OpenRaiser/Intern-Atlas huggingface dataset:
A novel approach against E-mail attacks derived from user-awareness based techniques
Venue: arXiv cs.CR 2012
Year: 2012

A large part of modern day communications are carried out through the medium of E-mails, especially corporate communications. More and more people are using E-mail for personal uses too. Companies also send notifications to their customers in E-mail. In fact, in the Multinational business scenario E-mail is the most convenient and sought-after method of communication. Important features of E-mail such as its speed, reliability, efficient storage options and a large number of added facilities make it highly popular among people from all sectors of business and society. But being largely popular has its negative aspects too. E-mails are the preferred medium for a large number of attacks over the internet. Some of the most popular attacks over the internet include spams, and phishing mails. Both spammers and phishers utilize E-mail services quite efficiently in spite of a large number of detection and prevention techniques already in place. Very few methods are actually good in detection/prevention of spam/phishing related mails but they have higher false positives. These techniques are implemented at the server and in addition to giving higher number of false positives, they add to the processing load on the server. This paper outlines a novel approach to detect not only spam, but also scams, phishing and advertisement related mails. In this method, we overcome the limitations of server-side detection techniques by utilizing some intelligence on the part of users. Keywords parsing, token separation and knowledge bases are used in the background to detect almost all E-mail attacks. The proposed methodology, if implemented, can help protect E-mail users from almost all kinds of unwanted mails with enhanced efficiency, reduced number of false positives while not increasing the load on E-mail servers.
C-PASS-PC: A Cloud-driven Prototype of Multi-Center Proactive Surveillance System for Prostate Cancer
Venue: arXiv cs.CE 2012
Year: 2012

Currently there are many clinical trials using paper case report forms as the primary data collection tool. Cloud Computing platforms provide big potential for increasing efficiency through a web-based data collection interface, especially for large-scale multi-center trials. Traditionally, clinical and biological data for multi-center trials are stored in one dedicated, centralized database system running at a data coordinating center (DCC). This paper presents C-PASS-PC, a cloud-driven prototype of multi-center proactive surveillance system for prostate cancer. The prototype is developed in PHP, JQuery and CSS with an Oracle backend in a local Web server and database server and deployed on Google App Engine (GAE) and Google Cloud SQL-MySQL. The deploying process is fast and easy to follow. The C-PASS-PC prototype can be accessed through an SSL-enabled web browser. Our approach proves the concept that cloud computing platforms such as GAE is a suitable and flexible solution in the near future for multi-center clinical trials.
Hirarchical Digital Image Inpainting Using Wavelets
Venue: arXiv cs.CV 2012
Year: 2012

Inpainting is the technique of reconstructing unknown or damaged portions of an image in a visually plausible way. Inpainting algorithm automatically fills the damaged region in an image using the information available in undamaged region. Propagation of structure and texture information becomes a challenge as the size of damaged area increases. In this paper, a hierarchical inpainting algorithm using wavelets is proposed. The hierarchical method tries to keep the mask size smaller while wavelets help in handling the high pass structure information and low pass texture information separately. The performance of the proposed algorithm is tested using different factors. The results of our algorithm are compared with existing methods such as interpolation, diffusion and exemplar techniques.
Layered Subspace Codes for Network Coding
Venue: arXiv cs.IT 2012
Year: 2012

Subspace codes were introduced by Kötter and Kschischang for error control in random linear network coding. In this paper, a layered type of subspace codes is considered, which can be viewed as a superposition of multiple component subspace codes. Exploiting the layered structure, we develop two decoding algorithms for these codes. The first algorithm operates by separately decoding each component code. The second algorithm is similar to the successive interference cancellation (SIC) algorithm for conventional superposition coding, and further permits an iterative version. We show that both algorithms decode not only deterministically up to but also probabilistically beyond the error-correction capability of the overall code. Finally we present possible applications of layered subspace codes in several network coding scenarios.
A Novel Approach of Harris Corner Detection of Noisy Images using Adaptive Wavelet Thresholding Technique
Venue: arXiv cs.CV 2012
Year: 2012

In this paper we propose a method of corner detection for obtaining features which is required to track and recognize objects within a noisy image. Corner detection of noisy images is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Though Corner detection of these noisy images does not provide desired results, hence de-noising is required. Adaptive wavelet thresholding approach is applied for the same.
SINR Statistics of Correlated MIMO Linear Receivers
Venue: arXiv cs.IT 2012
Year: 2012

Linear receivers offer a low complexity option for multi-antenna communication systems. Therefore, understanding the outage behavior of the corresponding SINR is important in a fading mobile environment. In this paper we introduce a large deviations method, valid nominally for a large number M of antennas, which provides the probability density of the SINR of Gaussian channel MIMO Minimum Mean Square Error (MMSE) and zero-forcing (ZF) receivers, with arbitrary transmission power profiles and in the presence of receiver antenna correlations. This approach extends the Gaussian approximation of the SINR, valid for large M asymptotically close to the center of the distribution, obtaining the non-Gaussian tails of the distribution. Our methodology allows us to calculate the SINR distribution to next-to-leading order (O(1/M)) and showcase the deviations from approximations that have appeared in the literature (e.g. the Gaussian or the generalized Gamma distribution). We also analytically evaluate the outage probability, as well as the uncoded bit-error-rate. We find that our approximation is quite accurate even for the smallest antenna arrays (2x2).
A Cache Management Strategy to Replace Wear Leveling Techniques for Embedded Flash Memory
Venue: arXiv cs.AR 2012
Year: 2012

Prices of NAND flash memories are falling drastically due to market growth and fabrication process mastering while research efforts from a technological point of view in terms of endurance and density are very active. NAND flash memories are becoming the most important storage media in mobile computing and tend to be less confined to this area. The major constraint of such a technology is the limited number of possible erase operations per block which tend to quickly provoke memory wear out. To cope with this issue, state-of-the-art solutions implement wear leveling policies to level the wear out of the memory and so increase its lifetime. These policies are integrated into the Flash Translation Layer (FTL) and greatly contribute in decreasing the write performance. In this paper, we propose to reduce the flash memory wear out problem and improve its performance by absorbing the erase operations throughout a dual cache system replacing FTL wear leveling and garbage collection services. We justify this idea by proposing a first performance evaluation of an exclusively cache based system for embedded flash memories. Unlike wear leveling schemes, the proposed cache solution reduces the total number of erase operations reported on the media by absorbing them in the cache for workloads expressing a minimal global sequential rate.
Spectrum Leasing and Cooperative Resource Allocation in Cognitive OFDMA Networks
Venue: arXiv cs.IT 2012
Year: 2012

This paper considers a cooperative OFDMA-based cognitive radio network where the primary system leases some of its subchannels to the secondary system for a fraction of time in exchange for the secondary users (SUs) assisting the transmission of primary users (PUs) as relays. Our aim is to determine the cooperation strategies among the primary and secondary systems so as to maximize the sum-rate of SUs while maintaining quality-of-service (QoS) requirements of PUs. We formulate a joint optimization problem of PU transmission mode selection, SU (or relay) selection, subcarrier assignment, power control, and time allocation. By applying dual method, this mixed integer programming problem is decomposed into parallel per-subcarrier subproblems, with each determining the cooperation strategy between one PU and one SU. We show that, on each leased subcarrier, the optimal strategy is to let a SU exclusively act as a relay or transmit for itself. This result is fundamentally different from the conventional spectrum leasing in single-channel systems where a SU must transmit a fraction of time for itself if it helps the PU's transmission. We then propose a subgradient-based algorithm to find the asymptotically optimal solution to the primal problem in polynomial time. Simulation results demonstrate that the proposed algorithm can significantly enhance the network performance.
Detection and Classification of Viewer Age Range Smart Signs at TV Broadcast
Venue: arXiv cs.CV 2012
Year: 2012

In this paper, the identification and classification of Viewer Age Range Smart Signs, designed by the Radio and Television Supreme Council of Turkey, to give age range information for the TV viewers, are realized. Therefore, the automatic detection at the broadcast will be possible, enabling the manufacturing of TV receivers which are sensible to these signs. The most important step at this process is the pattern recognition. Since the symbols that must be identified are circular, various circle detection techniques can be employed. In our study, first, two different circle segmentation methods for still images are analyzed, their advantages and drawbacks are discussed. A popular neural network structure called Multilayer Perceptron is employed for the classification. Afterwards, the same procedures are carried out for streaming video. All of the steps depicted above are realized on a standard PC.
Development of an e-learning system incorporating semantic web
Venue: arXiv cs.CY 2012
Year: 2012

E-Learning is efficient, task relevant and just-in-time learning grown from the learning requirements of the new and dynamically changing world. The term Semantic Web covers the steps to create a new WWW architecture that augments the content with formal semantics enabling better possibilities of navigation through the cyberspace and its contents. In this paper, we present the Semantic Web-Based model for our e-learning system taking into account the learning environment at Saudi Arabian universities. The proposed system is mainly based on ontology-based descriptions of content, context and structure of the learning materials. It further provides flexible and personalized access to these learning materials. The framework has been validated by an interview based qualitative method.
Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text Summarization
Venue: arXiv cs.IR 2012
Year: 2012

In Automatic Text Summarization, preprocessing is an important phase to reduce the space of textual representation. Classically, stemming and lemmatization have been widely used for normalizing words. However, even using normalization on large texts, the curse of dimensionality can disturb the performance of summarizers. This paper describes a new method for normalization of words to further reduce the space of representation. We propose to reduce each word to its initial letters, as a form of Ultra-stemming. The results show that Ultra-stemming not only preserve the content of summaries produced by this representation, but often the performances of the systems can be dramatically improved. Summaries on trilingual corpora were evaluated automatically with Fresa. Results confirm an increase in the performance, regardless of summarizer system used.
A framework for the analytical performance assessment of matrix and tensor-based ESPRIT-type algorithms
Venue: arXiv cs.PF 2012
Year: 2012

In this paper we present a generic framework for the asymptotic performance analysis of subspace-based parameter estimation schemes. It is based on earlier results on an explicit first-order expansion of the estimation error in the signal subspace obtained via an SVD of the noisy observation matrix. We extend these results in a number of aspects. Firstly, we derive an explicit first-order expansion of the Higher- Order SVD (HOSVD)-based subspace estimate. Secondly, we show how to obtain explicit first-order expansions of the estimation error of ESPRIT-type algorithms and provide the expressions for matrix-based and tensor-based Standard ESPRIT and Unitary ESPRIT. Thirdly, we derive closed-form expressions for the mean square error (MSE) and show that they only depend on the second-order moments of the noise. Hence, we only need the noise to be zero mean and possess finite second order moments. Fourthly, we investigate the effect of using Structured Least Squares (SLS) to solve the overdetermined shift invariance equations in ESPRIT and provide an explicit first-order expansion as well as a closed-form MSE expression. Finally, we simplify the MSE for the special case of a single source and compute the asymptotic efficiency of the investigated ESPRIT-type algorithms in compact closed-form expressions which only depend on the array size and the effective SNR. Our results are more general than existing results on the performance analysis of ESPRIT-type algorithms since (a) we do not need any assumptions about the noise except for the mean to be zero and the second-order moments to be finite (in contrast to earlier results that require Gaussianity or second-order circular symmetry); (b) our results are asymptotic in the effective SNR, i.e., we do not require the number of samples to be large; (c) we present a framework that incorporates various ESPRIT-type algorithms in one unified manner.
Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines
Venue: arXiv cs.DC 2012
Year: 2012

In this paper, we address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively.
Autonomic Cloud Computing: Open Challenges and Architectural Elements
Venue: arXiv cs.DC 2012
Year: 2012

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.
Information Metrics (iMetrics): A Research Specialty with a Socio-Cognitive Identity?
Venue: arXiv cs.DL 2012
Year: 2012

"Bibliometrics", "scientometrics", "informetrics", and "webometrics" can all be considered as manifestations of a single research area with similar objectives and methods, which we call "information metrics" or iMetrics. This study explores the cognitive and social distinctness of iMetrics with respect to the general information science (IS), focusing on a core of researchers, shared vocabulary and literature/knowledge base. Our analysis investigates the similarities and differences between four document sets. The document sets are drawn from three core journals for iMetrics research (Scientometrics, Journal of the American Society for Information Science and Technology, and Journal of Informetrics). We split JASIST into document sets containing iMetrics and general IS articles. The volume of publications in this representation of the specialty has increased rapidly during the last decade. A core of researchers that predominantly focus on iMetrics topics can thus be identified. This core group has developed a shared vocabulary as exhibited in high similarity of title words and one that shares a knowledge base. The research front of this field moves faster than the research front of information science in general, bringing it closer to Price's dream.
Tractable Optimization Problems through Hypergraph-Based Structural Restrictions
Venue: arXiv cs.AI 2012
Year: 2012

Several variants of the Constraint Satisfaction Problem have been proposed and investigated in the literature for modelling those scenarios where solutions are associated with some given costs. Within these frameworks computing an optimal solution is an NP-hard problem in general; yet, when restricted over classes of instances whose constraint interactions can be modelled via (nearly-)acyclic graphs, this problem is known to be solvable in polynomial time. In this paper, larger classes of tractable instances are singled out, by discussing solution approaches based on exploiting hypergraph acyclicity and, more generally, structural decomposition methods, such as (hyper)tree decompositions.
On the Interpretation of Spreadsheets within their Environment
Venue: arXiv cs.SE 2012
Year: 2012

A demonstration in MS Excel to show how users can connect their spreadsheet models to the external environment that the model represents. We employ indexes to generate a list of relevant evidence that is hyperlinked to the context in which the evidence is discussed. The hyperlinks between the index and the contextual discussion have their own specific presentational identity. We contend that these presentational differences aid the integrity and understanding of complex models. Where models are complex, separate individual results can lead to contradictory conclusions. The demonstration includes a methodology for interpreting the analyses within a workbook and presenting them in the form of a standard written report.
Some refined results on convergence of curvelet transform
Venue: arXiv cs.IT 2012
Year: 2012

Article presents proof that M-term non-linear approximation of functions that are C^3 apart from C^3 edges in curvelet frame have squared L^2 approximation bounded by M^(-2).
Moving Window Network Coding in Cooperative Multicast (v1)
Venue: arXiv cs.NI 2012
Year: 2012

Cooperative multicast is an effective solution to address the bottleneck problem of single-hop broadcast in wireless networks. By incorporating with the random linear network coding technique, the existing schemes can reduce the retransmission overhead significantly. However, the receivers may incur large decoding delay and complexity due to the batch decoding scheme. In addition, the dependency on the explicit feedback leads to scalability problem in larger networks. In this paper, a cooperative multicast protocol named MWNCast is proposed based on a novel moving window network coding technique. We prove three properties of the proposed scheme. Firstly, without explicit feedback, MWNCast can approach the cooperative capacity with the packet loss probability dropping almost exponentially with the increase of window size. Secondly, the average decoding delay of a receiver is on the order of $O(\frac{1}{(1-ρ)^2})$ with respect to its traffic intensity $ρ$. Thirdly, MWNCast can achieve the linear decoding complexity of $O(W)$ with respect to the window size $W$. Simulation results show that MWNCast outperforms the existing schemes by achieving better tradeoff between the throughput and decoding delay, meanwhile keeping the packet loss probability and decoding complexity at a very low levelwithout explicit feedback.
Profitable Scheduling on Multiple Speed-Scalable Processors
Venue: arXiv cs.DS 2012
Year: 2012

We present a new online algorithm for profit-oriented scheduling on multiple speed-scalable processors. Moreover, we provide a tight analysis of the algorithm's competitiveness. Our results generalize and improve upon work by \textcite{Chan:2010}, which considers a single speed-scalable processor. Using significantly different techniques, we can not only extend their model to multiprocessors but also prove an enhanced and tight competitive ratio for our algorithm. In our scheduling problem, jobs arrive over time and are preemptable. They have different workloads, values, and deadlines. The scheduler may decide not to finish a job but instead to suffer a loss equaling the job's value. However, to process a job's workload until its deadline the scheduler must invest a certain amount of energy. The cost of a schedule is the sum of lost values and invested energy. In order to finish a job the scheduler has to determine which processors to use and set their speeds accordingly. A processor's energy consumption is power $\Power{s}$ integrated over time, where $\Power{s}=s^α$ is the power consumption when running at speed $s$. Since we consider the online variant of the problem, the scheduler has no knowledge about future jobs. This problem was introduced by \textcite{Chan:2010} for the case of a single processor. They presented an online algorithm which is $α^α+2eα$-competitive. We provide an online algorithm for the case of multiple processors with an improved competitive ratio of $α^α$.
Theorem Proving in Large Formal Mathematics as an Emerging AI Field
Venue: arXiv cs.AI 2012
Year: 2012

In the recent years, we have linked a large corpus of formal mathematics with automated theorem proving (ATP) tools, and started to develop combined AI/ATP systems working in this setting. In this paper we first relate this project to the earlier large-scale automated developments done by Quaife with McCune's Otter system, and to the discussions of the QED project about formalizing a significant part of mathematics. Then we summarize our adventure so far, argue that the QED dreams were right in anticipating the creation of a very interesting semantic AI field, and discuss its further research directions.