OpenRaiser/Intern-Atlas huggingface dataset:
Impact of e-Government Services on Private Sector: An Empirical Assessment Model
Venue: arXiv cs.CY 2015
Year: 2015

Despite the large investments in the field of e-Government (e-Gov) around the world, little is known about the impact such investment. This is due to the lack of guidance evaluation, absence of appropriate tools to measure the impact of e-Gov on the private sector, as well as the lack of effective management to resolve or eliminate the barriers to e-Gov services that led to the failure or delay of many projects. This paper is primarily concerned in determining the impact of e-Gov services on the private sector. A combination of Modified Technology Acceptance Model (TAM), DeLone and McLean's of IS success will be utilized as a research model and e-Gov Economics Project (eGEP) framework to measure (Efficiency, Democracy & Effectiveness impact) for G2B services. The research result will help e-Gov decision makers to recognize the critical factors that are responsible for G2B success, specifically factors they need to pay attention to gain the highest return on their technology investment, hence enabling them to measure the impact for e-Gov on the private sector. The paper has also demonstrated the usefulness of Structural Equation Modeling (SEM) in analysis of small data sets and in exploratory research.
Counter-Example Guided Synthesis of Control Lyapunov Functions for Switched Systems
Venue: arXiv cs.SY 2015
Year: 2015

We investigate the problem of synthesizing switching controllers for stabilizing continuous-time plants. First, we introduce a class of control Lyapunov functions (CLFs) for switched systems along with a switching strategy that yields a closed loop system with a guaranteed minimum dwell time in each switching mode. However, the challenge lies in automatically synthesizing appropriate CLFs. Assuming a given fixed form for the CLF with unknown coefficients, we derive quantified nonlinear constraints whose feasible solutions (if any) correspond to CLFs for the original system. However, solving quantified nonlinear constraints pose a challenge to most LMI/BMI-based relaxations. Therefore, we investigate a general approach called Counter-Example Guided Inductive Synthesis (CEGIS), that has been widely used in the emerging area of automatic program synthesis. We show how a LMI-based relaxation can be formulated within the CEGIS framework for synthesizing CLFs. We also evaluate our approach on a number of interesting benchmarks, and compare the performance of the new approach with our previous work that uses off-the-shelf nonlinear constraint solvers instead of the LMI relaxation. The results shows synthesizing CLFs by using LMI solvers inside a CEGIS framework can be a computational feasible approach to synthesizing CLFs.
Randomization Improving Online Time-Sensitive Revenue Maximization for Green Data Centers
Venue: arXiv cs.DC 2015
Year: 2015

Green data centers have become more and more popular recently due to their sustainability. The resource management module within a green data center, which is in charge of dispatching jobs and scheduling energy, becomes especially critical as it directly affects a center's profit and sustainability. The thrust of managing a green data center's machine and energy resources lies at the uncertainty of incoming job requests and future showing-up green energy supplies. Thus, the decision of scheduling resources has to be made in an online manner. Some heuristic deterministic online algorithms have been proposed in recent literature. In this paper, we consider online algorithms for green data centers and introduce a randomized solution with the objective of maximizing net profit. Competitive analysis is employed to measure online algorithms' theoretical performance. Our algorithm is theoretical-sound and it outperforms the previously known deterministic algorithms in many settings using real traces. To complement our study, optimal offline algorithms are also designed.
Synergy, Redundancy and Common Information
Venue: arXiv cs.IT 2015
Year: 2015

We consider the problem of decomposing the total mutual information conveyed by a pair of predictor random variables about a target random variable into redundant, unique and synergistic contributions. We focus on the relationship between "redundant information" and the more familiar information-theoretic notions of "common information". Our main contribution is an impossibility result. We show that for independent predictor random variables, any common information based measure of redundancy cannot induce a nonnegative decomposition of the total mutual information. Interestingly, this entails that any reasonable measure of redundant information cannot be derived by optimization over a single random variable.
Time Synchronization of Turbo-Coded Square-QAM-Modulated Transmissions: Code-Aided ML Estimator and Closed-Form Cramér-Rao Lower Bounds
Venue: arXiv cs.IT 2015
Year: 2015

This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-QAM transmissions and derives, for the very first time, its CA Cramér-Rao lower bounds (CRLBs) in closed-form expressions. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function (LF) of the system explicitly in terms of the code bits' \textit{a priori} log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo iteration loop wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output (SISO) decoders and exploited at a per-turbo-iteration basis in order to refine the ML time delay estimate. The latter is then used to better re-synchronize the system, through feedback to the matched filter (MF), so as to obtain more reliable symbol-rate samples for the next turbo iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte-Carlo simulations. They will also show unambiguously the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization (SP-EM) algorithm.
On Binary Classification with Single-Layer Convolutional Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

Convolutional neural networks are becoming standard tools for solving object recognition and visual tasks. However, most of the design and implementation of these complex models are based on trail-and-error. In this report, the main focus is to consider some of the important factors in designing convolutional networks to perform better. Specifically, classification with wide single-layer networks with large kernels as a general framework is considered. Particularly, we will show that pre-training using unsupervised schemes is vital, reasonable regularization is beneficial and applying of strong regularizers like dropout could be devastating. Pool size is also could be as important as learning procedure itself. In addition, it has been presented that using such a simple and relatively fast model for classifying cats and dogs, performance is close to state-of-the-art achievable by a combination of SVM models on color and texture features.
Joint Sensing and Reception Design of SIMO Hybrid Cognitive Radio Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, the problem of joint design of Spectrum Sensing (SS) and receive beamforming (BF), with reference to a Cognitive Radio (CR) system, is considered. The aim of the proposed design is the maximization of the achievable average uplink rate of a Secondary User (SU), subject to an outage-based Quality-of-Service (QoS) constraint for primary communication. A hybrid CR system approach is studied, according to which, the system either operates as an interweave (i.e., opportunistic) or as an underlay (i.e., spectrum sharing) CR system, based on SS results. A realistic Channel State Information (CSI) framework is assumed, according to which, the direct channel links are known by the multiple antenna receivers (RXs), while, merely statistical (covariance) information is available for the interference links. A new, closed form approximation is derived for the outage probability of primary communication, and the problem of rate-optimal selection of SS parameters and receive beamformers is addressed for hybrid, interweave and underlay CR systems. It is proven that our proposed system design outperforms both underlay and interweave CR systems for a range of system scenarios.
Millimeter Wave Ad Hoc Networks: Noise-limited or Interference-limited?
Venue: arXiv cs.IT 2015
Year: 2015

In millimeter wave (mmWave) communication systems, narrow beam operations overcome severe channel attenuations, reduce multiuser interference, and thus introduce the new concept of noise-limited mmWave wireless networks. The regime of the network, whether noise-limited or interference-limited, heavily reflects on the medium access control (MAC) layer throughput and on proper resource allocation and interference management strategies. Yet, alternating presence of these regimes and, more importantly, their dependence on the mmWave design parameters are ignored in the current approaches to mmWave MAC layer design, with the potential disastrous consequences on the throughput/delay performance. In this paper, tractable closed-form expressions for collision probability and MAC layer throughput of mmWave networks, operating under slotted ALOHA and TDMA, are derived. The new analysis reveals that mmWave networks may exhibit a non-negligible transitional behavior from a noise-limited regime to an interference-limited regime, depending on the density of the transmitters, density and size of obstacles, transmission probability, beamwidth, and transmit power. It is concluded that a new framework of adaptive hybrid resource allocation procedure, containing a proactive contention-based phase followed by a reactive contention-free one with dynamic phase durations, is necessary to cope with such transitional behavior.
Collaborative Bibliographic System for Review/Survey Articles
Venue: arXiv cs.DL 2015
Year: 2015

This paper proposes a Bibliographic system intends to exchange bibliographic information of survey/review articles by relying on Web service technology. It allows researchers and university students to interact with system via single service using platform-independent standard named Web service to add, search and retrieve bibliographic information of review articles in various science and technology fields and build-up a dedicated database for these articles in each science and technology field. Additionally, different implementation scenarios of the proposed system are presented and described, and rich features that offered by such system are studied and described. However, this paper explains the proposed system using computing area due to the existence of detailed taxonomy of this area, which allows defining the system, their functionalities and features provided. However, the proposed system is not only confined to computing area, it can support any other science and technology area without any need to modify this system.
Cyclic codes over the ring $\mathbb{F}_p[u,v,w]/\langle u^2, v^2, w^2, uv-vu, vw-wv, uw-wu \rangle$
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we investigate cyclic codes over the ring $ \mathbb{F}_p[u,v,w]\langle u^2,$ $v^2, w^2$, $uv-vu, vw-wv, uw-wu \rangle$, where $p$ is a prime number. Which is a part of family of Frobenius rings. We find a unique set of generators for these codes and characterize the free cyclic codes. We also study the rank and the Hamming distance of these codes. We also constructs some good $p-ary$ codes as the Gray images of these cyclic codes.
Feasible methodology for optimization of a novel reversible binary compressor
Venue: arXiv cs.AR 2015
Year: 2015

Now a day reversible logic is an attractive research area due to its low power consumption in the area of VLSI circuit design. The reversible logic gate is utilized to optimize power consumption by a feature of retrieving input logic from an output logic because of bijective mapping between input and output. In this manuscript, we design 4 2 and 5 2 reversible compressor circuits using a new type of reversible gate. In addition, we propose new gate, named as inventive0 gate for optimizing a compressor circuit. The utility of the inventive0 gate is that it can be used as full adder and full subtraction with low value of garbage outputs and quantum cost. An algorithm is shown for designing a compressor structure. The comparative study shows that the proposed compressor structure outperforms the existing ones in terms of garbage outputs, number of gates and quantum cost. The compressor can reduce the effect of carry (Produce from full adder) of the arithmetic frame design. In addition, we implement a basic reversible gate of MOS transistor with less number of MOS transistor count.
Downlink Performance of Massive MIMO under General Channel Aging Conditions
Venue: arXiv cs.IT 2015
Year: 2015

Massive multiple-input multiple-output (MIMO) is a promising technology aiming at achieving high spectral efficiency by deploying a large number of base station (BS) antennas using coherent combining. Channel aging due to user mobility is a significant degrading factor of such systems. In addition, cost efficiency of massive MIMO is a prerecuisite for their deployment, that leads to low cost antenna elements inducing high phase noise. Since phase is time-dependent, it contributes to channel aging. For this reason, we present a novel joint channel-phase noise model, that enables us to study the downlink of massive MIMO with maximum ratio transmission (MRT) precoder under these conditions by means of the deterministic equivalent of the achievable sum-rate. Among the noteworthy outcomes is that the degradation due to user mobility dominates over the effect of phase noise. Nevertheless, we demonstrate that the joint effects of phase noise and user mobility do not degrade the power scaling law $1/\sqrt{M}$ ($M$ is the number of BS antennas), as has been established in massive MIMO systems with imperfect channel state information.
Capacity Theorems for Broadcast Channels with Two Channel State Components Known at the Receivers
Venue: arXiv cs.IT 2015
Year: 2015

We establish the capacity region of several classes of broadcast channels with random state in which the channel to each user is selected from two possible channel state components and the state is known only at the receivers. When the channel components are deterministic, we show that the capacity region is achieved via Marton coding. This channel model does not belong to any class of broadcast channels for which the capacity region was previously known and is useful in studying wireless communication channels when the fading state is known only at the receivers. We then establish the capacity region when the channel components are ordered, e.g., degraded. In particular we show that the capacity region for the broadcast channel with degraded Gaussian vector channel components is attained via Gaussian input distribution. Finally, we extend the results on ordered channels to two broadcast channel examples with more than two channel components, but show that these extensions do not hold in general.
Voted Kernel Regularization
Venue: arXiv cs.LG 2015
Year: 2015

This paper presents an algorithm, Voted Kernel Regularization , that provides the flexibility of using potentially very complex kernel functions such as predictors based on much higher-degree polynomial kernels, while benefitting from strong learning guarantees. The success of our algorithm arises from derived bounds that suggest a new regularization penalty in terms of the Rademacher complexities of the corresponding families of kernel maps. In a series of experiments we demonstrate the improved performance of our algorithm as compared to baselines. Furthermore, the algorithm enjoys several favorable properties. The optimization problem is convex, it allows for learning with non-PDS kernels, and the solutions are highly sparse, resulting in improved classification speed and memory requirements.
Power Allocation for Mixed Traffic Broadcast with Service Outage Constraint
Venue: arXiv cs.IT 2015
Year: 2015

To transmit a mixture of real-time and non-real-time traffic in a broadcast system, we impose a basic service rate $r_0$ for real-time traffic and use the excess rate beyond $r_0$ to transmit non-real-time traffic. Considering the time-varying nature of wireless channels, the basic service rate is guaranteed with a service outage constraint, where service outage occurs when the channel capacity is below the basic service rate. This approach is well suited for providing growing services like video, real-time TV, etc., in group transportation systems such as coach, high-speed train, and airplane. We show that the optimal power allocation policy depends only on the statistics of the minimum gain of all user channels, and it is a combination of water-filling and channel inversion. We provide the optimal power allocation policy, which guarantees that real-time traffic be delivered with quality of service (QoS) for every user. Moreover, we show that the required minimum average power to satisfy the service outage constraint increases linearly with the number of users.
Multivariate Topology Simplification
Venue: arXiv cs.CG 2015
Year: 2015

Topological simplification of scalar and vector fields is well-established as an effective method for analysing and visualising complex data sets. For multi-field data, topological analysis requires simultaneous advances both mathematically and computationally. We propose a robust multivariate topology simplification method based on ``lip''-pruning from the Reeb Space. Mathematically, we show that the projection of the Jacobi Set of multivariate data into the Reeb Space produces a Jacobi Structure that separates the Reeb Space into simple components. We also show that the dual graph of these components gives rise to a Reeb Skeleton that has properties similar to the scalar contour tree and Reeb Graph, for topologically simple domains. We then introduce a range measure to give a scaling-invariant total ordering of the components or features that can be used for simplification. Computationally, we show how to compute Jacobi Structure, Reeb Skeleton, Range and Geometric Measures in the Joint Contour Net (an approximation of the Reeb Space) and that these can be used for visualisation similar to the contour tree or Reeb Graph.
A Comparison of Mechanisms for Integrating Handwritten and Generated Code for Object-Oriented Programming Languages
Venue: arXiv cs.SE 2015
Year: 2015

Code generation from models is a core activity in model-driven development (MDD). For complex systems it is usually impossible to generate the entire software system from models alone. Thus, MDD requires mechanisms for integrating generated and handwritten code. Applying such mechanisms without considering their effects can cause issues in projects with many model and code artifacts, where a sound integration for generated and handwritten code is necessary. We provide an overview of mechanisms for integrating generated and handwritten code for object-oriented languages. In addition to that, we define and apply criteria to compare these mechanisms. The results are intended to help MDD tool developers in choosing an appropriate integration mechanism.
Understanding interdependency through complex information sharing
Venue: arXiv cs.IT 2015
Year: 2015

The interactions between three or more random variables are often nontrivial, poorly understood, and yet, are paramount for future advances in fields such as network information theory, neuroscience, genetics and many others. In this work, we propose to analyze these interactions as different modes of information sharing. Towards this end, we introduce a novel axiomatic framework for decomposing the joint entropy, which characterizes the various ways in which random variables can share information. The key contribution of our framework is to distinguish between interdependencies where the information is shared redundantly, and synergistic interdependencies where the sharing structure exists in the whole but not between the parts. We show that our axioms determine unique formulas for all the terms of the proposed decomposition for a number of cases of interest. Moreover, we show how these results can be applied to several network information theory problems, providing a more intuitive understanding of their fundamental limits.
Are there any nicely structured preference~profiles~nearby?
Venue: arXiv cs.GT 2015
Year: 2015

We investigate the problem of deciding whether a given preference profile is close to having a certain nice structure, as for instance single-peaked, single-caved, single-crossing, value-restricted, best-restricted, worst-restricted, medium-restricted, or group-separable profiles. We measure this distance by the number of voters or alternatives that have to be deleted to make the profile a nicely structured one. Our results classify the problem variants with respect to their computational complexity, and draw a clear line between computationally tractable (polynomial-time solvable) and computationally intractable (NP-hard) questions.
Dynamic Poisson Factorization
Venue: arXiv cs.LG 2015
Year: 2015

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.