OpenRaiser/Intern-Atlas huggingface dataset:
A Linear Belief Function Approach to Portfolio Evaluation
Venue: arXiv cs.AI 2012
Year: 2012

By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset pricing models. We then appeal to Dempster's rule of combination to integrate the knowledge for assessing an overall belief of portfolio performance, and updating the belief by incorporating additional information. We use an example of three gold stocks to illustrate the approach.
Approximate Decomposition: A Method for Bounding and Estimating Probabilistic and Deterministic Queries
Venue: arXiv cs.AI 2012
Year: 2012

In this paper, we introduce a method for approximating the solution to inference and optimization tasks in uncertain and deterministic reasoning. Such tasks are in general intractable for exact algorithms because of the large number of dependency relationships in their structure. Our method effectively maps such a dense problem to a sparser one which is in some sense "closest". Exact methods can be run on the sparser problem to derive bounds on the original answer, which can be quite sharp. We present empirical results demonstrating that our method works well on the tasks of belief inference and finding the probability of the most probable explanation in belief networks, and finding the cost of the solution that violates the smallest number of constraints in constraint satisfaction problems. On one large CPCS network, for example, we were able to calculate upper and lower bounds on the conditional probability of a variable, given evidence, that were almost identical in the average case.
1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"
Venue: arXiv cs.IR 2012
Year: 2012

We exploit the redundancy and volume of information on the web to build a computerized player for the ABC TV game show 'Who Wants To Be A Millionaire?' The player consists of a question-answering module and a decision-making module. The question-answering module utilizes question transformation techniques, natural language parsing, multiple information retrieval algorithms, and multiple search engines; results are combined in the spirit of ensemble learning using an adaptive weighting scheme. Empirically, the system correctly answers about 75% of questions from the Millionaire CD-ROM, 3rd edition - general-interest trivia questions often about popular culture and common knowledge. The decision-making module chooses from allowable actions in the game in order to maximize expected risk-adjusted winnings, where the estimated probability of answering correctly is a function of past performance and confidence in in correctly answering the current question. When given a six question head start (i.e., when starting from the $2,000 level), we find that the system performs about as well on average as humans starting at the beginning. Our system demonstrates the potential of simple but well-chosen techniques for mining answers from unstructured information such as the web.
Policy-contingent abstraction for robust robot control
Venue: arXiv cs.RO 2012
Year: 2012

This paper presents a scalable control algorithm that enables a deployed mobile robot system to make high-level decisions under full consideration of its probabilistic belief. Our approach is based on insights from the rich literature of hierarchical controllers and hierarchical MDPs. The resulting controller has been successfully deployed in a nursing facility near Pittsburgh, PA. To the best of our knowledge, this work is a unique instance of applying POMDPs to high-level robotic control problems.
An Axiomatic Approach to Robustness in Search Problems with Multiple Scenarios
Venue: arXiv cs.AI 2012
Year: 2012

This paper is devoted to the search of robust solutions in state space graphs when costs depend on scenarios. We first present axiomatic requirements for preference compatibility with the intuitive idea of robustness.This leads us to propose the Lorenz dominance rule as a basis for robustness analysis. Then, after presenting complexity results about the determination of robust solutions, we propose a new sophistication of A* specially designed to determine the set of robust paths in a state space graph. The behavior of the algorithm is illustrated on a small example. Finally, an axiomatic justification of the refinement of robustness by an OWA criterion is provided.
Dealing with uncertainty in fuzzy inductive reasoning methodology
Venue: arXiv cs.AI 2012
Year: 2012

The aim of this research is to develop a reasoning under uncertainty strategy in the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR emerged from the General Systems Problem Solving developed by G. Klir. It is a data driven methodology based on systems behavior rather than on structural knowledge. It is a very useful tool for both the modeling and the prediction of those systems for which no previous structural knowledge is available. FIR reasoning is based on pattern rules synthesized from the available data. The size of the pattern rule base can be very large making the prediction process quite difficult. In order to reduce the size of the pattern rule base, it is possible to automatically extract classical Sugeno fuzzy rules starting from the set of pattern rules. The Sugeno rule base preserves pattern rules knowledge as much as possible. In this process some information is lost but robustness is considerably increased. In the forecasting process either the pattern rule base or the Sugeno fuzzy rule base can be used. The first option is desirable when the computational resources make it possible to deal with the overall pattern rule base or when the extracted fuzzy rules are not accurate enough due to uncertainty associated to the original data. In the second option, the prediction process is done by means of the classical Sugeno inference system. If the amount of uncertainty associated to the data is small, the predictions obtained using the Sugeno fuzzy rule base will be very accurate. In this paper a mixed pattern/fuzzy rules strategy is proposed to deal with uncertainty in such a way that the best of both perspectives is used. Areas in the data space with a higher level of uncertainty are identified by means of the so-called error models. The prediction process in these areas makes use of a mixed pattern/fuzzy rules scheme, whereas areas identified with a lower level of uncertainty only use the Sugeno fuzzy rule base. The proposed strategy is applied to a real biomedical system, i.e., the central nervous system control of the cardiovascular system.
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Venue: arXiv cs.AI 2012
Year: 2012

Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function by the heuristic methods: loopy belief Propagation and e-cutoff. We tested the performance of e-cutoff on three large real Bayesian networks: ANDES, CPCS, and PATHFINDER. We observed that on each of these networks the EPIS-BN algorithm gives us a considerable improvement over the current state of the art algorithm, the AIS-BN algorithm. In addition, it avoids the costly learning stage of the AIS-BN algorithm.
Efficient Inference in Large Discrete Domains
Venue: arXiv cs.AI 2012
Year: 2012

In this paper we examine the problem of inference in Bayesian Networks with discrete random variables that have very large or even unbounded domains. For example, in a domain where we are trying to identify a person, we may have variables that have as domains, the set of all names, the set of all postal codes, or the set of all credit card numbers. We cannot just have big tables of the conditional probabilities, but need compact representations. We provide an inference algorithm, based on variable elimination, for belief networks containing both large domain and normal discrete random variables. We use intensional (i.e., in terms of procedures) and extensional (in terms of listing the elements) representations of conditional probabilities and of the intermediate factors.
Enhanced Image Analysis Using Cached Mobile Robots
Venue: arXiv cs.OH 2012
Year: 2012

In the field of Artificial intelligence Image processing plays a vital role in Decision making. Nowadays Mobile robots work as a Network sharing Centralized Database. All Image inputs are compared against this database and decision is made. In some cases the Centralized database is in other side of the globe and Mobile robots compare Input image through satellite link this sometime results in delays in decision making which may result in castrophe. This Research paper is about how to make image processing in mobile robots less time consuming and fast decision making. This research paper compares search techniques employed currently and optimum search method which we are going to state. Nowadays Mobile robots are extensively used in environments which are dangerous to human beings. In this dangerous situations quick Decision making makes the difference between Hit and Miss this can also results in Day to day tasks performed by Mobile robots Successful or Failure.
Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs
Venue: arXiv cs.LG 2012
Year: 2012

We consider the problem of learning the structure of undirected graphical models with bounded treewidth, within the maximum likelihood framework. This is an NP-hard problem and most approaches consider local search techniques. In this paper, we pose it as a combinatorial optimization problem, which is then relaxed to a convex optimization problem that involves searching over the forest and hyperforest polytopes with special structures, independently. A supergradient method is used to solve the dual problem, with a run-time complexity of $O(k^3 n^{k+2} \log n)$ for each iteration, where $n$ is the number of variables and $k$ is a bound on the treewidth. We compare our approach to state-of-the-art methods on synthetic datasets and classical benchmarks, showing the gains of the novel convex approach.
Base Station Cooperation with Feedback Optimization: A Large System Analysis
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we study feedback optimization problems that maximize the users' signal to interference plus noise ratio (SINR) in a two-cell MIMO broadcast channel. Assuming the users learn their direct and interfering channels perfectly, they can feed back this information to the base stations (BSs) over the uplink channels. The BSs then use the channel information to design their transmission scheme. Two types of feedback are considered: analog and digital. In the analog feedback case, the users send their unquantized and uncoded CSI over the uplink channels. In this context, given a user's fixed transmit power, we investigate how he/she should optimally allocate it to feed back the direct and interfering (or cross) CSI for two types of base station cooperation schemes, namely, Multi-Cell Processing (MCP) and Coordinated Beamforming (CBf). In the digital feedback case, the direct and cross link channel vectors of each user are quantized separately, each using RVQ, with different size codebooks. The users then send the index of the quantization vector in the corresponding codebook to the BSs. Similar to the feedback optimization problem in the analog feedback, we investigate the optimal bit partitioning for the direct and interfering link for both types of cooperation. We focus on regularized channel inversion precoding structures and perform our analysis in the large system limit in which the number of users per cell ($K$) and the number of antennas per BS ($N$) tend to infinity with their ratio $β=\frac{K}{N}$ held fixed.
A Lossless Data Hiding Technique based on AES-DWT
Venue: arXiv cs.CR 2012
Year: 2012

In this paper we propose a new data hiding technique. The new technique uses steganography and cryptography on images with a size of 256x256 pixels and an 8-bit grayscale format. There are design restrictions such as a fixed-size cover image, and reconstruction without error of the hidden image. The steganography technique uses a Haar-DWT (Discrete Wavelet Transform) with hard thresholding and LSB (Less Significant Bit) technique on the cover image. The algorithms used for compressing and ciphering the secret image are lossless JPG and AES, respectively. The proposed technique is used to generate a stego image which provides a double type of security that is robust against attacks. Results are reported for different thresholds levels in terms of PSNR.
Performance Analysis of ANFIS in short term Wind Speed Prediction
Venue: arXiv cs.AI 2012
Year: 2012

Results are presented on the performance of Adaptive Neuro-Fuzzy Inference system (ANFIS) for wind velocity forecasts in the Isthmus of Tehuantepec region in the state of Oaxaca, Mexico. The data bank was provided by the meteorological station located at the University of Isthmus, Tehuantepec campus, and this data bank covers the period from 2008 to 2011. Three data models were constructed to carry out 16, 24 and 48 hours forecasts using the following variables: wind velocity, temperature, barometric pressure, and date. The performance measure for the three models is the mean standard error (MSE). In this work, performance analysis in short-term prediction is presented, because it is essential in order to define an adequate wind speed model for eolian parks, where a right planning provide economic benefits.
Mining the Web for the Voice of the Herd to Track Stock Market Bubbles
Venue: arXiv cs.CL 2012
Year: 2012

We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Using a four-year corpus of 17,713 online, finance-related articles (10M+ words) from the Financial Times, the New York Times, and the BBC, we show that week-to-week changes in power-law distributions reflect market movements of the Dow Jones Industrial Average (DJI), the FTSE-100, and the NIKKEI-225. Notably, the statistical regularities in language track the 2007 stock market bubble, showing emerging structure in the language of commentators, as progressively greater agreement arose in their positive perceptions of the market. Furthermore, during the bubble period, a marked divergence in positive language occurs as revealed by a Kullback-Leibler analysis.
Coevolving Cellular Automata with Memory for Chemical Computing: Boolean Logic Gates in the B-Z Reaction
Venue: arXiv cs.ET 2012
Year: 2012

We propose that the behaviour of non-linear media can be controlled automatically through coevolutionary systems. By extension, forms of unconventional computing, i.e., massively parallel non-linear computers, can be realised by such an approach. In this study a light-sensitive sub-excitable Belousov-Zhabotinsky reaction is controlled using various heterogeneous cellular automata. A checkerboard image comprising of varying light intensity cells is projected onto the surface of a catalyst-loaded gel resulting in rich spatio-temporal chemical wave behaviour. The coevolved cellular automata are shown to be able to control chemical activity through dynamic control of the light intensity. The approach is demonstrated through the creation of a number of simple Boolean logic gates.
A Text Steganography Method Using Pangram and Image Mediums
Venue: arXiv cs.CR 2012
Year: 2012

Steganography is the art and science of writing hidden messages in such a way that no one apart from the sender and the receiver would realize that a secret communicating is taking place. Unlike cryptography which only scrambles secret data keeping them overt, steganography covers secret data into medium files such as image files and transmits them in total secrecy avoiding drawing eavesdroppers suspicions. However, considering that the public channel is monitored by eavesdroppers, it is vulnerable to stego-attacks which refer to randomly trying to break the medium file and recover the secret data out of it. That is often true because steganalysts assume that the secret data are encoded into a single medium file and not into multiple ones that complement each other. This paper proposes a text steganography method for hiding secret textual data using two mediums; a Pangram sentence containing all the characters of the alphabet, and an uncompressed image file. The algorithm tries to search for every character of the secret message into the Pangram text. The search starts from a random index called seed and ends up on the index of the first occurrence of the character being searched for. As a result, two indexes are obtained, the seed and the offset indexes. Together they are embedded into the three LSBs of the color channels of the image medium. Ultimately, both mediums mainly the Pangram and the image are sent to the receiver. The advantage of the proposed method is that it makes the covert data hard to be recovered by unauthorized parties as it uses two mediums, instead of one, to deliver the secret data. Experiments conducted, illustrated an example that explained how to encode and decode a secret text message using the Pangram and the image mediums.
A nuclear-norm based convex formulation for informed source separation
Venue: arXiv cs.SD 2012
Year: 2012

We study the problem of separating audio sources from a single linear mixture. The goal is to find a decomposition of the single channel spectrogram into a sum of individual contributions associated to a certain number of sources. In this paper, we consider an informed source separation problem in which the input spectrogram is partly annotated. We propose a convex formulation that relies on a nuclear norm penalty to induce low rank for the contributions. We show experimentally that solving this model with a simple subgradient method outperforms a previously introduced nonnegative matrix factorization (NMF) technique, both in terms of source separation quality and computation time.
Identifying Metaphor Hierarchies in a Corpus Analysis of Finance Articles
Venue: arXiv cs.CL 2012
Year: 2012

Using a corpus of over 17,000 financial news reports (involving over 10M words), we perform an analysis of the argument-distributions of the UP- and DOWN-verbs used to describe movements of indices, stocks, and shares. Using measures of the overlap in the argument distributions of these verbs and k-means clustering of their distributions, we advance evidence for the proposal that the metaphors referred to by these verbs are organised into hierarchical structures of superordinate and subordinate groups.
Information Capacity of an Energy Harvesting Sensor Node
Venue: arXiv cs.IT 2012
Year: 2012

Energy harvesting sensor nodes are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting 'green communication'. In this paper we focus on communicating reliably over an AWGN channel using such an energy harvesting sensor node. An important part of this work involves appropriate modeling of the energy harvesting, as done via various practical architectures. Our main result is the characterization of the Shannon capacity of the communication system. The key technical challenge involves dealing with the dynamic (and stochastic) nature of the (quadratic) cost of the input to the channel. As a corollary, we find close connections between the capacity achieving energy management policies and the queueing theoretic throughput optimal policies.
Complexity fits the fittest
Venue: arXiv cs.LO 2012
Year: 2012

In this paper we shall relate computational complexity to the principle of natural selection. We shall do this by giving a philosophical account of complexity versus universality. It seems sustainable to equate universal systems to complex systems or at least to potentially complex systems. Post's problem on the existence of (natural) intermediate degrees (between decidable and universal RE) then finds its analog in the Principle of Computional Equivalence (PCE). In this paper we address possible driving forces --if any-- behind PCE. Both the natural aspects as well as the cognitive ones are investigated. We postulate a principle GNS that we call the Generalized Natural Selection principle that together with the Church-Turing thesis is seen to be in close correspondence to a weak version of PCE. Next, we view our cognitive toolkit in an evolutionary light and postulate a principle in analogy with Fodor's language principle. In the final part of the paper we reflect on ways to provide circumstantial evidence for GNS by means of theorems, experiments or, simulations.
Language Without Words: A Pointillist Model for Natural Language Processing
Venue: arXiv cs.CL 2012
Year: 2012

This paper explores two separate questions: Can we perform natural language processing tasks without a lexicon?; and, Should we? Existing natural language processing techniques are either based on words as units or use units such as grams only for basic classification tasks. How close can a machine come to reasoning about the meanings of words and phrases in a corpus without using any lexicon, based only on grams? Our own motivation for posing this question is based on our efforts to find popular trends in words and phrases from online Chinese social media. This form of written Chinese uses so many neologisms, creative character placements, and combinations of writing systems that it has been dubbed the "Martian Language." Readers must often use visual queues, audible queues from reading out loud, and their knowledge and understanding of current events to understand a post. For analysis of popular trends, the specific problem is that it is difficult to build a lexicon when the invention of new ways to refer to a word or concept is easy and common. For natural language processing in general, we argue in this paper that new uses of language in social media will challenge machines' abilities to operate with words as the basic unit of understanding, not only in Chinese but potentially in other languages.
Space Efficient Cryptographic Protocol Using Recursive Bitwise & Pairs Of Bits Of Operation (RBPBO)
Venue: arXiv cs.CR 2012
Year: 2012

The technique considers a message as binary string on which a Efficient Cryptographic Protocol using Recursive Bitwise amd pairs of Bits of operation (RBPBO) is performed. A block of n bits is taken as an input stream, where n varies from 4 to 256, from a continuous stream of bits and the technique operates on it to generate the intermediate encrypted stream. This technique directly involves all the bits of blocks in a boolean operation and a session key. The same operation is performed repeatedly for different block sizes as per the specification of a session key to generate the final encrypted stream. It is a kind of block cipher and symmetric in nature hence, decoding is done following the same procedure. A comparison of the proposed technique with existing and industrially accepted RSA and TDES has also been done in terms of frequency distribution and non homogeneity of source and encrypted files. Key words: Space Efficient Cryptographic Protocol using Recursive Bitwise & pairs of Bits of operation (RBPBO),Cipher text, Block cipher, Session Key.