OpenRaiser/Intern-Atlas huggingface dataset:
Cookies Invading Our Privacy for Marketing Advertising and Security Issues
Venue: arXiv cs.CY 2013
Year: 2013

Privacy has been a major concern for everybody over the internet. Governments across the globe have given their views on how the internet space can be managed effectively so that there is some control on the flow of confidential information and privacy to users and as well as to data is achieved. Taking advantage of the lack of one unified body that could govern the online space with its strict and stringent rules certain websites use the text files called cookies in collecting data from users and using them for marketing them in advertisements networks and third party websites with the help of JavaScript and flash technologies. Even before many of us think what is happening around us in the online usage we are being invaded by the cookies and are targeted with their specific information that could tempt us to buy a product or service to which we were longing for in the recent past. Though it may be argued its a kind of marketing strategy to give the customer what he wants but it can no way be something like the user is just being targeted because he has already shown interest in something and he should be disturbed until he gets hold of something. Its purely a security breach as most of the websites dont ask permission for the usage of cookies and setting them into the users browser and the most important thing is no privacy statement is issued that the information is used for targeted marketing. This analysis paper has views from different sources, an example of such an activity that is a potential security breach and various other information of what these sites do to use the deadly cookies for commercial tricks.
Simulation of a typical house in the region of Antananarivo, Madagascar. Determination of passive solutions using local materials
Venue: arXiv cs.CE 2013
Year: 2013

This paper deals with new proposals for the design of passive solutions adapted to the climate of the highlands of Madagascar. While the strongest population density is located in the central highlands, the problem of thermal comfort in buildings occurs mainly during winter time. Currently, people use raw wood to warm the poorly designed houses. This leads to a large scale deforestation of the areas and causes erosion and environmental problems. The methodology used consisted of the identification of a typical building and of a typical meteorological year. Simulations were carried out using a thermal and airflow software (CODYRUN) to improve each building component (roof, walls, windows, and soil) in such a way as to estimate the influence of some technical solutions on each component in terms of thermal comfort. The proposed solutions also took into account the use of local materials and the standard of living of the country.
Scalable, Robust and Real Time Communication Architecture For Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

In this paper we propose wireless sensor network architecture with layered protocols, targeting different aspects of the awareness requirements in wireless sensor networks. Under such a unified framework, we pay special attention to the most important awareness issues in wireless sensor networks: namely dynamic awareness, energy awareness and spatiotemporal awareness. First, we propose the spatiotemporal aware routing protocol for wireless sensor networks, which maintains a constant delivery speed for soft real-time communication. Second, we introduce the time-energy aware aggregation scheme, which increases the degree of aggregation and reduces energy consumption without jeopardizing the end-to-end delay. It is the data aggregation scheme to take the timely delivery of messages as well as protocol overhead into account to adjust aggregation strategies adaptively in accordance with assessed traffic conditions and expected wireless sensor network requirements. Third, we also deal with dynamic awareness by proposing a new communication category based on a lazy-binding concept. The evaluation of this integrated architecture demonstrates its performance composability and its capability to be tailored for applications with different awareness requirements. We believe that the integrated architecture, supporting various kinds of awareness requirements, lays a foundation for overall wireless sensor network.
Disappointment in Social Choice Protocols
Venue: arXiv cs.MA 2013
Year: 2013

Social choice theory is a theoretical framework for analysis of combining individual preferences, interests, or welfare to reach a collective decision or social welfare in some sense. We introduce a new criterion for social choice protocols called social disappointment. Social disappointment happens when the outcome of a voting system occurs for those alternatives which are at the end of at least half of individual preference profiles. Here we introduce some protocols that prevent social disappointment and prove an impossibility theorem based on this key concept.
Spatially Sparse Precoding in Millimeter Wave MIMO Systems
Venue: arXiv cs.IT 2013
Year: 2013

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.
Weight Distribution for Non-binary Cluster LDPC Code Ensemble
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we derive the average weight distributions for the irregular non-binary cluster low-density parity-check (LDPC) code ensembles. Moreover, we give the exponential growth rate of the average weight distribution in the limit of large code length. We show that there exist $(2,d_c)$-regular non-binary cluster LDPC code ensembles whose normalized typical minimum distances are strictly positive.
A Further Generalization of the Finite-Population Geiringer-like Theorem for POMDPs to Allow Recombination Over Arbitrary Set Covers
Venue: arXiv cs.AI 2013
Year: 2013

A popular current research trend deals with expanding the Monte-Carlo tree search sampling methodologies to the environments with uncertainty and incomplete information. Recently a finite population version of Geiringer theorem with nonhomologous recombination has been adopted to the setting of Monte-Carlo tree search to cope with randomness and incomplete information by exploiting the entrinsic similarities within the state space of the problem. The only limitation of the new theorem is that the similarity relation was assumed to be an equivalence relation on the set of states. In the current paper we lift this "curtain of limitation" by allowing the similarity relation to be modeled in terms of an arbitrary set cover of the set of state-action pairs.
Augmented Reality in ICT for Minimum Knowledge Loss
Venue: arXiv cs.OH 2013
Year: 2013

Informatics world digitizes the human beings, with the contribution made by all the industrial people. In the recent survey it is proved that people are not accustomed or they are not able to access the electronic devices to its extreme usage. Also people are more dependent to the technologies and their day-to-day activities are ruled by the same. In this paper we discuss on one of the advanced technology which will soon rule the world and make the people are more creative and at the same time hassle-free. This concept is introduced as 6th sense technology by an IIT, Mumbai student who is presently Ph.D., scholar in MIT, USA. Similar to this research there is one more research going on under the title Augmented Reality. This research makes a new association with the real world to digital world and allows us to share and manipulate the information directly with our mental thoughts. A college which implements state of the art technology for teaching and learning, Higher College of Technology, Muscat, (HCT) tries to identify the opportunities and limitations of implementing this augmented reality for teaching and learning. The research team of HCT, here, tries to give two scenarios in which augmented reality can fit in. Since this research is in the conceptual level we are trying to illustrate the history of this technology and how it can be adopted in the teaching environment
Finding Distinct Subpalindromes Online
Venue: arXiv cs.DS 2013
Year: 2013

We exhibit an online algorithm finding all distinct palindromes inside a given string in time $Θ(n\log|Σ|)$ over an ordered alphabet and in time $Θ(n|Σ|)$ over an unordered alphabet. Using a reduction from a dictionary-like data structure, we prove the optimality of this algorithm in the comparison-based computation model.
Mnemonics for Higher Education Using Contemporary Technologies
Venue: arXiv cs.CY 2013
Year: 2013

Education pierces into the contemporary world due to the influence of the emerging technologies in the digital world. Education field is also bombarded by the modern technologies and the demands of the digital society. It is affected by cloud computing, green computing and Internet. Still the success of this depends upon the minimization of the knowledge loss, while transferring ideas from teacher to student. Teaching & Learning is always an art. Even though modern technology is introduced along with the traditional method of teaching in Higher College of Technology (HCT), knowledge loss is very high still. Mnemonics is an old tool; still suitable for digital era students with the influence of recent technologies. Hence this paper discusses how to implement this mnemonics with the help of modern technologies like E-Learning and M-Learning. In this paper we have tried to discuss about E-learning and M-learning usage for implementing Mnemonics, and also discussed how far this concept can be used to make the Information and Communication Technology (ICT) more efficient and effective in term of making the students learning experience as a fun and without huge knowledge loss during the teaching and learning process including practical subjects. This paper also explains the initial research conducted with the students of database specialization of HCT and findings of that research about the knowledge loss. We have also suggests the future research areas in Mnemonics.
On the Optimal Representation of Algebraic Expressions of Fibonacci Graphs
Venue: arXiv cs.DS 2013
Year: 2013

The paper investigates relationship between algebraic expressions and graphs. We consider a digraph called a Fibonacci graph which gives a generic example of non-series-parallel graphs. Our intention in this paper is to simplify the expressions of Fibonacci graphs and eventually find their shortest representations. With that end in view, we describe the optimal decomposition method for generating Fibonacci graph expressions that is conjectured to provide these representations. Proof (or disproof) of this conjecture is presented as an open problem.
Early Detection of Alzheimer's - A Crucial Requirement
Venue: arXiv cs.CV 2013
Year: 2013

Alzheimer's, an old age disease of people over 65 years causes problems with memory, thinking and behavior. This disease progresses very slow and its identification in early stages is very difficult. The symptoms of Alzheimer's appear slowly and gradually will have worse effects. In its early stages, not only the patients themselves but their loved ones are generally unable to accept that the patient is suffering from disease. In this paper, we have proposed a new algorithm to detect patients of Alzheimer's at early stages by comparing the Magnetic Resonance Images (MRI) of the patients with normal persons of their age. The progress of the disease can also be monitored by periodic comparison of the previous and current MRI.
Hybrid fuzzy logic and pid controller based ph neutralization pilot plant
Venue: arXiv cs.SY 2013
Year: 2013

Use of Control theory within process control industries has changed rapidly due to the increase complexity of instrumentation, real time requirements, minimization of operating costs and highly nonlinear characteristics of chemical process. Previously developed process control technologies which are mostly based on a single controller are not efficient in terms of signal transmission delays, processing power for computational needs and signal to noise ratio. Hybrid controller with efficient system modelling is essential to cope with the current challenges of process control in terms of control performance. This paper presents an optimized mathematical modelling and advance hybrid controller (Fuzzy Logic and PID) design along with practical implementation and validation of pH neutralization pilot plant. This procedure is particularly important for control design and automation of Physico-chemical systems for process control industry.
Opportunities & Challenges In Automatic Speech Recognition
Venue: arXiv cs.CL 2013
Year: 2013

Automatic speech recognition enables a wide range of current and emerging applications such as automatic transcription, multimedia content analysis, and natural human-computer interfaces. This paper provides a glimpse of the opportunities and challenges that parallelism provides for automatic speech recognition and related application research from the point of view of speech researchers. The increasing parallelism in computing platforms opens three major possibilities for speech recognition systems: improving recognition accuracy in non-ideal, everyday noisy environments; increasing recognition throughput in batch processing of speech data; and reducing recognition latency in realtime usage scenarios. This paper describes technical challenges, approaches taken, and possible directions for future research to guide the design of efficient parallel software and hardware infrastructures.
Increasing the Network life Time by Simulated Annealing Algorithm in WSN with Point Coverage
Venue: arXiv cs.NI 2013
Year: 2013

Since we are not able to replace the battery in a wireless sensor networks (WSNs), the issues of energy and lifetime are the most important parameters. In asymmetrical networks, different sensors with various abilities are used. Super nodes, with higher power and wider range of communication in comparison with common sensors, are used to cause connectivity and transmit data to base stations in these networks. It is crucial to select the parameters of fit function and monitoring sensors optimally in a point covering network. In this paper, we utilized an algorithm to select monitoring sensors. The selection is done by using a novel algorithm that used by simulated annealing. This selection takes remained energy into consideration. This method increases lifetime, decreases and balances energy consumption as confirmed by simulation results.
Unraveling the Evolution of Defectors in Online Business Games
Venue: arXiv cs.GT 2013
Year: 2013

Anonymous online business environments have a social dilemma situation in it. A dilemma on whether to cooperate or Defect. Defection by a buyer to seller and/or seller to buyer might give each a better profit at the cost of the loss of other. However, if these parties were to interact in future too, a bad past reference might prevent cooperative actions, thus depriving each other from a better gain. The anonymity of the players and an absence of central governing body still make this environment tempting for the defectors. What might be the evolutionary behavior of defectors in such environment? How could their increasing population be controlled? It is these two questions basically that we attempt to address in this research work. A genetic algorithm based spatial iterated prisoners dilemma (SIPD) environment has been used to simulate the experiments. A case where compensation for the looser is provided by the system is modeled and analyzed through experiments. Our results show that compensation can be useful in decreasing defective population in the society, however, this might not be enough for the evolution of a cooperative and reliable society of trustworthy players.
Estimating or Propagating Gradients Through Stochastic Neurons
Venue: arXiv cs.LG 2013
Year: 2013

Stochastic neurons can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic neurons, i.e., can we "back-propagate" through these stochastic neurons? We examine this question, existing approaches, and present two novel families of solutions, applicable in different settings. In particular, it is demonstrated that a simple biologically plausible formula gives rise to an an unbiased (but noisy) estimator of the gradient with respect to a binary stochastic neuron firing probability. Unlike other estimators which view the noise as a small perturbation in order to estimate gradients by finite differences, this estimator is unbiased even without assuming that the stochastic perturbation is small. This estimator is also interesting because it can be applied in very general settings which do not allow gradient back-propagation, including the estimation of the gradient with respect to future rewards, as required in reinforcement learning setups. We also propose an approach to approximating this unbiased but high-variance estimator by learning to predict it using a biased estimator. The second approach we propose assumes that an estimator of the gradient can be back-propagated and it provides an unbiased estimator of the gradient, but can only work with non-linearities unlike the hard threshold, but like the rectifier, that are not flat for all of their range. This is similar to traditional sigmoidal units but has the advantage that for many inputs, a hard decision (e.g., a 0 output) can be produced, which would be convenient for conditional computation and achieving sparse representations and sparse gradients.
Fast Linearized Alternating Direction Minimization Algorithm with Adaptive Parameter Selection for Multiplicative Noise Removal
Venue: arXiv cs.CV 2013
Year: 2013

Owing to the edge preserving ability and low computational cost of the total variation (TV), variational models with the TV regularization have been widely investigated in the field of multiplicative noise removal. The key points of the successful application of these models lie in: the optimal selection of the regularization parameter which balances the data-fidelity term with the TV regularizer; the efficient algorithm to compute the solution. In this paper, we propose two fast algorithms based on the linearized technique, which are able to estimate the regularization parameter and recover the image simultaneously. In the iteration step of the proposed algorithms, the regularization parameter is adjusted by a special discrepancy function defined for multiplicative noise. The convergence properties of the proposed algorithms are proved under certain conditions, and numerical experiments demonstrate that the proposed algorithms overall outperform some state-of-the-art methods in the PSNR values and computational time.
Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising
Venue: arXiv cs.GT 2013
Year: 2013

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.
Clustering Algorithms for Scale-free Networks and Applications to Cloud Resource Management
Venue: arXiv cs.DC 2013
Year: 2013

In this paper we introduce algorithms for the construction of scale-free networks and for clustering around the nerve centers, nodes with a high connectivity in a scale-free networks. We argue that such overlay networks could support self-organization in a complex system like a cloud computing infrastructure and allow the implementation of optimal resource management policies.
A Bag of Words Approach for Semantic Segmentation of Monitored Scenes
Venue: arXiv cs.CV 2013
Year: 2013

This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera. Our algorithm classifies each perceptually homogenous region as one of the predefined classes learned from a collection of manually labelled images. The proposed approach combines two different types of information. First, color segmentation is performed to divide the scene into perceptually similar regions. Then, the second step is based on SIFT keypoints and uses the bag of words representation of the regions for the classification. The prediction is done using a Naïve Bayesian Network as a generative classifier. Compared to existing techniques, our method provides more compact representations of scene contents and the segmentation result is more consistent with human perception due to the combination of the color information with the image keypoints. The experiments conducted on a publicly available data set demonstrate the validity of the proposed method.
ScrambleSuit: A Polymorph Network Protocol to Circumvent Censorship
Venue: arXiv cs.CR 2013
Year: 2013

Deep packet inspection technology became a cornerstone of Internet censorship by facilitating cheap and effective filtering of what censors consider undesired information. Moreover, filtering is not limited to simple pattern matching but makes use of sophisticated techniques such as active probing and protocol classification to block access to popular circumvention tools such as Tor. In this paper, we propose ScrambleSuit; a thin protocol layer above TCP whose purpose is to obfuscate the transported application data. By using morphing techniques and a secret exchanged out-of-band, we show that ScrambleSuit can defend against active probing and other fingerprinting techniques such as protocol classification and regular expressions. We finally demonstrate that our prototype exhibits little overhead and enables effective and lightweight obfuscation for application layer protocols.