OpenRaiser/Intern-Atlas huggingface dataset:
Accurate Streaming Support Vector Machines
Venue: arXiv cs.LG 2014
Year: 2014

A widely-used tool for binary classification is the Support Vector Machine (SVM), a supervised learning technique that finds the "maximum margin" linear separator between the two classes. While SVMs have been well studied in the batch (offline) setting, there is considerably less work on the streaming (online) setting, which requires only a single pass over the data using sub-linear space. Existing streaming algorithms are not yet competitive with the batch implementation. In this paper, we use the formulation of the SVM as a minimum enclosing ball (MEB) problem to provide a streaming SVM algorithm based off of the blurred ball cover originally proposed by Agarwal and Sharathkumar. Our implementation consistently outperforms existing streaming SVM approaches and provides higher accuracies than libSVM on several datasets, thus making it competitive with the standard SVM batch implementation.
Framework for Wireless Network Security using Quantum Cryptography
Venue: arXiv cs.CR 2014
Year: 2014

Data that is transient over an unsecured wireless network is always susceptible to being intercepted by anyone within the range of the wireless signal. Hence providing secure communication to keep the user information and devices safe when connected wirelessly has become one of the major concerns. Quantum cryptography provides a solution towards absolute communication security over the network by encoding information as polarized photons, which can be sent through the air. This paper explores on the aspect of application of quantum cryptography in wireless networks. In this paper we present a methodology for integrating quantum cryptography and security of IEEE 802.11 wireless networks in terms of distribution of the encryption keys.
When Computer Vision Gazes at Cognition
Venue: arXiv cs.AI 2014
Year: 2014

Joint attention is a core, early-developing form of social interaction. It is based on our ability to discriminate the third party objects that other people are looking at. While it has been shown that people can accurately determine whether another person is looking directly at them versus away, little is known about human ability to discriminate a third person gaze directed towards objects that are further away, especially in unconstraint cases where the looker can move her head and eyes freely. In this paper we address this question by jointly exploring human psychophysics and a cognitively motivated computer vision model, which can detect the 3D direction of gaze from 2D face images. The synthesis of behavioral study and computer vision yields several interesting discoveries. (1) Human accuracy of discriminating targets 8°-10° of visual angle apart is around 40% in a free looking gaze task; (2) The ability to interpret gaze of different lookers vary dramatically; (3) This variance can be captured by the computational model; (4) Human outperforms the current model significantly. These results collectively show that the acuity of human joint attention is indeed highly impressive, given the computational challenge of the natural looking task. Moreover, the gap between human and model performance, as well as the variability of gaze interpretation across different lookers, require further understanding of the underlying mechanisms utilized by humans for this challenging task.
A New Approach of Learning Hierarchy Construction Based on Fuzzy Logic
Venue: arXiv cs.CY 2014
Year: 2014

In recent years, adaptive learning systems rely increasingly on learning hierarchy to customize the educational logic developed in their courses. Most approaches do not consider that the relationships of prerequisites between the skills are fuzzy relationships. In this article, we describe a new approach of a practical application of fuzzy logic techniques to the construction of learning hierarchies. For this, we use a learning hierarchy predefined by one or more experts of a specific field. However, the relationships of prerequisites between the skills in the learning hierarchy are not definitive and they are fuzzy relationships. Indeed, we measure relevance degree of all relationships existing in this learning hierarchy and we try to answer to the following question: Is the relationships of prerequisites predefined in initial learning hierarchy are correctly established or not?
Image quality assessment measure based on natural image statistics in the Tetrolet domain
Venue: arXiv cs.CV 2014
Year: 2014

This paper deals with a reduced reference (RR) image quality measure based on natural image statistics modeling. For this purpose, Tetrolet transform is used since it provides a convenient way to capture local geometric structures. This transform is applied to both reference and distorted images. Then, Gaussian Scale Mixture (GSM) is proposed to model subbands in order to take account statistical dependencies between tetrolet coefficients. In order to quantify the visual degradation, a measure based on Kullback Leibler Divergence (KLD) is provided. The proposed measure was tested on the Cornell VCL A-57 dataset and compared with other measures according to FR-TV1 VQEG framework.
Cooperative Change Detection for Online Power Quality Monitoring
Venue: arXiv cs.SY 2014
Year: 2014

This paper considers the real-time power quality monitoring in power grid systems. The goal is to detect the occurrence of disturbances in the nominal sinusoidal voltage/current signal as quickly as possible such that protection measures can be taken in time. Based on an autoregressive (AR) model for the disturbance, we propose a generalized local likelihood ratio (GLLR) detector which processes meter readings sequentially and alarms as soon as the test statistic exceeds a prescribed threshold. The proposed detector not only reacts to a wide range of disturbances, but also achieves lower detection delay compared to the conventional block processing method. Then we further propose to deploy multiple meters to monitor the power signal cooperatively. The distributed meters communicate wirelessly to a central meter, where the data fusion and detection are performed. In light of the limited bandwidth of wireless channels, we develop a level-triggered sampling scheme, where each meter transmits only one-bit each time asynchronously. The proposed multi-meter scheme features substantially low communication overhead, while its performance is close to that of the ideal case where distributed meter readings are perfectly available at the central meter.
A Robust and Scalable Algorithm for the Steiner Problem in Graphs
Venue: arXiv cs.DS 2014
Year: 2014

We present an effective heuristic for the Steiner Problem in Graphs. Its main elements are a multistart algorithm coupled with aggressive combination of elite solutions, both leveraging recently-proposed fast local searches. We also propose a fast implementation of a well-known dual ascent algorithm that not only makes our heuristics more robust (by quickly dealing with easier cases), but can also be used as a building block of an exact (branch-and-bound) algorithm that is quite effective for some inputs. On all graph classes we consider, our heuristic is competitive with (and sometimes more effective than) any previous approach with similar running times. It is also scalable: with long runs, we could improve or match the best published results for most open instances in the literature.
Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and $L_1$ Regularization
Venue: arXiv cs.CV 2014
Year: 2014

This paper deals with the multiple annotation problem in medical application of cancer detection in digital images. The main assumption is that though images are labeled by many experts, the number of images read by the same expert is not large. Thus differing with the existing work on modeling each expert and ground truth simultaneously, the multi annotation information is used in a soft manner. The multiple labels from different experts are used to estimate the probability of the findings to be marked as malignant. The learning algorithm minimizes the Kullback Leibler (KL) divergence between the modeled probabilities and desired ones constraining the model to be compact. The probabilities are modeled by logit regression and multiple instance learning concept is used by us. Experiments on a real-life computer aided diagnosis (CAD) problem for CXR CAD lung cancer detection demonstrate that the proposed algorithm leads to similar results as learning with a binary RVMMIL classifier or a mixture of binary RVMMIL models per annotator. However, this model achieves a smaller complexity and is more preferable in practice.
Autonomous Load Disaggregation Approach based on Active Power Measurements
Venue: arXiv cs.OH 2014
Year: 2014

With the help of smart metering valuable information of the appliance usage can be retrieved. In detail, non-intrusive load monitoring (NILM), also called load disaggregation, tries to identify appliances in the power draw of an household. In this paper an unsupervised load disaggregation approach is proposed that works without a priori knowledge about appliances. The proposed algorithm works autonomously in real time. The number of used appliances and the corresponding appliance models are learned in operation and are progressively updated. The proposed algorithm is considering each useful and suitable detected power state. The algorithm tries to detect power states corresponding to on/off appliances as well as to multi-state appliances based on active power measurements in 1s resolution. We evaluated the novel introduced load disaggregation approach on real world data by testing the possibility to disaggregate energy demand on appliance level.
Trading Wireless Information and Power Transfer: Relay Selection to Minimize the Outage Probability
Venue: arXiv cs.IT 2014
Year: 2014

This paper studies the outage probability minimization problem for a multiple relay network with energy harvesting constraints. The relays are hybrid nodes used for simultaneous wireless information and power transfer from the source radio frequency (RF) signals. There is a trade-off associated with the amount of time a relay node is used for energy and information transfer. Large intervals of information transfer implies little time for energy harvesting from RF signals and thus, high probability of outage events. We propose relay selection schemes for a cooperative system with a fixed number of RF powered relays. We address both causal and non-causal channel state information cases at the relay--destination link and evaluate the trade-off associated with information/power transfer in the context of minimization of outage probability.
A Cryptographic Mutual Authentication Scheme for Web Applications
Venue: arXiv cs.CR 2014
Year: 2014

The majority of current web authentication is built on username/password. Unfortunately, password replacement offers more security, but it is difficult to use and expensive to deploy. In this paper, we propose a new mutual authentication scheme called StrongAuth which preserves most password authentication advantages and simultaneously improves security using cryptographic primitives. Our scheme not only offers webmasters a clear framework which to build secure user authentication, but it also provides almost the same conventional user experience. Security analysis shows that the proposed scheme fulfills the required user authentication security benefits, and can resist various possible attacks.
A Review of Software Quality Models for the Evaluation of Software Products
Venue: arXiv cs.SE 2014
Year: 2014

Actually, software products are increasing in a fast way and are used in almost all activities of human life. Consequently measuring and evaluating the quality of a software product has become a critical task for many companies. Several models have been proposed to help diverse types of users with quality issues. The development of techniques for building software has influenced the creation of models to assess the quality. Since 2000 the construction of software started to depend on generated or manufactured components and gave rise to new challenges for assessing quality. These components introduce new concepts such as configurability, reusability, availability, better quality and lower cost. Consequently the models are classified in basic models which were developed until 2000, and those based on components called tailored quality models. The purpose of this article is to describe the main models with their strengths and point out some deficiencies. In this work, we conclude that in the present age, aspects of communications play an important factor in the quality of software
Analysis of Average Travel Time for Stateless Opportunistic Routing Techniques
Venue: arXiv cs.NI 2014
Year: 2014

Wireless network applications, such as, searching, routing, self stabilization and query processing can be modeled as random walks on graphs. Stateless Opportunistic routing technique is a robust distributed routing technique based on random walk approach, where nodes transfer the packets to one of their direct neighbors uniformly, until the packets reach their destinations. Simplicity in execution, fault tolerance, low overhead and robustness to topology changes made it more suitable to wireless sensor networks scenarios. But the main disadvantage of stateless opportunistic routing is estimating and studying the effect of network parameters on the packet latency. In this work, we derived the analytical expressions for mean latency or average packet travel time for r-nearest neighbor cycle, r-nearest neighbor torus networks. Further, we derived the generalized expression for mean latency for m-dimensional r- nearest neighbor torus networks and studied the effect of number of nodes, nearest neighbors and network dimension on average packet travel time.
Sequential Hypothesis Tests for Adaptive Locality Sensitive Hashing
Venue: arXiv cs.IR 2014
Year: 2014

All pairs similarity search is a problem where a set of data objects is given and the task is to find all pairs of objects that have similarity above a certain threshold for a given similarity measure-of-interest. When the number of points or dimensionality is high, standard solutions fail to scale gracefully. Approximate solutions such as Locality Sensitive Hashing (LSH) and its Bayesian variants (BayesLSH and BayesLSHLite) alleviate the problem to some extent and provides substantial speedup over traditional index based approaches. BayesLSH is used for pruning the candidate space and computation of approximate similarity, whereas BayesLSHLite can only prune the candidates, but similarity needs to be computed exactly on the original data. Thus where ever the explicit data representation is available and exact similarity computation is not too expensive, BayesLSHLite can be used to aggressively prune candidates and provide substantial speedup without losing too much on quality. However, the loss in quality is higher in the BayesLSH variant, where explicit data representation is not available, rather only a hash sketch is available and similarity has to be estimated approximately. In this work we revisit the LSH problem from a Frequentist setting and formulate sequential tests for composite hypothesis (similarity greater than or less than threshold) that can be leveraged by such LSH algorithms for adaptively pruning candidates aggressively. We propose a vanilla sequential probability ration test (SPRT) approach based on this idea and two novel variants. We extend these variants to the case where approximate similarity needs to be computed using fixed-width sequential confidence interval generation technique.
Distributed Protocols and Heterogeneous Trust: Technical Report
Venue: arXiv cs.DC 2014
Year: 2014

The robustness of distributed systems is usually phrased in terms of the number of failures of certain types that they can withstand. However, these failure models are too crude to describe the different kinds of trust and expectations of participants in the modern world of complex, integrated systems extending across different owners, networks, and administrative domains. Modern systems often exist in an environment of heterogeneous trust, in which different participants may have different opinions about the trustworthiness of other nodes, and a single participant may consider other nodes to differ in their trustworthiness. We explore how to construct distributed protocols that meet the requirements of all participants, even in heterogeneous trust environments. The key to our approach is using lattice-based information flow to analyse and prove protocol properties. To demonstrate this approach, we show how two earlier distributed algorithms can be generalized to work in the presence of heterogeneous trust: first, Heterogeneous Fast Consensus, an adaptation of the earlier Bosco Fast Consensus protocol; and second, Nysiad, an algorithm for converting crash-tolerant protocols to be Byzantine-tolerant. Through simulations, we show that customizing a protocol to a heterogeneous trust configuration yields performance improvements over the conventional protocol designed for homogeneous trust.
Millimeter-wave Evolution for 5G Cellular Networks
Venue: arXiv cs.NI 2014
Year: 2014

Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mm-wave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5 GHz band. Furthermore, the paper also gives latest status of mm-wave devices and regulations to show the feasibility of using mm-wave in the 5G systems.
The rank invariant stability via interleavings
Venue: arXiv cs.CG 2014
Year: 2014

A lower bound for the interleaving distance on persistence vector spaces is given in terms of rank invariants. This offers an alternative proof of the stability of rank invariants.
Class-Based Service Connectivity using Multi-Level Bandwidth Adaptation in Multimedia Wireless Networks
Venue: arXiv cs.NI 2014
Year: 2014

Due to the fact that quality of service requirements are not very strict for all traffic types, more calls of higher priority can be accommodated by reducing some bandwidth allocation for the bandwidth adaptive calls. The bandwidth adaptation to accept a higher priority call is more than that of a lower priority call. Therefore, the multi-level bandwidth adaptation technique improves the overall forced call termination probability as well as provides priority of the traffic classes in terms of call blocking probability without reducing the bandwidth utilization. We propose a novel bandwidth adaptation model that releases multi-level of bandwidth from the existing multimedia traffic calls. The amount of released bandwidth is decided based on the priority of the requesting traffic calls and the number of existing bandwidth adaptive calls. This prioritization of traffic classes does not reduce the bandwidth utilization. Moreover, our scheme reduces the overall forced call termination probability significantly. The proposed scheme is modeled using the Markov Chain. The numerical results show that the proposed scheme is able to provide negligible handover call dropping probability as well as significantly reduced new call blocking probability of higher priority calls without increasing the overall forced call termination probability.
Entropy and long-range correlations in random symbolic sequences
Venue: arXiv cs.IT 2014
Year: 2014

The goal of this paper is to develop an estimate for the entropy of random long-range correlated symbolic sequences with elements belonging to a finite alphabet. As a plausible model, we use the high-order additive stationary ergodic Markov chain. Supposing that the correlations between random elements of the chain are weak we express the differential entropy of the sequence by means of the symbolic pair correlation function. We also examine an algorithm for estimating the differential entropy of finite symbolic sequences. We show that the entropy contains two contributions, the correlation and fluctuation ones. The obtained analytical results are used for numerical evaluation of the entropy of written English texts and DNA nucleotide sequences. The developed theory opens the way for constructing a more consistent and sophisticated approach to describe the systems with strong short- and weak long-range correlations.
Evaluating arbitration and conflict resolution mechanisms in the Spanish Wikipedia
Venue: arXiv cs.CY 2014
Year: 2014

In open collaborative projects like Wikipedia, interactions among users can produce tension and misunderstandings. Complex disputes require more sophisticated mechanisms of conflict resolution. In this paper, we examine the case of the Spanish Wikipedia and its Arbitration Committee, known as CRC, over its two years of activity. We postulate that the high percentage of rejections of cases presented by non-administrators, the lack of diversity inside the committee (composed only by administrators), and the high number of cases involving administrators played a central role in its eventual downfall. We conclude that mechanisms that fail to acknowledge the ecosystem they are part of cannot succceed. Therefore, further research is needed to determine if granting more decision-making power to non-administrators may lead to more effective conflict resolution mechanisms.