers to contribute to scientific research by executing human computation tasks. Examples of successful projects are Galaxy Zoo and FoldIt. A key feature of this kind of project is its capacity to engage volunteers. An important requirement for the proposal and evaluation of new engagement strategies is having a clear understanding of the typical engagement of the volunteers; however, even though several projects of this kind have already been completed, little is known about this issue. In this paper, we investigate the engagement pattern of the volunteers in their interactions in human computation for citizen science projects, how they differ among themselves in terms of engagement, and how those volunteer engagement features should be taken into account for establishing the engagement encouragement strategies that should be brought into play in a given project. To this end, we define four quantitative engagement metrics to measure different aspects of volunteer engagement, and use data mining algorithms to identify the different volunteer profiles in terms of the engagement metrics. Our study is based on data collected from two projects: Galaxy Zoo and The Milky Way Project. The results show that the volunteers in such projects can be grouped into five distinct engagement profiles that we label as follows: hardworking, spasmodic, persistent, lasting, and moderate. The analysis of these profiles provides a deeper understanding of the nature of volunteers' engagement in human computation for citizen science projects
An improved energy argument for the Hegselmann-Krause model
Venue: arXiv cs.SY 2015
Year: 2015

We show that the freezing time of the $d$-dimensional Hegselmann-Krause model is $O(n^4)$ where $n$ is the number of agents. This improves the best known upper bound whenever $d\geq 2$.