OpenRaiser/Intern-Atlas huggingface dataset:
Multi criteria decision making approach for selecting effort estimation model
Venue: arXiv cs.SE 2013
Year: 2013

Effort Estimation has always been a challenging task for the Project managers. Many researchers have tried to help them by creating different types of models. This has been already proved that none is successful for all types of projects and every type of environment. Analytic Hierarchy Process has been identified as the tool that would help in Multi Criteria Decision Making. Researchers have identified that Analytic Hierarchy Process can be used for the comparison of effort estimation of different models and techniques. But the problem with traditional Analytic Hierarchy Process is its inability to deal with the imprecision and subjectivity in the pairwise comparison process. The motive of this paper is to propose Fuzzy Analytic Hierarchy Process, which can be used to rectify the subjectivity and imprecision of Analytic Hierarchy Process and can be used for selecting the type of Model best suited for estimating the effort for a given problem type or environment. Instead of single crisp value, Fuzzy Analytic Hierarchy Process uses a range of values to incorporate decision maker uncertainty. From this range, decision maker can select the value that reflects his confidence and also he can specify his attitude like optimistic, pessimistic or moderate. In this work, the comparison of Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process is concluded using a case study of selection of effort estimation model.
Prefix and plain Kolmogorov complexity characterizations of 2-randomness: simple proofs
Venue: arXiv cs.IT 2013
Year: 2013

Joseph Miller [16] and independently Andre Nies, Frank Stephan and Sebastiaan Terwijn [18] gave a complexity characterization of 2-random sequences in terms of plain Kolmogorov complexity C: they are sequences that have infinitely many initial segments with O(1)-maximal plain complexity (among the strings of the same length). Later Miller [17] showed that prefix complexity K can also be used in a similar way: a sequence is 2-random if and only if it has infinitely many initial segments with O(1)-maximal prefix complexity (which is n + K (n) for strings of length n). The known proofs of these results are quite involved; in this paper we provide simple direct proofs for both of them. In [16] Miller also gave a quantitative version of the first result: the 0'-randomness deficiency of a sequence ω equals lim inf [n - C (ω1 . . . ωn)] + O(1). (Our simplified proof can also be used to prove this.) We show (and this seems to be a new result) that a similar quantitative result is also true for prefix complexity: 0'-randomness deficiency equals lim inf [n + K (n) -- K (ω1 . . . ωn)] + O(1).
Can social microblogging be used to forecast intraday exchange rates?
Venue: arXiv cs.SI 2013
Year: 2013

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.
Rate Control Adaptation for Heterogeneous Handovers
Venue: arXiv cs.NI 2013
Year: 2013

We present enhancements to the TCP-Friendly Rate Control mechanism (TFRC) designed to better handle the intermittent connectivity occurring in mobility situations. Our aim is to quickly adapt to new network conditions and better support real-time applications for which the user-perceived quality depends on the immediate transmission rate. We propose to suspend the transmission before disconnections occur, in a way inspired by Freeze-TCP, and extend the solution by probing the network after reconnecting to enable full use of the newly available capacity. We first introduce a numerical model of TFRC's performance after a network handover and use it to evaluate the potential performance gains for realistic network parameters. We then describe a set of additions to TFRC to achieve these gains. Implementations within the Datagram Congestion Control Protocol (DCCP) for ns -2 and Linux have been adapted to support these enhancements. Comparisons of experimental results for the original and modified DCCP are presented for a number of example mobility scenarios. We thus show how the proposed modifications enable faster recovery after disconnected periods as well as significantly improved adjustments to the newly available network conditions and an improvement in the quality of experience (QoE) for video-streaming applications.
Message-Passing Algorithms for Optimal Utilization of Cognitive Radio Networks
Venue: arXiv cs.IT 2013
Year: 2013

Cognitive Radio has been proposed as a key technology to significantly improve spectrum usage in wireless networks by enabling unlicensed users to access unused resource. We present new algorithms that are needed for the implementation of opportunistic scheduling policies that maximize the throughput utilization of resources by secondary users, under maximum interference constraints imposed by existing primary users. Our approach is based on the Belief Propagation (BP) algorithm, which is advantageous due to its simplicity and potential for distributed implementation. We examine convergence properties and evaluate the performance of the proposed BP algorithms via simulations and demonstrate that the results compare favorably with a benchmark greedy strategy.
Implementation of Automata Theory to Improve the Learning Disability
Venue: arXiv cs.CY 2013
Year: 2013

There are various types of disability egress in world like blindness, deafness, and Physical disabilities. It is quite difficult to deal with people with disability. Learning disability (LD) is types of disability totally different from general disability. To deal children with learning disability is difficult for both parents and teacher. As parent deal with only single child so it bit easy. But teacher deals with different students at a time so its more difficult to deal with group of students with learning disability. If there is more students with learning disability so it is necessary that first all identify the type of learning disability in group of students. Some students have learning disability of mathematics; some have learning disability of other subjects. By using theory of Automata it easy to analysis the level of disability among all students then deal with them accordingly. For these purpose deterministic automata is the best practice. Teacher deals with deterministic students in class and check there response. In this research deterministic automata is use to facilitated the teacher which help teacher in identification of students with learning disability.
Blind and robust images watermarking based on wavelet and edge insertion
Venue: arXiv cs.MM 2013
Year: 2013

This paper gives a new scheme of watermarking technique related to insert the mark by adding edge in HH sub-band of the host image after wavelet decomposition. Contrary to most of the watermarking algorithms in wavelet domain, our method is blind and results show that it is robust against the JPEG and GIF compression, histogram and spectrum spreading, noise adding and small rotation. Its robustness against compression is better than others watermarking algorithms reported in the literature. The algorithm is flexible because its capacity or robustness can be improved by modifying some parameters.
Intelligent City Traffic Management and Public Transportation System
Venue: arXiv cs.AI 2013
Year: 2013

Intelligent Transportation System in case of cities is controlling traffic congestion and regulating the traffic flow. This paper presents three modules that will help in managing city traffic issues and ultimately gives advanced development in transportation system. First module, Congestion Detection and Management will provide user real time information about congestion on the road towards his destination, Second module, Intelligent Public Transport System will provide user real time public transport information,i.e, local buses, and the third module, Signal Synchronization will help in controlling congestion at signals, with real time adjustments of signal timers according to the congestion. All the information that user is getting about the traffic or public transportation will be provided on users day to day device that is mobile through Android application or SMS. Moreover, communication can also be done via Website for Clients having internet access. And all these modules will be fully automated without any human intervention at server side.
Extreme Scaling of Lattice Quantum Chromodynamics
Venue: arXiv cs.DC 2013
Year: 2013

As the complexity and size of challenges in science and engineering are continually increasing, it is highly important that applications are able to scale strongly to very large numbers of cores (>100,000 cores) to enable HPC systems to be utilised efficiently. This paper presents results of strong scaling tests performed with an MPI only and a hybrid MPI + OpenMP version of the Lattice QCD application BQCD on the European Tier-0 system SuperMUC at LRZ.
What do university rankings by fields rank? Exploring discrepancies between the organizational structure of universities and bibliometric classifications
Venue: arXiv cs.DL 2013
Year: 2013

University rankings by fields are usually based on the research output of universities. However, research managers and rankings consumers expect to see in such fields a reflection of the structure of their own organizational institution. In this study we address such misinterpretation by developing the research profile of the organizational units of two Spanish universities: University of Granada and Pompeu Fabra University. We use two classification systems, the subject categories offered by Thomson Scientific which are commonly used on bibliometric studies, and the 37 disciplines displayed by the Spanish I-UGR Rankings which are constructed from an aggregation of the former. We also describe in detail problems encountered when working with address data from a top down approach and we show differences between universities structures derived from the interdisciplinary organizational forms of new managerialism at universities. We conclude by highlighting that rankings by fields should clearly state the methodology for the construction of such fields. We indicate that the construction of research profiles may be a good solution for universities for finding out levels of discrepancy between organizational units and subject fields.
Computing in matrix groups without memory
Venue: arXiv cs.CC 2013
Year: 2013

Memoryless computation is a novel means of computing any function of a set of registers by updating one register at a time while using no memory. We aim to emulate how computations are performed on modern cores, since they typically involve updates of single registers. The computation model of memoryless computation can be fully expressed in terms of transformation semigroups, or in the case of bijective functions, permutation groups. In this paper, we view registers as elements of a finite field and we compute linear permutations without memory. We first determine the maximum complexity of a linear function when only linear instructions are allowed. We also determine which linear functions are hardest to compute when the field in question is the binary field and the number of registers is even. Secondly, we investigate some matrix groups, thus showing that the special linear group is internally computable but not fast. Thirdly, we determine the smallest set of instructions required to generate the special and general linear groups. These results are important for memoryless computation, for they show that linear functions can be computed very fast or that very few instructions are needed to compute any linear function. They thus indicate new advantages of using memoryless computation.
Multirate generalized additive Runge Kutta methods
Venue: arXiv cs.NA 2013
Year: 2013

This work constructs a new class of multirate schemes based on the recently developed generalized additive Runge-Kutta (GARK) methods (Sandu and Guenther, 2013). Multirate schemes use different step sizes for different components and for different partitions of the right-hand side based on the local activity levels. We show that the new multirate GARK family includes many well-known multirate schemes as special cases. The order conditions theory follows directly from the GARK accuracy theory. Nonlinear stability and monotonicity investigations show that these properties are inherited from the base schemes provided that additional coupling conditions hold.
Self-Organizing Mobility Robustness Optimization in LTE Networks with eICIC
Venue: arXiv cs.NI 2013
Year: 2013

We address the problem of Mobility Robustness Optimization (MRO) and describe centralized Self Organizing Network (SON) solutions that can optimize connected-mode mobility Key Performance Indicators (KPIs). Our solution extends the earlier work of eICIC parameter optimization [7], to heterogeneous networks with mobility, and outline methods of progressive complexity that optimize the Retaining/Offloading Bias which are macro/pico views of Cell Individual Offset parameters. Simulation results under real LTE network deployment assumptions of a US metropolitan area demonstrate the effects of such solutions on the mobility KPIs. To our knowledge, this solution is the first that demonstrates the joint optimization of eICIC and MRO.
Can Facial Uniqueness be Inferred from Impostor Scores?
Venue: arXiv cs.CV 2013
Year: 2013

In Biometrics, facial uniqueness is commonly inferred from impostor similarity scores. In this paper, we show that such uniqueness measures are highly unstable in the presence of image quality variations like pose, noise and blur. We also experimentally demonstrate the instability of a recently introduced impostor-based uniqueness measure of [Klare and Jain 2013] when subject to poor quality facial images.
Deludedly Agreeing to Agree
Venue: arXiv cs.GT 2013
Year: 2013

We study conditions relating to the impossibility of agreeing to disagree in models of interactive KD45 belief (in contrast to models of S5 knowledge, which are used in nearly all the agreements literature). We show that even when the truth axiom is not assumed it turns out that players will find it impossible to agree to disagree under fairly broad conditions.
Agreeing on Decisions: An Analysis with Counterfactuals
Venue: arXiv cs.GT 2013
Year: 2013

Moses & Nachum ([7]) identify conceptual flaws in Bacharach's generalization ([3]) of Aumann's seminal "agreeing to disagree" result ([1]). Essentially, Bacharach's framework requires agents' decision functions to be defined over events that are informationally meaningless for the agents. In this paper, we argue that the analysis of the agreement theorem should be carried out in information structures that can accommodate for counterfactual states. We therefore develop a method for constructing such "counterfactual structures" (starting from partitional structures), and prove a new agreement theorem within such structures. Furthermore, we show that our approach also resolves the conceptual flaws in the sense that, within our framework, decision functions are always only defined over events that are informationally meaningful for the agents.
Reasoning Under the Principle of Maximum Entropy for Modal Logics K45, KD45, and S5
Venue: arXiv cs.LO 2013
Year: 2013

We propose modal Markov logic as an extension of propositional Markov logic to reason under the principle of maximum entropy for modal logics K45, KD45, and S5. Analogous to propositional Markov logic, the knowledge base consists of weighted formulas, whose weights are learned from data. However, in contrast to Markov logic, in our framework we use the knowledge base to define a probability distribution over non-equivalent epistemic situations (pointed Kripke structures) rather than over atoms, and use this distribution to assign probabilities to modal formulas. As in all probabilistic representations, the central task in our framework is inference. Although the size of the state space grows doubly exponentially in the number of propositions in the domain, we provide an algorithm that scales only exponentially in the size of the knowledge base. Finally, we briefly discuss the case of languages with an infinite number of propositions.
Leveraging Physical Layer Capabilites: Distributed Scheduling in Interference Networks with Local Views
Venue: arXiv cs.NI 2013
Year: 2013

In most wireless networks, nodes have only limited local information about the state of the network, which includes connectivity and channel state information. With limited local information about the network, each node's knowledge is mismatched; therefore, they must make distributed decisions. In this paper, we pose the following question - if every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While link scheduling answers the above question for point-to-point physical layers which are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced PHY layer design, as suggested by results in network information theory. To make progress on this challenging problem, we propose a constructive distributed algorithm that achieves rates higher than link scheduling based on interference avoidance, especially if each node knows more than one hop of network state information. We compare our new aggressive algorithm to a conservative algorithm we have presented in [1]. Both algorithms schedule sub-networks such that each sub-network can employ advanced interference-embracing coding schemes to achieve higher rates. Our innovation is in the identification, selection and scheduling of sub-networks, especially when sub-networks are larger than a single link.
A language independent web data extraction using vision based page segmentation algorithm
Venue: arXiv cs.IR 2013
Year: 2013

Web usage mining is a process of extracting useful information from server logs i.e. users history. Web usage mining is a process of finding out what users are looking for on the internet. Some users might be looking at only textual data, where as some others might be interested in multimedia data. One would retrieve the data by copying it and pasting it to the relevant document. But this is tedious and time consuming as well as difficult when the data to be retrieved is plenty. Extracting structured data from a web page is challenging problem due to complicated structured pages. Earlier they were used web page programming language dependent; the main problem is to analyze the html source code. In earlier they were considered the scripts such as java scripts and cascade styles in the html files. When it makes different for existing solutions to infer the regularity of the structure of the Web Pages only by analyzing the tag structures. To overcome this problem we are using a new algorithm called VIPS algorithm i.e. independent language. This approach primary utilizes the visual features on the webpage to implement web data extraction.
Degrees-of-Freedom Region of MISO-OFDMA Broadcast Channel with Imperfect CSIT
Venue: arXiv cs.IT 2013
Year: 2013

This contribution investigates the Degrees-of-Freedom region of a two-user frequency correlated Multiple-Input-Single-Output (MISO) Broadcast Channel (BC) with imperfect Channel State Information at the transmitter (CSIT). We assume that the system consists of an arbitrary number of subbands, denoted as $L$. Besides, the CSIT state varies across users and subbands. A tight outer-bound is found as a function of the minimum average CSIT quality between the two users. Based on the CSIT states across the subbands, the DoF region is interpreted as a weighted sum of the optimal DoF regions in the scenarios where the CSIT of both users are perfect, alternatively perfect and not known. Inspired by the weighted-sum interpretation and identifying the benefit of the optimal scheme for the unmatched CSIT proposed by Chen et al., we also design a scheme achieving the upper-bound for the general $L$-subband scenario in frequency domain BC, thus showing the optimality of the DoF region.
Code Optimization in FORM
Venue: arXiv cs.SC 2013
Year: 2013

We describe the implementation of output code optimization in the open source computer algebra system FORM. This implementation is based on recently discovered techniques of Monte Carlo tree search to find efficient multivariate Horner schemes, in combination with other optimization algorithms, such as common subexpression elimination. For systems for which no specific knowledge is provided it performs significantly better than other methods we could compare with. Because the method has a number of free parameters, we also show some methods by which to tune them to different types of problems.
A characterization of those automata that structurally generate finite groups
Venue: arXiv cs.FL 2013
Year: 2013

Antonenko and Russyev independently have shown that any Mealy automaton with no cycles with exit--that is, where every cycle in the underlying directed graph is a sink component--generates a fi- nite (semi)group, regardless of the choice of the production functions. Antonenko has proved that this constitutes a characterization in the non-invertible case and asked for the invertible case, which is proved in this paper.
Generalized Thompson Sampling for Contextual Bandits
Venue: arXiv cs.LG 2013
Year: 2013

Thompson Sampling, one of the oldest heuristics for solving multi-armed bandits, has recently been shown to demonstrate state-of-the-art performance. The empirical success has led to great interests in theoretical understanding of this heuristic. In this paper, we approach this problem in a way very different from existing efforts. In particular, motivated by the connection between Thompson Sampling and exponentiated updates, we propose a new family of algorithms called Generalized Thompson Sampling in the expert-learning framework, which includes Thompson Sampling as a special case. Similar to most expert-learning algorithms, Generalized Thompson Sampling uses a loss function to adjust the experts' weights. General regret bounds are derived, which are also instantiated to two important loss functions: square loss and logarithmic loss. In contrast to existing bounds, our results apply to quite general contextual bandits. More importantly, they quantify the effect of the "prior" distribution on the regret bounds.