OpenRaiser/Intern-Atlas huggingface dataset:
Transmit antenna subset selection in MIMO OFDM system using adaptive mutation Genetic algorithm
Venue: arXiv cs.IT 2014
Year: 2014

Multiple input multiple output techniques are considered attractive for future wireless communication systems, due to the continuing demand for high data rates, spectral efficiency, suppress interference ability and robustness of transmission. MIMO-OFDM is very helpful to transmit high data rate in wireless transmission and provides good maximum system capacity by getting the advantages of both MIMO and OFDM. The main problem in this system is that increase in number of transmit and receive antennas lead to hardware complexity. To tackle this issue, an effective optimal transmit antenna subset selection method is proposed in paper with the aid of Adaptive Mutation Genetic Algorithm (AGA). Here, the selection of transmit antenna subsets are done by the adaptive mutation of Genetic Algorithm in MIMO-OFDM system. For all the mutation points, the fitness function are evaluated and from that value, best fitness based mutation points are chosen. After the selection of best mutation points, the mutation process is carried out, accordingly. The implementation of proposed work is done in the working platform MATLAB and the performance are evaluated with various selection of transmit antenna subsets. Moreover, the comparison results between the existing GA with mutation and the proposed GA with adaptive mutation are discussed. Hence, using the proposed work, the selection of transmit antenna with the maximum capacity is made and which leads to the reduced hardware complexity and undisturbed data rate in the MIMO-OFDM system
Applying Agile Requirements Engineering Approach for Re-engineering & Changes in existing Brownfield Adaptive Systems
Venue: arXiv cs.SE 2014
Year: 2014

Requirements Engineering (RE) is a key activity in the development of software systems and is concerned with the identification of the goals of stakeholders and their elaboration into precise statements of desired services and behavior. The research describes an Agile Requirements Engineering approach for re-engineering & changes in existing Brownfield adaptive system. The approach has few modifications that can be used as a part of SCRUM development process for re-engineering & changes. The approach illustrates the re-engineering & changes requirements through introduction of GAP analysis & requirements structuring & prioritization by creating AS-IS & TO-BE models with 80 / 20 rule. An attempt to close the gap between requirements engineering & agile methods in form of this approach is provided for practical implementation.
Low rank matrix recovery from rank one measurements
Venue: arXiv cs.IT 2014
Year: 2014

We study the recovery of Hermitian low rank matrices $X \in \mathbb{C}^{n \times n}$ from undersampled measurements via nuclear norm minimization. We consider the particular scenario where the measurements are Frobenius inner products with random rank-one matrices of the form $a_j a_j^*$ for some measurement vectors $a_1,...,a_m$, i.e., the measurements are given by $y_j = \mathrm{tr}(X a_j a_j^*)$. The case where the matrix $X=x x^*$ to be recovered is of rank one reduces to the problem of phaseless estimation (from measurements, $y_j = |\langle x,a_j\rangle|^2$ via the PhaseLift approach, which has been introduced recently. We derive bounds for the number $m$ of measurements that guarantee successful uniform recovery of Hermitian rank $r$ matrices, either for the vectors $a_j$, $j=1,...,m$, being chosen independently at random according to a standard Gaussian distribution, or $a_j$ being sampled independently from an (approximate) complex projective $t$-design with $t=4$. In the Gaussian case, we require $m \geq C r n$ measurements, while in the case of $4$-designs we need $m \geq Cr n \log(n)$. Our results are uniform in the sense that one random choice of the measurement vectors $a_j$ guarantees recovery of all rank $r$-matrices simultaneously with high probability. Moreover, we prove robustness of recovery under perturbation of the measurements by noise. The result for approximate $4$-designs generalizes and improves a recent bound on phase retrieval due to Gross, Kueng and Krahmer. In addition, it has applications in quantum state tomography. Our proofs employ the so-called bowling scheme which is based on recent ideas by Mendelson and Koltchinskii.
Project-based Learning within a Large-Scale Interdisciplinary Research Effort
Venue: arXiv cs.CY 2014
Year: 2014

The modern engineering landscape increasingly requires a range of skills to successfully integrate complex systems. Project-based learning is used to help students build professional skills. However, it is typically applied to small teams and small efforts. This paper describes an experience in engaging a large number of students in research projects within a multi-year interdisciplinary research effort. The projects expose the students to various disciplines in Computer Science (embedded systems, algorithm design, networking), Electrical Engineering (circuit design, wireless communications, hardware prototyping), and Applied Physics (thin-film battery design, solar cell fabrication). While a student project is usually focused on one discipline area, it requires interaction with at least two other areas. Over 5 years, 180 semester-long projects have been completed. The students were a diverse group of high school, undergraduate, and M.S. Computer Science, Computer Engineering, and Electrical Engineering students. Some of the approaches that were taken to facilitate student learning are real-world system development constraints, regular cross-group meetings, and extensive involvement of Ph.D. students in student mentorship and knowledge transfer. To assess the approaches, a survey was conducted among the participating students. The results demonstrate the effectiveness of the approaches. For example, 70% of the students surveyed indicated that working on their research project improved their ability to function on multidisciplinary teams more than coursework, internships, or any other activity.
Complexity of LP in Terms of the Face Lattice
Venue: arXiv cs.CC 2014
Year: 2014

Let $X$ be a finite set in $Z^d$. We consider the problem of optimizing linear function $f(x) = c^T x$ on $X$, where $c\in Z^d$ is an input vector. We call it a problem $X$. A problem $X$ is related with linear program $\max\limits_{x \in P} f(x)$, where polytope $P$ is a convex hull of $X$. The key parameters for evaluating the complexity of a problem $X$ are the dimension $d$, the cardinality $|X|$, and the encoding size $S(X) = \log_2 \left(\max\limits_{x\in X} \|x\|_{\infty}\right)$. We show that if the (time and space) complexity of some algorithm $A$ for solving a problem $X$ is defined only in terms of combinatorial structure of $P$ and the size $S(X)$, then for every $d$ and $n$ there exists polynomially (in $d$, $\log n$, and $S$) solvable problem $Y$ with $\dim Y = d$, $|Y| = n$, such that the algorithm $A$ requires exponential time or space for solving $Y$.
Analysis of Named Entity Recognition and Linking for Tweets
Venue: arXiv cs.CL 2014
Year: 2014

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.
Deep Structured learning for mass segmentation from Mammograms
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning. Specifically, using structured support vector machine (SSVM), we formulate a model that combines different types of potential functions, including one that classifies image regions using deep learning. Our main goal with this work is to show the accuracy and efficiency improvements that these relatively new techniques can provide for the segmentation of breast masses from mammograms. We also propose an easily reproducible quantitative analysis to as- sess the performance of breast mass segmentation methodologies based on widely accepted accuracy and running time measurements on public datasets, which will facilitate further comparisons for this segmentation problem. In particular, we use two publicly available datasets (DDSM-BCRP and INbreast) and propose the computa- tion of the running time taken for the methodology to produce a mass segmentation given an input image and the use of the Dice index to quantitatively measure the segmentation accuracy. For both databases, we show that our proposed methodology produces competitive results in terms of accuracy and running time.
Spectral Efficiency Scaling Laws in Dense Random Wireless Networks with Multiple Receive Antennas
Venue: arXiv cs.IT 2014
Year: 2014

This paper considers large random wireless networks where transmit-and-receive node pairs communicate within a certain range while sharing a common spectrum. By modeling the spatial locations of nodes based on stochastic geometry, analytical expressions for the ergodic spectral efficiency of a typical node pair are derived as a function of the channel state information available at a receiver (CSIR) in terms of relevant system parameters: the density of communication links, the number of receive antennas, the path loss exponent, and the operating signal-to-noise ratio. One key finding is that when the receiver only exploits CSIR for the direct link, the sum of spectral efficiencies linearly improves as the density increases, when the number of receive antennas increases as a certain super-linear function of the density. When each receiver exploits CSIR for a set of dominant interfering links in addition to the direct link, the sum of spectral efficiencies linearly increases with both the density and the path loss exponent if the number of antennas is a linear function of the density. This observation demonstrates that having CSIR for dominant interfering links provides a multiplicative gain in the scaling law. It is also shown that this linear scaling holds for direct CSIR when incorporating the effect of the receive antenna correlation, provided that the rank of the spatial correlation matrix scales super-linearly with the density. Simulation results back scaling laws derived from stochastic geometry.
Optimal WiFi Sensing via Dynamic Programming
Venue: arXiv cs.IT 2014
Year: 2014

The problem of finding an optimal sensing schedule for a mobile device that encounters an intermittent WiFi access opportunity is considered. At any given time, the WiFi is in any of the two modes, ON or OFF, and the mobile's incentive is to connect to the WiFi in the ON mode as soon as possible, while spending as little sensing energy. We introduce a dynamic programming framework which enables the characterization of an explicit solution for several models, particularly when the OFF periods are exponentially distributed. While the problem for non-exponential OFF periods is ill-posed in general, a usual workaround in literature is to make the mobile device aware if one ON period is completely missed. In this restricted setting, using the DP framework, the deterministic nature of the optimal sensing policy is established, and value iterations are shown to converge to the optimal solution. Finally, we address the blind situation where the distributions of ON and OFF periods are unknown. A continuous bandit based learning algorithm that has vanishing regret (loss compared to the optimal strategy with the knowledge of distributions) is presented, and comparisons with the optimal schemes are provided for exponential ON and OFF times.
Improved bound on the worst case complexity of Policy Iteration
Venue: arXiv cs.CC 2014
Year: 2014

Solving Markov Decision Processes (MDPs) is a recurrent task in engineering. Even though it is known that solutions for minimizing the infinite horizon expected reward can be found in polynomial time using Linear Programming techniques, iterative methods like the Policy Iteration algorithm (PI) remain usually the most efficient in practice. This method is guaranteed to converge in a finite number of steps. Unfortunately, it is known that it may require an exponential number of steps in the size of the problem to converge. On the other hand, many open questions remain considering the actual worst case complexity. In this work, we provide the first improvement over the fifteen years old upper bound from Mansour & Singh (1999) by showing that PI requires at most k/(k-1)*k^n/n + o(k^n/n) iterations to converge, where n is the number of states of the MDP and k is the maximum number of actions per state. Perhaps more importantly, we also show that this bound is optimal for an important relaxation of the problem.
XML Information Retrieval:An overview
Venue: arXiv cs.IR 2014
Year: 2014

Locating and distilling the valuable relevant information continued to be the major challenges of Information Retrieval (IR) Systems owing to the explosive growth of online web information. These challenges can be considered the XML Information Retrieval challenges as XML has become a de facto standard over the Web. The research on XML IR starts with the classical IR strategies customized to XML IR. Later novel IR strategies specific to XML IR are evolved. Meanwhile literatures reveal development of the rapid and intelligent IR systems. Despite their success in their specified constrained domains, they have additional limitations in the complex information space. The effectiveness of IR systems is thus unsolved in satisfying the most. This article attemptsan overview of earlier efforts and the gaps in XML IR.
Data Driven Authentication: On the Effectiveness of User Behaviour Modelling with Mobile Device Sensors
Venue: arXiv cs.CR 2014
Year: 2014

We propose a lightweight, and temporally and spatially aware user behaviour modelling technique for sensor-based authentication. Operating in the background, our data driven technique compares current behaviour with a user profile. If the behaviour deviates sufficiently from the established norm, actions such as explicit authentication can be triggered. To support a quick and lightweight deployment, our solution automatically switches from training mode to deployment mode when the user's behaviour is sufficiently learned. Furthermore, it allows the device to automatically determine a suitable detection threshold. We use our model to investigate practical aspects of sensor-based authentication by applying it to three publicly available data sets, computing expected times for training duration and behaviour drift. We also test our model with scenarios involving an attacker with varying knowledge and capabilities.
A Systematic Security Evaluation of Android's Multi-User Framework
Venue: arXiv cs.CR 2014
Year: 2014

Like many desktop operating systems in the 1990s, Android is now in the process of including support for multi-user scenarios. Because these scenarios introduce new threats to the system, we should have an understanding of how well the system design addresses them. Since the security implications of multi-user support are truly pervasive, we developed a systematic approach to studying the system and identifying problems. Unlike other approaches that focus on specific attacks or threat models, ours systematically identifies critical places where access controls are not present or do not properly identify the subject and object of a decision. Finding these places gives us insight into hypothetical attacks that could result, and allows us to design specific experiments to test our hypothesis. Following an overview of the new features and their implementation, we describe our methodology, present a partial list of our most interesting hypotheses, and describe the experiments we used to test them. Our findings indicate that the current system only partially addresses the new threats, leaving the door open to a number of significant vulnerabilities and privacy issues. Our findings span a spectrum of root causes, from simple oversights, all the way to major system design problems. We conclude that there is still a long way to go before the system can be used in anything more than the most casual of sharing environments.
Effect of Degradation in Molecular Communication: Impairment or Enhancement?
Venue: arXiv cs.ET 2014
Year: 2014

In the nanonetworking literature, many solutions have been suggested to enable the nanomachine-to-nanomachine communication. Among these solutions, we focus on what constitutes the basis for molecular communication paradigms --molecular communication via diffusion (MCvD). In this paper, we start with an analytical modeling of a spherical absorbing receiver under messenger molecule degradation and show that our formulations are in agreement with the simulation results of a similar topology. Next, we identify how such signal characteristics as pulse peak time and pulse amplitude are affected by degradation. Indeed, we show analytically how in MCvD, signal shaping is achieved through degradation. We also compare communication under messenger molecule degradation with the case of no-degradation and electromagnetic communication in terms of channel characteristics. Lastly, we evaluate the communication performance of the scenarios having various degradation rates. Here, we assess the system performance according to traditional network metrics such as the level of inter-symbol interference, detection performance, bit error rate, and channel capacity. Our results indicate that introducing degradation significantly improves the system performance when the rate of degradation is appropriately selected. We make a thorough analysis of the communication scenario by taking into account different detection thresholds, symbol durations, and communication distances.
Reasoning for ALCQ extended with a flexible meta-modelling hierarchy
Venue: arXiv cs.AI 2014
Year: 2014

This works is motivated by a real-world case study where it is necessary to integrate and relate existing ontologies through meta- modelling. For this, we introduce the Description Logic ALCQM which is obtained from ALCQ by adding statements that equate individuals to concepts in a knowledge base. In this new extension, a concept can be an individual of another concept (called meta-concept) which themselves can be individuals of yet another concept (called meta meta-concept) and so on. We define a tableau algorithm for checking consistency of an ontology in ALCQM and prove its correctness.
OFDM Transmission Performance Evaluation in V2X Communication
Venue: arXiv cs.NI 2014
Year: 2014

The Vehicle to Vehicle and Vehicle to Infrastructure V2X communication systems are one of the main topics in research domain. Its performance evaluation is an important step before their on board integration into vehicles and its probable real deployment. This paper studies the physical layer PHY of the upcoming vehicular communication standard IEEE 802.11p. This standard PHY Layer model, with much associated phenomena, is implemented in V2V and V2I, situations through different scenarios. The series of simulation results carried out, perform data exchange between high speed vehicles over different channels models and different transmitted packet size. We underline several propagation channel and other important parameters, which affect both the physical layer network performance and the QoT. The Bit Error Rate BER versus Signal to Noise Ratio SNR of all coding rates is used to evaluate the performance of the communication.
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
Venue: arXiv cs.LG 2014
Year: 2014

As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Unfortunately, effective use of clusters for ML requires considerable expertise in writing distributed code, while highly-abstracted frameworks like Hadoop have not, in practice, approached the performance seen in specialized ML implementations. The recent Parameter Server (PS) paradigm is a middle ground between these extremes, allowing easy conversion of single-machine parallel ML applications into distributed ones, while maintaining high throughput through relaxed "consistency models" that allow inconsistent parameter reads. However, due to insufficient theoretical study, it is not clear which of these consistency models can really ensure correct ML algorithm output; at the same time, there remain many theoretically-motivated but undiscovered opportunities to maximize computational throughput. Motivated by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models. We then use the gleaned insights to improve a consistency model using an "eager" PS communication mechanism, and implement it as a new PS system that enables ML algorithms to reach their solution more quickly.
Malware "Ecology" Viewed as Ecological Succession: Historical Trends and Future Prospects
Venue: arXiv cs.CR 2014
Year: 2014

The development and evolution of malware including computer viruses, worms, and trojan horses, is shown to be closely analogous to the process of community succession long recognized in ecology. In particular, both changes in the overall environment by external disturbances, as well as, feedback effects from malware competition and antivirus coevolution have driven community succession and the development of different types of malware with varying modes of transmission and adaptability.
ISI Mitigation Techniques in Molecular Communication
Venue: arXiv cs.ET 2014
Year: 2014

Molecular communication is a new field of communication where molecules are used to transfer information. Among the proposed methods, molecular communication via diffusion (MCvD) is particularly effective. One of the main challenges in MCvD is the intersymbol interference (ISI), which inhibits communication at high data rates. Furthermore, at the nano scale, energy efficiency becomes an essential problem. Before addressing these problems, a pre-determined threshold for the received signal must be calculated to make a decision. In this paper, an analytical technique is proposed to determine the optimum threshold, whereas in the literature, these thresholds are generally calculated empirically. Since the main goal of this paper is to build an MCvD system suitable for operating at high data rates without sacrificing quality, new modulation and filtering techniques are proposed to decrease the effects of ISI and enhance energy efficiency. As a transmitter-based solution, a modulation technique for MCvD, molecular transition shift keying (MTSK), is proposed in order to increase the data rate via suppressing the ISI. Furthermore, for energy efficiency, a power adjustment technique that utilizes the residual molecules is proposed. Finally, as a receiver-based solution, a new energy efficient decision feedback filter (DFF) is proposed as a substitute for the decoders such as minimum mean squared error (MMSE) and decision feedback equalizer (DFE). The error performance of DFF and MMSE equalizers are compared in terms of bit error rates, and it is concluded that DFF may be more advantageous when energy efficiency is concerned, due to its lower computational complexity.