OpenRaiser/Intern-Atlas huggingface dataset:
LSA-Advanced and C-RAN: A (5G) Romance of Many Dimensions
Venue: arXiv cs.CY 2016
Year: 2016

We examine the dimensional limitations of Licensed Shared Access (LSA) regulatory framework for spectrum sharing and propose a solution for its more dynamic implementation. We consider an additional dimension for sharing, beyond those of time, space and frequency, i.e. the sharing of the infrastructure. We explain why we believe that the emerging regulations and use-case scenarios are limiting the potential of LSA, and with this in mind make a set of recommendations to unlock it fully. As a specific case study, we present our architectural work around Cloud-based Radio Access Network (C-RAN) and LSA to demonstrate the possibility of a dynamic implementation of multiple incumbents and multiple LSA licensees engaged in sharing. Our case study highlights multiple ways for LSA framework to evolve into a global and flexible sharing model, and prove that LSA and C-RAN are a good match to rethink how we look at networks targeting a more flexible, efficient and profitable way of operating them.
Applying Gaussian distributed constraints to Gaussian distributed variables
Venue: arXiv cs.SY 2016
Year: 2016

This paper develops an analytical method of truncating inequality constrained Gaussian distributed variables where the constraints are themselves described by Gaussian distributions. Existing truncation methods either assume hard constraints, or use numerical methods to handle uncertain constraints. The proposed approach introduces moment-based Gaussian approximations of the truncated distribution. This method can be applied to numerous problems, with the motivating problem being Kalman filtering with uncertain constraints. In a simulation example, the developed method is shown to outperform unconstrained Kalman filtering by over 40% and hard-constrained Kalman filtering by over 17%.
Systematic evaluation of CNN advances on the ImageNet
Venue: arXiv cs.NE 2016
Year: 2016

The paper systematically studies the impact of a range of recent advances in CNN architectures and learning methods on the object categorization (ILSVRC) problem. The evalution tests the influence of the following choices of the architecture: non-linearity (ReLU, ELU, maxout, compatibility with batch normalization), pooling variants (stochastic, max, average, mixed), network width, classifier design (convolutional, fully-connected, SPP), image pre-processing, and of learning parameters: learning rate, batch size, cleanliness of the data, etc. The performance gains of the proposed modifications are first tested individually and then in combination. The sum of individual gains is bigger than the observed improvement when all modifications are introduced, but the "deficit" is small suggesting independence of their benefits. We show that the use of 128x128 pixel images is sufficient to make qualitative conclusions about optimal network structure that hold for the full size Caffe and VGG nets. The results are obtained an order of magnitude faster than with the standard 224 pixel images.
Anarchy in Tor: Performance Cost of Decentralization
Venue: arXiv cs.CR 2016
Year: 2016

Like many routing protocols, the Tor anonymity network has decentralized path selection, in clients locally and independently choose paths. As a result, network resources may be left idle, leaving the system in a suboptimal state. This is referred to as the price of anarchy, where agents acting in their own self interest can make poor decisions when viewed in a global context. In this paper we explore the cost of anarchy in Tor by examining the potential performance increases that can be gained by centrally optimizing circuit and relay selection using global knowledge. In experiments with both offline and online algorithms, we show that centrally coordinated clients can achieve up to 75% higher bandwidth compared to traditional Tor. Drawing on these findings, we design and evaluate a decentralized version of our online algorithm, in which relays locally distribute information enabling clients to make smarter decisions locally and perform downloads 10-60% faster. Finally, we perform a privacy analysis of the decentralized algorithm against a passive and active adversary trying to reduce anonymity of clients and increase their view of the Tor network. We conclude that this decentralized algorithm does not enable new attacks, while providing significantly higher performance.
Optimal Motion of Flexible Objects with Oscillations Elimination at the Final Point
Venue: arXiv cs.RO 2016
Year: 2016

In this article, a theoretical justification of one type of skew-symmetric optimal translational motion (moving in the minimal acceptable time) of a flexible object carried by a robot from its initial to its final position of absolute quiescence with the exception of the oscillations at the end of the motion is presented. The Hamilton-Ostrogradsky principle is used as a criterion for searching an optimal control. The data of experimental verification of the control are presented using the Orthoglide robot for translational motions and several masses were attached to a flexible beam.
On the Benefit of Merging Suffix Array Intervals for Parallel Pattern Matching
Venue: arXiv cs.DS 2016
Year: 2016

We present parallel algorithms for exact and approximate pattern matching with suffix arrays, using a CREW-PRAM with $p$ processors. Given a static text of length $n$, we first show how to compute the suffix array interval of a given pattern of length $m$ in $O(\frac{m}{p}+ \lg p + \lg\lg p\cdot\lg\lg n)$ time for $p \le m$. For approximate pattern matching with $k$ differences or mismatches, we show how to compute all occurrences of a given pattern in $O(\frac{m^kσ^k}{p}\max\left(k,\lg\lg n\right)\!+\!(1+\frac{m}{p}) \lg p\cdot \lg\lg n + \text{occ})$ time, where $σ$ is the size of the alphabet and $p \le σ^k m^k$. The workhorse of our algorithms is a data structure for merging suffix array intervals quickly: Given the suffix array intervals for two patterns $P$ and $P'$, we present a data structure for computing the interval of $PP'$ in $O(\lg\lg n)$ sequential time, or in $O(1+\lg_p\lg n)$ parallel time. All our data structures are of size $O(n)$ bits (in addition to the suffix array).
Exploring the Use of Enterprise 2.0 and Its Impact on Social Capital within a Large Organisation
Venue: arXiv cs.CY 2016
Year: 2016

Despite the rampant adoption of Enterprise 2.0, there is lack of empirical evidence of how Enterprise 2.0 is aptly supporting the business objectives. Social capital theory will be used as a theoretical lens to understand the impact and implications of individual use of Enterprise 2.0. To ascertain the impact from the use of Enterprise 2.0 on the various dimensions of social capital, a single in-depth qualitative case study was conducted with a large professional services organisation. The findings unfold the different areas of impacts based on actual individual use and experience. The research concludes with a framework delineating the intertwined relationship between each social capital dimensions.
A DSS framework for maintaining relevant features of the Small Business B2C Websites
Venue: arXiv cs.CY 2016
Year: 2016

Managers are heavily engaged in strategic decision-making for businesses, particularly in a changing environment. One of the most important decisions for online small businesses, as part of their strategic planning, is selecting relevant features on their websites, both to attract and interact with consumers. However, only a few Australian small businesses use strategic tools for selecting their website features. As a result, businesses lose potential domestic sales in the business-to-consumer (B2C) sector. The aim of this study is to determine the relationship between factors that influence consumers' online purchasing, and owner/manager strategic decisions in selecting relevant features for websites. Results from employing qualitative case studies with small business owner/managers, and a content analysis of website features, inform the design of a Decision Support Systems (DSS) framework. This may assist owner/managers' strategic decisions to implement competitive features on B2C websites that ultimately attract more consumers.
Designing an ELearning Portal for Developing Countries: An Action Design Approach
Venue: arXiv cs.CY 2016
Year: 2016

This paper presents the first phase of a study on using course design, including current eLearning and social trends, to overcome challenges in eLearning within developing countries, particularly for vocational/non-mainstream training. The paper outlines the research and development of an ICT artefact using the Action Design Research method. The artefact will later be deployed and evaluated. The paper discusses the previous research that has categorised challenges in eLearning in developing countries and explores how these can be overcome through course and element design. Course design includes training development, platform selection and platform hosting, particularly in the context of developing countries. Element design looks at the creation of content that should be available, practical and viable to both the user and developer through the exploitation of current eLearning and social trends. The paper concludes with the concept of the ICT artefact that has been designed for phase two of the study.
Symbolic Music Data Version 1.0
Venue: arXiv cs.SD 2016
Year: 2016

In this document, we introduce a new dataset designed for training machine learning models of symbolic music data. Five datasets are provided, one of which is from a newly collected corpus of 20K midi files. We describe our preprocessing and cleaning pipeline, which includes the exclusion of a number of files based on scores from a previously developed probabilistic machine learning model. We also define training, testing and validation splits for the new dataset, based on a clustering scheme which we also describe. Some simple histograms are included.
Monitoramento de redes sociais analise de ferramenta com foco no retorno estrategico empresarial (Monitoring social networks analysis tool focused on strategic business return)
Venue: arXiv cs.SI 2016
Year: 2016

With the recent emergence and the explosion of users in social networks, Internet users have come to know more about other people or companies through information obtained from online groups or communities and therefore meet new opportunities. An avalanche of diverse and unstructured information is created every minute, for billions of devices connected to the Internet all the time. Analyze, process, understand and evaluate these data, it is a challenging task, given the volume, velocity and variety of information from these social networks. However, a better understanding of the process of monitoring networks and the use of tools of this kind are essential for people, companies and the academic world. The application of monitoring systems, information management and the possibility of bringing brands to people, are some positives that this research will provide better knowledge of the problems. In this research will be analyzed monitoring tool of social networks SCUP, its details, its features and the tool related to business management. In conclusion, we present all the peculiarities of the software and the possibilities applicable in the corporate environment, as aid to decision making.
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
Venue: arXiv cs.AI 2016
Year: 2016

This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state.
Convolution by Evolution: Differentiable Pattern Producing Networks
Venue: arXiv cs.NE 2016
Year: 2016

In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution.
Comments On "A New Transient Attack On The Kish Key Distribution System"
Venue: arXiv cs.CR 2016
Year: 2016

A recent IEEE Access Paper by Gunn, Allison and Abbott (GAA) proposed a new transient attack against the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange system. The attack is valid, but it is easy to build a defense for the KLJN system. Here we note that GAA's paper contains several invalid statements regarding security measures and the continuity of functions in classical physics. These deficiencies are clarified in our present paper, wherein we also emphasize that a new version of the KLJN system is immune against all existing attacks, including the one by GAA.
Wars Without Beginning or End: Violent Political Organizations and Irregular Warfare in the Sahel-Sahara
Venue: arXiv cs.SI 2016
Year: 2016

This article examines the structure and spatial patterns of violent political organizations in the Sahel-Sahara, a region characterized by growing political instability over the last 20 years. Drawing on a public collection of disaggregated data, the article uses network science to represent alliances and conflicts of 179 organizations that were involved in violent events between 1997 and 2014. To this end, we combine two spectral embedding techniques that have previously been considered separately: one for directed graphs (relationships are asymmetric), and one for signed graphs (relationships are positive or negative). Our result show that groups that are net attackers are indistinguishable at the level of their individual behavior, but clearly separate into pro- and anti-political violence based on the groups to which they are close. The second part of the article maps a series of 389 events related to nine Trans-Saharan Islamist groups between 2004 and 2014. Spatial analysis suggests that cross-border movement has intensified following the establishment of military bases by AQIM in Mali but reveals no evidence of a border sanctuary. Owing to the transnational nature of conflict, the article shows that national management strategies and foreign military interventions have profoundly affected the movement of Islamist groups.
A Modified Vortex Search Algorithm for Numerical Function Optimization
Venue: arXiv cs.AI 2016
Year: 2016

The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.
Physical extracurricular activities in educational child-robot interaction
Venue: arXiv cs.RO 2016
Year: 2016

In an exploratory study on educational child-robot interaction we investigate the effect of alternating a learning activity with an additional shared activity. Our aim is to enhance and enrich the relationship between child and robot by introducing "physical extracurricular activities". This enriched relationship might ultimately influence the way the child and robot interact with the learning material. We use qualitative measurement techniques to evaluate the effect of the additional activity on the child-robot relationship. We also explore how these metrics can be integrated in a highly exploratory cumulative score for the relationship between child and robot. This cumulative score suggests a difference in the overall child-robot relationship between children who engage in a physical extracurricular activity with the robot, and children who only engage in the learning activity with the robot.
Theoretical Robopsychology: Samu Has Learned Turing Machines
Venue: arXiv cs.AI 2016
Year: 2016

From the point of view of a programmer, the robopsychology is a synonym for the activity is done by developers to implement their machine learning applications. This robopsychological approach raises some fundamental theoretical questions of machine learning. Our discussion of these questions is constrained to Turing machines. Alan Turing had given an algorithm (aka the Turing Machine) to describe algorithms. If it has been applied to describe itself then this brings us to Turing's notion of the universal machine. In the present paper, we investigate algorithms to write algorithms. From a pedagogy point of view, this way of writing programs can be considered as a combination of learning by listening and learning by doing due to it is based on applying agent technology and machine learning. As the main result we introduce the problem of learning and then we show that it cannot easily be handled in reality therefore it is reasonable to use machine learning algorithm for learning Turing machines.
Face valuing: Training user interfaces with facial expressions and reinforcement learning
Venue: arXiv cs.HC 2016
Year: 2016

An important application of interactive machine learning is extending or amplifying the cognitive and physical capabilities of a human. To accomplish this, machines need to learn about their human users' intentions and adapt to their preferences. In most current research, a user has conveyed preferences to a machine using explicit corrective or instructive feedback; explicit feedback imposes a cognitive load on the user and is expensive in terms of human effort. The primary objective of the current work is to demonstrate that a learning agent can reduce the amount of explicit feedback required for adapting to the user's preferences pertaining to a task by learning to perceive a value of its behavior from the human user, particularly from the user's facial expressions---we call this face valuing. We empirically evaluate face valuing on a grip selection task. Our preliminary results suggest that an agent can quickly adapt to a user's changing preferences with minimal explicit feedback by learning a value function that maps facial features extracted from a camera image to expected future reward. We believe that an agent learning to perceive a value from the body language of its human user is complementary to existing interactive machine learning approaches and will help in creating successful human-machine interactive applications.
Audio Content based Geotagging in Multimedia
Venue: arXiv cs.SD 2016
Year: 2016

In this paper we propose methods to extract geographically relevant information in a multimedia recording using its audio. Our method primarily is based on the fact that urban acoustic environment consists of a variety of sounds. Hence, location information can be inferred from the composition of sound events/classes present in the audio. More specifically, we adopt matrix factorization techniques to obtain semantic content of recording in terms of different sound classes. These semantic information are then combined to identify the location of recording.
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing
Venue: arXiv cs.CV 2016
Year: 2016

We consider the very challenging task of restoring images (i) which have a large number of missing pixels, (ii) whose existing pixels are corrupted by noise and (iii) the ideal image to be restored contains both cartoon and texture elements. The combination of these three properties makes this inverse problem a very difficult one. The solution proposed in this manuscript is based on directional global three-part decomposition (DG3PD) [ThaiGottschlich2016] with directional total variation norm, directional G-norm and $\ell_\infty$-norm in curvelet domain as key ingredients of the model. Image decomposition by DG3PD enables a decoupled inpainting and denoising of the cartoon and texture components. A comparison to existing approaches for inpainting and denoising shows the advantages of the proposed method. Moreover, we regard the image restoration problem from the viewpoint of a Bayesian framework and we discuss the connections between the proposed solution by function space and related image representation by harmonic analysis and pyramid decomposition.
Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond
Venue: arXiv cs.DC 2016
Year: 2016

With the appearance of the heterogeneous platform OpenPower,many-core accelerator devices have been coupled with Power host processors for the first time. Towards utilizing their full potential, it is worth investigating performance portable algorithms that allow to choose the best-fitting hardware for each domain-specific compute task. Suiting even the high level of parallelism on modern GPGPUs, our presented approach relies heavily on abstract meta-programming techniques, which are essential to focus on fine-grained tuning rather than code porting. With this in mind, the CUDA-based open-source plasma simulation code PIConGPU is currently being abstracted to support the heterogeneous OpenPower platform using our fast porting interface cupla, which wraps the abstract parallel C++11 kernel acceleration library Alpaka. We demonstrate how PIConGPU can benefit from the tunable kernel execution strategies of the Alpaka library, achieving portability and performance with single-source kernels on conventional CPUs, Power8 CPUs and NVIDIA GPUs.