OpenRaiser/Intern-Atlas huggingface dataset:
A Cross-layer Contention Based Synchronous MAC Protocol for Transmission Delay Reduction in Multi-Hop WSNs
Venue: arXiv cs.NI 2016
Year: 2016

Recently designed cross-layer contention based synchronous MAC protocols like the PRMAC protocol, for wireless sensor networks (WSNs) enable a node to schedule multi-hop transmission of multiple data packets in a cycle. However, these systems accommodate both the request-to-send data process and the confirmation-to-send data process in the same data transmission scheduling window (i.e. data window). This reduces the length of the multi-hop flow setup in the data window. In a multi-hop scenario, this degrades both the packet delivery ratio (PDR) and the end-to-end transmission delay (E2ETD). In this paper, we propose a cross-layer contention based synchronous MAC protocol, which accommodates the request-to-send data process in the data window and the confirmation-to-send data process in the sleep window for increased efficiency. We evaluate our proposed protocol through ns-2.35 simulations and compare its performance with the PRMAC protocol. Results suggest that in multi-hop scenario, proposed protocol outperforms PRMAC both in terms of the E2ETD and the packet delivery ratio (PDR).
Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity
Venue: arXiv cs.CV 2016
Year: 2016

It is an ill-posed problem to recover the true scene colors from a color biased image by discounting the effects of scene illuminant and camera spectral sensitivity (CSS) at the same time. Most color constancy (CC) models have been designed to first estimate the illuminant color, which is then removed from the color biased image to obtain an image taken under white light, without the explicit consideration of CSS effect on CC. This paper first studies the CSS effect on illuminant estimation arising in the inter-dataset-based CC (inter-CC), i.e., training a CC model on one dataset and then testing on another dataset captured by a distinct CSS. We show the clear degradation of existing CC models for inter-CC application. Then a simple way is proposed to overcome such degradation by first learning quickly a transform matrix between the two distinct CSSs (CSS-1 and CSS-2). The learned matrix is then used to convert the data (including the illuminant ground truth and the color biased images) rendered under CSS-1 into CSS-2, and then train and apply the CC model on the color biased images under CSS-2, without the need of burdensome acquiring of training set under CSS-2. Extensive experiments on synthetic and real images show that our method can clearly improve the inter-CC performance for traditional CC algorithms. We suggest that by taking the CSS effect into account, it is more likely to obtain the truly color constant images invariant to the changes of both illuminant and camera sensors.
Compact Layered Drawings of General Directed Graphs
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of layering general directed graphs under height and possibly also width constraints. Given a directed graph G = (V,A) and a maximal height, we propose a layering approach that minimizes a weighted sum of the number of reversed arcs, the arc lengths, and the width of the drawing. We call this the Compact Generalized Layering Problem (CGLP). Here, the width of a drawing is defined as the maximum sum of the number of vertices placed on a layer and the number of dummy vertices caused by arcs traversing the layer. The CGLP is NP-hard. We present two MIP models for this problem. The first one (EXT) is our extension of a natural formulation for directed acyclic graphs as suggested by Healy and Nikolov. The second one (CGL) is a new formulation based on partial orderings. Our computational experiments on two benchmark sets show that the CGL formulation can be solved much faster than EXT using standard commercial MIP solvers. Moreover, we suggest a variant of CGL, called MML, that can be seen as a heuristic approach. In our experiments, MML clearly improves on CGL in terms of running time while it does not considerably increase the average arc lengths and widths of the layouts although it solves a slightly different problem where the dummy vertices are not taken into account.
Tuning for Software Analytics: is it Really Necessary?
Venue: arXiv cs.SE 2016
Year: 2016

Context: Data miners have been widely used in software engineering to, say, generate defect predictors from static code measures. Such static code defect predictors perform well compared to manual methods, and they are easy to use and useful to use. But one of the "black art" of data mining is setting the tunings that control the miner. Objective:We seek simple, automatic, and very effective method for finding those tunings. Method: For each experiment with different data sets (from open source JAVA systems), we ran differential evolution as anoptimizer to explore the tuning space (as a first step) then tested the tunings using hold-out data. Results: Contrary to our prior expectations, we found these tunings were remarkably simple: it only required tens, not thousands,of attempts to obtain very good results. For example, when learning software defect predictors, this method can quickly find tuningsthat alter detection precision from 0% to 60%. Conclusion: Since (1) the improvements are so large, and (2) the tuning is so simple, we need to change standard methods insoftware analytics. At least for defect prediction, it is no longer enough to just run a data miner and present the resultwithoutconducting a tuning optimization study. The implication for other kinds of analytics is now an open and pressing issue
Bring-Your-Own-Device (BYOD): An Evaluation of Associated Risks to Corporate Information Security
Venue: arXiv cs.CR 2016
Year: 2016

This study evaluates the cyber-risks to Business Information Assets posed by the adoption of Bring-Your-Own-Device (BYOD) to the workplace. BYOD is an emerging trend where employees bring and use personal computing devices on the companys network to access applications and sensitive data like emails, calendar and scheduling applications, documents, etc. Employees are captivated by BYOD because they can have access to private items as well as perform certain job functions while being unrestricted to their desks. This is however usually done on the blind side of management or the system administrator; a situation that tends to expose vital and sensitive corporate information to various threats like unwanted network traffic, unknown applications, malwares, and viruses. Expert opinions were elicited in this exploratory study. The study evaluated the characteristics of BYOD, assessed associated risks, threats and vulnerabilities. The findings indicate that little or no security measures were instituted to mitigate risks associated with BYOD. Though, profound benefits abound with BYOD adoption, they could be eroded by security threats and costs of mitigation in curing breaches. The most significant risk was found to be Data Loss which was in consonance with similar studies on Smartphone security risks. Some mitigation measures are then recommended.
Wikipedia mining of hidden links between political leaders
Venue: arXiv cs.SI 2016
Year: 2016

We describe a new method of reduced Google matrix which allows to establish direct and hidden links between a subset of nodes of a large directed network. This approach uses parallels with quantum scattering theory, developed for processes in nuclear and mesoscopic physics and quantum chaos. The method is applied to the Wikipedia networks in different language editions analyzing several groups of political leaders of USA, UK, Germany, France, Russia and G20. We demonstrate that this approach allows to recover reliably direct and hidden links among political leaders. We argue that the reduced Google matrix method can form the mathematical basis for studies in social and political sciences analyzing Leader-Members eXchange (LMX).
Auction-Based Coopetition between LTE Unlicensed and Wi-Fi
Venue: arXiv cs.GT 2016
Year: 2016

Motivated by the recent efforts in extending LTE to the unlicensed spectrum, we propose a novel spectrum sharing framework for the coopetition (i.e., cooperation and competition) between LTE and Wi-Fi in the unlicensed band. Basically, the LTE network can choose to work in one of the two modes: in the competition mode, it randomly accesses an unlicensed channel, and interferes with the Wi-Fi access point using the same channel; in the cooperation mode, it delivers traffic for the Wi-Fi users in exchange for the exclusive access of the corresponding channel. Because the LTE network works in an interference-free manner in the cooperation mode, it can achieve a much larger data rate than that in the competition mode, which allows it to effectively serve both its own users and the Wi-Fi users. We design a second-price reverse auction mechanism, which enables the LTE provider and the Wi-Fi access point owners (APOs) to effectively negotiate the operation mode. Specifically, the LTE provider is the auctioneer (buyer), and the APOs are the bidders (sellers) who compete to sell their channel access opportunities to the LTE provider. In Stage I of the auction, the LTE provider announces a reserve rate. In Stage II of the auction, the APOs submit their bids. We show that the auction involves allocative externalities, i.e., the cooperation between the LTE provider and one APO benefits other APOs who are not directly involved in this cooperation. As a result, a particular APO's willingness to cooperate is affected by its belief about other APOs' willingness to cooperate. This makes our analysis much more challenging than that of the conventional second-price auction, where bidding truthfully is a weakly dominant strategy. We show that the APOs have a unique form of the equilibrium bidding strategies in Stage II, based on which we analyze the LTE provider's optimal reserve rate in Stage I.
Uniform Transformation of Non-Separable Probability Distributions
Venue: arXiv cs.NE 2016
Year: 2016

A theoretical framework is developed to describe the transformation that distributes probability density functions uniformly over space. In one dimension, the cumulative distribution can be used, but does not generalize to higher dimensions, or non-separable distributions. A potential function is shown to link probability density functions to their transformation, and to generalize the cumulative. A numerical method is developed to compute the potential, and examples are shown in two dimensions.
(Lack Of) Representation of Non Western World in process of creation of Web standards
Venue: arXiv cs.CY 2016
Year: 2016

World Wide Consortium (W3C) as an standard setting organization for the world wide web plays a very important role in shaping the web. We focus on the ongoing controversy related to Encrypted Media Extensions (EME) and found that there was a serious lack of participation from people from non western countries. We also found serious lack of gender diversity in the EME debate.
Analytical Estimation in Differential Optical Transmission Systems Influenced by Equalization Enhanced Phase Noise
Venue: arXiv cs.IT 2016
Year: 2016

An analytical model is presented for assessing the performance of the bit-error-rate (BER) in the differential m-level phase shift keying (m-PSK) transmission systems, where the influence of equalization enhanced phase noise (EEPN) has been considered. Theoretical analysis has been carried out in differential quadrature phase shift keying (DQPSK), differential 8-PSK (D8PSK), and differential 16-PSK (D16PSK) coherent optical transmission systems. The influence of EEPN on the BER performance, in term of signal-to-noise ratio (SNR), are investigated for different fiber dispersion, LO laser linewidths, symbol rates, and modulation formats. Our analytical model achieves a good agreement with previously reported EEPN induced BER floors, and can give an accurate prediction for the DQPSK system, and a leading-order approximation for the D8PSK and the D16PSK systems.
Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing
Venue: arXiv cs.DC 2016
Year: 2016

Traditional dehazing techniques, as a well studied topic in image processing, are now widely used to eliminate the haze effects from individual images. However, even the state-of-the-art dehazing algorithms may not provide sufficient support to video analytics, as a crucial pre-processing step for video-based decision making systems (e.g., robot navigation), due to the limitations of these algorithms on poor result coherence and low processing efficiency. This paper presents a new framework, particularly designed for video dehazing, to output coherent results in real time, with two novel techniques. Firstly, we decompose the dehazing algorithms into three generic components, namely transmission map estimator, atmospheric light estimator and haze-free image generator. They can be simultaneously processed by multiple threads in the distributed system, such that the processing efficiency is optimized by automatic CPU resource allocation based on the workloads. Secondly, a cross-frame normalization scheme is proposed to enhance the coherence among consecutive frames, by sharing the parameters of atmospheric light from consecutive frames in the distributed computation platform. The combination of these techniques enables our framework to generate highly consistent and accurate dehazing results in real-time, by using only 3 PCs connected by Ethernet.
Feasibility of Post-Editing Speech Transcriptions with a Mismatched Crowd
Venue: arXiv cs.AI 2016
Year: 2016

Manual correction of speech transcription can involve a selection from plausible transcriptions. Recent work has shown the feasibility of employing a mismatched crowd for speech transcription. However, it is yet to be established whether a mismatched worker has sufficiently fine-granular speech perception to choose among the phonetically proximate options that are likely to be generated from the trellis of an ASRU. Hence, we consider five languages, Arabic, German, Hindi, Russian and Spanish. For each we generate synthetic, phonetically proximate, options which emulate post-editing scenarios of varying difficulty. We consistently observe non-trivial crowd ability to choose among fine-granular options.
Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

Biological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on recent insights in neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on adaptive spiking neurons. These spiking neurons efficiently encode information in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while homeostatically optimizing their firing rate. In the proposed paradigm of spiking neuron computation, neural adaptation is tightly coupled to synaptic plasticity, to ensure that downstream neurons can correctly decode upstream spiking neurons. We show that this type of network is inherently able to carry out asynchronous and event-driven neural computation, while performing identical to corresponding artificial neural networks (ANNs). In particular, we show that these adaptive spiking neurons can be drop in replacements for ReLU neurons in standard feedforward ANNs comprised of such units. We demonstrate that this can also be successfully applied to a ReLU based deep convolutional neural network for classifying the MNIST dataset. The ASNN thus outperforms current Spiking Neural Networks (SNNs) implementations, while responding (up to) an order of magnitude faster and using an order of magnitude fewer spikes. Additionally, in a streaming setting where frames are continuously classified, we show that the ASNN requires substantially fewer network updates as compared to the corresponding ANN.
Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks
Venue: arXiv cs.SY 2016
Year: 2016

This paper considers the distributed sampled-data control problem of a group of mobile robots connected via distance-induced proximity networks. A dwell time is assumed in order to avoid chattering in the neighbor relations that may be caused by abrupt changes of positions when updating information from neighbors. Distributed sampled-data control laws are designed based on nearest neighbour rules, which in conjunction with continuous-time dynamics results in hybrid closed-loop systems. For uniformly and independently initial states, a sufficient condition is provided to guarantee synchronization for the system without leaders. In order to steer all robots to move with the desired orientation and speed, we then introduce a number of leaders into the system, and quantitatively establish the proportion of leaders needed to track either constant or time-varying signals. All these conditions depend only on the neighborhood radius, the maximum initial moving speed and the dwell time, without assuming a prior properties of the neighbor graphs as are used in most of the existing literature.
Latent Dependency Forest Models
Venue: arXiv cs.AI 2016
Year: 2016

Probabilistic modeling is one of the foundations of modern machine learning and artificial intelligence. In this paper, we propose a novel type of probabilistic models named latent dependency forest models (LDFMs). A LDFM models the dependencies between random variables with a forest structure that can change dynamically based on the variable values. It is therefore capable of modeling context-specific independence. We parameterize a LDFM using a first-order non-projective dependency grammar. Learning LDFMs from data can be formulated purely as a parameter learning problem, and hence the difficult problem of model structure learning is circumvented. Our experimental results show that LDFMs are competitive with existing probabilistic models.
Ms. Pac-Man Versus Ghost Team CIG 2016 Competition
Venue: arXiv cs.AI 2016
Year: 2016

This paper introduces the revival of the popular Ms. Pac-Man Versus Ghost Team competition. We present an updated game engine with Partial Observability constraints, a new Multi-Agent Systems approach to developing Ghost agents and several sample controllers to ease the development of entries. A restricted communication protocol is provided for the Ghosts, providing a more challenging environment than before. The competition will debut at the IEEE Computational Intelligence and Games Conference 2016. Some preliminary results showing the effects of Partial Observability and the benefits of simple communication are also presented.
Distributed agent-based automated theorem proving in order-sorted first-order logic
Venue: arXiv cs.LO 2016
Year: 2016

This paper presents a distributed agent-based automated theorem proving framework based on order-sorted first-order logic. Each agent in our framework has its own knowledge base, communicating to its neighboring agent(s) using message-passing algorithms. The communication language between agents is restricted in such a manner that each agent can only communicate to its neighboring agent(s) by means of their common language. In this paper we provide a refutation-complete report procedure for automated theorem proving in order-sorted first-order logic in a subclass of distributed agent-based networks. Rather than studying and evaluating the performance improvement of the automated theorem proving in order-sorted first-order logic using parallel or distributed agents, this paper focuses on building proofs in order-sorted first-order logic in a distributed manner under the restriction that agents may report their knowledge or observations only with their predefined language.
A short review and primer on online processing of multiple signal sources in human computer interaction applications
Venue: arXiv cs.HC 2016
Year: 2016

The application of psychophysiological in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques. Stream processing sytems (SPS) are emerging computational platforms that can be utilized in human-computer interaction for real-time analysis of high-volume multimodal signals. Usage of complementary information contained in multiple signals is desireable as it can make HCI systems more robust. In this preprint we review existing software and hardware solutions for HCI-centric stream processing systems. The preprint also includes a brief introduction into SPS design considerations and structure from the perspective of analysing physiological signals. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We put special emphasis on everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, including 12 similar chapters, plus application guidelines and systematic review. Thus any citation should be made using the following reference: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction. Foundations and Trends in Human-Computer Interaction, vol. 9, no. 3-4, pp. 150--307, 2016.
Improved Optimistic Mirror Descent for Sparsity and Curvature
Venue: arXiv cs.LG 2016
Year: 2016

Online Convex Optimization plays a key role in large scale machine learning. Early approaches to this problem were conservative, in which the main focus was protection against the worst case scenario. But recently several algorithms have been developed for tightening the regret bounds in easy data instances such as sparsity, predictable sequences, and curved losses. In this work we unify some of these existing techniques to obtain new update rules for the cases when these easy instances occur together. First we analyse an adaptive and optimistic update rule which achieves tighter regret bound when the loss sequence is sparse and predictable. Then we explain an update rule that dynamically adapts to the curvature of the loss function and utilizes the predictable nature of the loss sequence as well. Finally we extend these results to composite losses.
Families of Optimal Binary Non-MDS Erasure Codes
Venue: arXiv cs.IT 2016
Year: 2016

We introduce a definition for \emph{Families of Optimal Binary Non-MDS Erasure Codes} for $[n, k]$ codes over $GF(2)$, and propose an algorithm for finding those families by using hill climbing techniques over Balanced XOR codes. Due to the hill climbing search, those families of codes have always better decoding probability than the codes generated in a typical Random Linear Network Coding scenario, i.e., random linear codes. We also show a surprising result that for small values of $k$, the decoding probability of our codes in $GF(2)$ is very close to the decoding probability of the codes obtained by Random Linear Network Coding but in the higher finite field $GF(4)$.