OpenRaiser/Intern-Atlas huggingface dataset:
Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-based Speed Estimation
Venue: arXiv cs.NI 2013
Year: 2013

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location based service, and Wi-Fi fingerprint-based approaches are the most popular and inexpensive solutions. Among them, one of the main trends is to incorporate the built-in sensors of smartphone and to exploit crowdsourcing potentials. However the noisy built-in sensors and multi-tasking limitation of underline OS often hinder the effectiveness of these schemes. In this work, we propose a passive crowdsourcing CSI-based indoor localization scheme, C2 IL. Our scheme C2 IL only requires the locating-device (e.g., a phone) to have a 802.11n wireless connection, and it does not rely on inertial sensors only existing in some smartphones. C2 IL is built upon our innovative method to accurately estimate the moving distance purely based on 802.11n Channel State Information (CSI). Our extensive evaluations show that the moving distance estimation error of our scheme is within 3% of the actual moving distance regardless of varying speeds and environment. Relying on the accurate moving distance estimation as constraints, we are able to construct a more accurate mapping between RSS fingerprints and location. To address the challenges of collecting fingerprints, a crowdsourcing- based scheme is designed to gradually establish the mapping and populate the fingerprints. In C2 IL, we design a trajectory clustering-based localization algorithm to provide precise real-time indoor localization and tracking. We developed and deployed a practical working system of C2 IL in a large office environment. Extensive evaluation results indicate that our scheme C2 IL provides accurate localization with error 2m at 80% at very complex indoor environment with minimal overhead.
Speed-Aware Routing for UAV Ad-Hoc Networks
Venue: arXiv cs.NI 2013
Year: 2013

In this paper we examine mobile ad-hoc networks (MANET) composed by unmanned aerial vehicles (UAVs). Due to the high-mobility of the nodes, these networks are very dynamic and the existing routing protocols partly fail to provide a reliable communication. We present Predictive-OLSR an extension to the Optimized Link-State Routing (OLSR) protocol: it enables efficient routing in very dynamic conditions. The key idea is to exploit GPS information to aid the routing protocol. Predictive-OLSR weights the expected transmission count (ETX) metric, taking into account the relative speed between the nodes. We provide numerical results obtained by a MAC-layer emulator that integrates a flight simulator to reproduce realistic flight conditions. These numerical results show that Predictive-OLSR significantly outperforms OLSR and BABEL, providing a reliable communication even in very dynamic conditions.
Time-Series Classification Through Histograms of Symbolic Polynomials
Venue: arXiv cs.AI 2013
Year: 2013

Time-series classification has attracted considerable research attention due to the various domains where time-series data are observed, ranging from medicine to econometrics. Traditionally, the focus of time-series classification has been on short time-series data composed of a unique pattern with intraclass pattern distortions and variations, while recently there have been attempts to focus on longer series composed of various local patterns. This study presents a novel method which can detect local patterns in long time-series via fitting local polynomial functions of arbitrary degrees. The coefficients of the polynomial functions are converted to symbolic words via equivolume discretizations of the coefficients' distributions. The symbolic polynomial words enable the detection of similar local patterns by assigning the same words to similar polynomials. Moreover, a histogram of the frequencies of the words is constructed from each time-series' bag of words. Each row of the histogram enables a new representation for the series and symbolize the existence of local patterns and their frequencies. Experimental evidence demonstrates outstanding results of our method compared to the state-of-art baselines, by exhibiting the best classification accuracies in all the datasets and having statistically significant improvements in the absolute majority of experiments.
A cryptographic image encryption technique for facial-blurring of images
Venue: arXiv cs.CR 2013
Year: 2013

Protection of faces in pictures and videos of people in connection with sensitive information, activism, abused cases and others on public broadcasting media and social net- works is very important. On social networks like YouTube,facebook, Twitter and others, videos are being posted with blurring techniques of which some of them cannot be recoverable. Most blurring techniques used can easily be recoverable using off-the-shelf software. The ones that are difficult to be recovered also can easily be used by abusers and ther wrong doers. This paper proposes an image encryption technique that will make it possible for selected facial area to be encrypted based on RGB pixel shuffling of an m*n size image. This will make it difficult for off-the-shelf software to restore the ncrypted image and also make it easy for the law enforcement agencies to reconstruct the face back in case the picture or video is related to an abuse case. The implementation of the encryption method will be done using MATLAB. At the and, there will be no change in the total size of the image during encryption and decryption process.
Fixed Parameter Complexity and Approximability of Norm Maximization
Venue: arXiv cs.CC 2013
Year: 2013

The problem of maximizing the $p$-th power of a $p$-norm over a halfspace-presented polytope in $\R^d$ is a convex maximization problem which plays a fundamental role in computational convexity. It has been shown in 1986 that this problem is $\NP$-hard for all values $p \in \mathbb{N}$, if the dimension $d$ of the ambient space is part of the input. In this paper, we use the theory of parametrized complexity to analyze how heavily the hardness of norm maximization relies on the parameter $d$. More precisely, we show that for $p=1$ the problem is fixed parameter tractable but that for all $p \in \mathbb{N} \setminus \{1\}$ norm maximization is W[1]-hard. Concerning approximation algorithms for norm maximization, we show that for fixed accuracy, there is a straightforward approximation algorithm for norm maximization in FPT running time, but there is no FPT approximation algorithm, the running time of which depends polynomially on the accuracy. As with the $\NP$-hardness of norm maximization, the W[1]-hardness immediately carries over to various radius computation tasks in Computational Convexity.
Mesure de la similarité entre termes et labels de concepts ontologiques
Venue: arXiv cs.IR 2013
Year: 2013

We propose in this paper a method for measuring the similarity between ontological concepts and terms. Our metric can take into account not only the common words of two strings to compare but also other features such as the position of the words in these strings, or the number of deletion, insertion or replacement of words required for the construction of one of the two strings from each other. The proposed method was then used to determine the ontological concepts which are equivalent to the terms that qualify toponymes. It aims to find the topographical type of the toponyme.
Distortion bounds and Two-Way Protocols for One-Shot Transmission of Correlated Random Variables
Venue: arXiv cs.IT 2013
Year: 2013

This paper provides lower bounds on the reconstruction error for transmission of two continuous correlated random vectors sent over both sum and parallel channels using the help of two causal feedback links from the decoder to the encoders connected to each sensor. This construction is considered for both uniformly and normally distributed sources with zero mean and unit variance. Additionally, a two-way retransmission protocol, which is a non-coherent adaptation of the original work by Yamamoto is introduced for an additive white Gaussian noise channel with one degree of freedom. Furthermore, the novel protocol of a single source is extended to the dual-source case again for two different source distributions. Asymptotic optimality of the protocols are analyzed and upper bounds on the distortion level are derived for two-rounds considering two extreme cases of high and low correlation among the sources. It is shown by both the upper and lower-bounds that collaboration can be achieved through energy accumulation. Analytical results are supported by numerical analysis for both the single and dual-source cases to show the improvement in terms of distortion to be gained by retransmission subject to the average energy used by protocol . To cover a more realistic scenario, the same protocol of a single source is adapted to a wireless channel and their performances are compared through numerical evaluation.
Incentives, Quality, and Risks: A Look Into the NSF Proposal Review Pilot
Venue: arXiv cs.GT 2013
Year: 2013

The National Science Foundation (NSF) will be experimenting with a new distributed approach to reviewing proposals, whereby a group of principal investigators (PIs) or proposers in a subfield act as reviewers for the proposals submitted by the same set of PIs. To encourage honesty, PIs' chances for getting funded are tied to the quality of their reviews (with respect to the reviews provided by the entire group), in addition to the quality of their proposals. Intuitively, this approach can more fairly distribute the review workload, discourage frivolous proposal submission, and encourage high quality reviews. On the other hand, this method has already raised concerns about the integrity of the process and the possibility of strategic manipulation. In this paper, we take a closer look at three specific issues in an attempt to gain a better understanding of the strengths and limitations of the new process beyond first impressions and anecdotal evidence. We start by considering the benefits and drawbacks of bundling the quality of PIs' reviews with the scientific merit of their proposals. We then consider the issue of collusion and favoritism. Finally, we examine whether the new process puts controversial proposals at a disadvantage. We conclude that some benefits of using review quality as an incentive mechanism may outweigh its drawbacks. On the other hand, even a coalition of two PIs can cause significant harm to the process, as the built-in incentives are not strong enough to deter collusion. While we also confirm the common suspicion that the process is skewed toward non-controversial proposals, the more unexpected finding is that among equally controversial proposals, those of lower quality get a leg up through this process. Thus the process not only favors non-controversial proposals, but in some sense, mediocrity. We also discuss possible ways to improve this review process.
Selection Mammogram Texture Descriptors Based on Statistics Properties Backpropagation Structure
Venue: arXiv cs.CV 2013
Year: 2013

Computer Aided Diagnosis (CAD) system has been developed for the early detection of breast cancer, one of the most deadly cancer for women. The benign of mammogram has different texture from malignant. There are fifty mammogram images used in this work which are divided for training and testing. Therefore, the selection of the right texture to determine the level of accuracy of CAD system is important. The first and second order statistics are the texture feature extraction methods which can be used on a mammogram. This work classifies texture descriptor into nine groups where the extraction of features is classified using backpropagation learning with two types of multi-layer perceptron (MLP). The best texture descriptor as selected when the value of regression 1 appears in both the MLP-1 and the MLP-2 with the number of epoches less than 1000. The results of testing show that the best selected texture descriptor is the second order (combination) using all direction (0, 45, 90 and 135) that have twenty four descriptors.
Time-Domain Large Signal Investigation on Dynamic Responses of the GDCC Quarterly Wavelength Shifted Distributed Feedback Semiconductor Laser
Venue: arXiv cs.NI 2013
Year: 2013

A numerical investigation on the dynamic large-signal analysis using a time-domain traveling wave model of quarter wave-shifted distributed feedback semiconductor lasers diode with a Gaussian distribution of the coupling coefficient (GDCC) is presented. It is found that the single-mode behavior and the more hole-burning effect corrections of quarter wave-shifted distributed feedback laser with large coupling coefficient can be improved significantly by this new proposed light source.
Zero vs. epsilon Error in Interference Channels
Venue: arXiv cs.IT 2013
Year: 2013

Traditional studies of multi-source, multi-terminal interference channels typically allow a vanishing probability of error in communication. Motivated by the study of network coding, this work addresses the task of quantifying the loss in rate when insisting on zero error communication in the context of interference channels.
Characteristics of International versus Non-International Scientific Publication Media in Team- and Author-Based Data
Venue: arXiv cs.DL 2013
Year: 2013

The enlarged coverage of the international publication and citation databases Web of Science and Scopus towards local media in social sciences was a welcome response to an increased usage of these databases in evaluation and funding systems. The mostly international journals available earlier were the basis for the development of current standard bibliometric indicators. The same indicators may no longer measure exactly the same concepts when applied to newly introduced or extended media categories, with possibly different characteristics than those of international journals. This paper investigates differences between media with and without international dimension in publication data at team and author level. The findings relate the international publication categories to research quality, important for validation of their usage in evaluation or funding models that aim to stimulate quality.
A Deterministic Construction of Projection matrix for Adaptive Trajectory Compression
Venue: arXiv cs.IT 2013
Year: 2013

Compressive Sensing, which offers exact reconstruction of sparse signal from a small number of measurements, has tremendous potential for trajectory compression. In order to optimize the compression, trajectory compression algorithms need to adapt compression ratio subject to the compressibility of the trajectory. Intuitively, the trajectory of an object moving in starlight road is more compressible compared to the trajectory of a object moving in winding roads, therefore, higher compression is achievable in the former case compared to the later. We propose an in-situ compression technique underpinning the support vector regression theory, which accurately predicts the compressibility of a trajectory given the mean speed of the object and then apply compressive sensing to adapt the compression to the compressibility of the trajectory. The conventional encoding and decoding process of compressive sensing uses predefined dictionary and measurement (or projection) matrix pairs. However, the selection of an optimal pair is nontrivial and exhaustive, and random selection of a pair does not guarantee the best compression performance. In this paper, we propose a deterministic and data driven construction for the projection matrix which is obtained by applying singular value decomposition to a sparsifying dictionary learned from the dataset. We analyze case studies of pedestrian and animal trajectory datasets including GPS trajectory data from 127 subjects. The experimental results suggest that the proposed adaptive compression algorithm, incorporating the deterministic construction of projection matrix, offers significantly better compression performance compared to the state-of-the-art alternatives.
LogMin: A Model For Call Log Mining In Mobile Devices
Venue: arXiv cs.CY 2013
Year: 2013

In today's instant communication era, mobile phones play an important role in the efficient communication with respect to both individual and official communication strata. With the drastic explosion in the quantity of calls received and made, there is a need for analyses of patterns in these call logs to assist the user of the mobile device in the optimal utilization. This paper proposes a model termed "LogMin" (Log Mining of Calls in Mobile devices) which is aimed towards mining of call log in mobile phones to discover patterns and keep the user informed about the trends in the log. The logging of calls would facilitate the user to get an insight into patterns based on the six different parameters identified by the proposed LogMin model. The proposed model is validated with a prototype implementation in the Android platform and various experiments were conducted on it. The results of the experiments in the LogMin Android implementation validate the efficiency of the proposed model with respect to user's relevancy metric which is computed as 96.52%.
Infinite Mixtures of Multivariate Gaussian Processes
Venue: arXiv cs.LG 2013
Year: 2013

This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating the computationally cubic complexity of the multivariate Gaussian process. A Dirichlet process prior is adopted to allow the (possibly infinite) number of mixture components to be automatically inferred from training data, and Markov chain Monte Carlo sampling techniques are used for parameter and latent variable inference. Preliminary experimental results on multivariate regression show the feasibility of the proposed model.
A Comprehensive Evaluation of Machine Learning Techniques for Cancer Class Prediction Based on Microarray Data
Venue: arXiv cs.LG 2013
Year: 2013

Prostate cancer is among the most common cancer in males and its heterogeneity is well known. Its early detection helps making therapeutic decision. There is no standard technique or procedure yet which is full-proof in predicting cancer class. The genomic level changes can be detected in gene expression data and those changes may serve as standard model for any random cancer data for class prediction. Various techniques were implied on prostate cancer data set in order to accurately predict cancer class including machine learning techniques. Huge number of attributes and few number of sample in microarray data leads to poor machine learning, therefore the most challenging part is attribute reduction or non significant gene reduction. In this work we have compared several machine learning techniques for their accuracy in predicting the cancer class. Machine learning is effective when number of attributes (genes) are larger than the number of samples which is rarely possible with gene expression data. Attribute reduction or gene filtering is absolutely required in order to make the data more meaningful as most of the genes do not participate in tumor development and are irrelevant for cancer prediction. Here we have applied combination of statistical techniques such as inter-quartile range and t-test, which has been effective in filtering significant genes and minimizing noise from data. Further we have done a comprehensive evaluation of ten state-of-the-art machine learning techniques for their accuracy in class prediction of prostate cancer. Out of these techniques, Bayes Network out performed with an accuracy of 94.11% followed by Navie Bayes with an accuracy of 91.17%. To cross validate our results, we modified our training dataset in six different way and found that average sensitivity, specificity, precision and accuracy of Bayes Network is highest among all other techniques used.
REECH-ME: Regional Energy Efficient Cluster Heads based on Maximum Energy Routing Protocol for WSNs
Venue: arXiv cs.NI 2013
Year: 2013

In this paper, we propose Regional Energy Efficient Cluster Heads based on Maximum Energy (REECH-ME) Routing Protocol for Wireless Sensor Networks (WSNs) . The main purpose of this protocol is to improve the network lifetime and particularly the stability period of the network. In REECH-ME, the node with the maximum energy in a region becomes Cluster Head (CH) of that region for that particular round and the number of the cluster heads in each round remains the same. Our technique outperforms LEACH which uses probabilistic approach for the selection of CHs. We also implement the Uniform Random Distribution Model to find the packet drop to make this protocol more practical. We also calculate the confidence interval of all our results which helps us to visualize the possible deviation of our graphs from the mean value.
The Economic Trend of Video Game Industry
Venue: arXiv cs.CY 2013
Year: 2013

In recent years the game industry has had a huge growth. We've seen new game consoles, great looking games and an increase in the number of people playing them. We are presently in the seventh generation of video games which focuses on consoles released since 2004. For home consoles,the seventh generation began on November 22, 2005 with the release of Xbox 360 and continued with the release of PlayStation 3 on November 11, 2006, and Wii on November 19, 2006. The current generation is having a console battle between Nintendo's Wii, Microsoft's Xbox 360, and Sony's PlayStation 3.The appearance of the three new consoles not only offers various purchase choices, but also greatly affects economy and culture.
M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs
Venue: arXiv cs.NI 2013
Year: 2013

In this research work, we advise gateway based energy-efficient routing protocol (M-GEAR) for Wireless Sensor Networks (WSNs). We divide the sensor nodes into four logical regions on the basis of their location in the sensing field. We install Base Station (BS) out of the sensing area and a gateway node at the centre of the sensing area. If the distance of a sensor node from BS or gateway is less than predefined distance threshold, the node uses direct communication. We divide the rest of nodes into two equal regions whose distance is beyond the threshold distance. We select cluster heads (CHs)in each region which are independent of the other region. These CHs are selected on the basis of a probability. We compare performance of our protocol with LEACH (Low Energy Adaptive Clustering Hierarchy). Performance analysis and compared statistic results show that our proposed protocol perform well in terms of energy consumption and network lifetime.
Clustering Chinese Regional Cultures with Online-gaming Data
Venue: arXiv cs.CY 2013
Year: 2013

To identify cluster of societies is not easy in subject to the availability of data. In this study, from the prospective of computational social science, we propose a novel method to cluster Chinese regional cultures. Using millions of geotagged online-gaming data of Chinese internet users playing online card and board games with regional features, 336 Chinese cities are grouped into several main clusters. The geographic boundaries of clusters coincide with the boundaries of provincial regions. The north regions in China have more geographical proximity, when regional variations in south regions are more evident.