OpenRaiser/Intern-Atlas huggingface dataset:
On Hierarchical Communication Topologies in the pi-calculus
Venue: arXiv cs.PL 2016
Year: 2016

This paper is concerned with the shape invariants satisfied by the communication topology of π-terms, and the automatic inference of these invariants. A π-term P is hierarchical if there is a finite forest T such that the communication topology of every term reachable from P satisfies a T-shaped invariant. We design a static analysis to prove a term hierarchical by means of a novel type system that enjoys decidable inference. The soundness proof of the type system employs a non-standard view of π-calculus reactions. The coverability problem for hierarchical terms is decidable. This is proved by showing that every hierarchical term is depth-bounded, an undecidable property known in the literature. We thus obtain an expressive static fragment of the π-calculus with decidable safety verification problems.
Visual Script and Language Identification
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we introduce a script identification method based on hand-crafted texture features and an artificial neural network. The proposed pipeline achieves near state-of-the-art performance for script identification of video-text and state-of-the-art performance on visual language identification of handwritten text. More than using the deep network as a classifier, the use of its intermediary activations as a learned metric demonstrates remarkable results and allows the use of discriminative models on unknown classes. Comparative experiments in video-text and text in the wild datasets provide insights on the internals of the proposed deep network.
Research Project: Text Engineering Tool for Ontological Scientometry
Venue: arXiv cs.CL 2016
Year: 2016

The number of scientific papers grows exponentially in many disciplines. The share of online available papers grows as well. At the same time, the period of time for a paper to loose at chance to be cited anymore shortens. The decay of the citing rate shows similarity to ultradiffusional processes as for other online contents in social networks. The distribution of papers per author shows similarity to the distribution of posts per user in social networks. The rate of uncited papers for online available papers grows while some papers 'go viral' in terms of being cited. Summarized, the practice of scientific publishing moves towards the domain of social networks. The goal of this project is to create a text engineering tool, which can semi-automatically categorize a paper according to its type of contribution and extract relationships between them into an ontological database. Semi-automatic categorization means that the mistakes made by automatic pre-categorization and relationship-extraction will be corrected through a wikipedia-like front-end by volunteers from general public. This tool should not only help researchers and the general public to find relevant supplementary material and peers faster, but also provide more information for research funding agencies.
Scale-Free Online Learning
Venue: arXiv cs.LG 2016
Year: 2016

We design and analyze algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors. Our algorithms are instances of the Follow the Regularized Leader (FTRL) and Mirror Descent (MD) meta-algorithms. We achieve adaptiveness to the norms of the loss vectors by scale invariance, i.e., our algorithms make exactly the same decisions if the sequence of loss vectors is multiplied by any positive constant. The algorithm based on FTRL works for any decision set, bounded or unbounded. For unbounded decisions sets, this is the first adaptive algorithm for online linear optimization with a non-vacuous regret bound. In contrast, we show lower bounds on scale-free algorithms based on MD on unbounded domains.
An Enhanced Edge Adaptive Steganography Approach Using Threshold Value for Region Selection
Venue: arXiv cs.MM 2016
Year: 2016

This paper attempts to improve the quality and the modification rate of a Stego Image. The input image provided for estimating the quality of an image and the modified rate is a bitmap image. The threshold value is used as a parameter for selecting the high frequency pixels from the Cover Image. The data embedding process are performed on the pixels that are found with the help of Threshold value by using LSBMR. The quality of an image is estimated by the value of PSNR and the modification rate of an image is estimated by the value of MSE. The proposed approach achieves about 0.2 to 0.6 % of improvement in the quality of an image and about 4 to 10 % of improvement in the modification rate of an image compared to the edge detection techniques such as Sobel and Canny.
LAPPS: Location Aware Password Protection System
Venue: arXiv cs.CR 2016
Year: 2016

Location Aware Password Protection System (LAPPS) is designed to strengthen the security of traditional password protection systems. This is achieved by adding several layers of protection to the passwords that most traditional password protection systems generate. The current implementation looks at the Password/Pin numbers of Credit/Debit cards that are used on Automated Teller Machine (ATM),though the underlying design of the system can be used in many other scenarios. A password that is generated will be allocated to a particular user and to the ATM that is nearest to the user. LAPPS ensures the following qualities of the passwords that it generates. Location Awareness: The passwords are generated according to the users' geographical area, that they request their passwords from. So a password will only be active in just one location. Time Awareness: A password will only be valid for five minutes. The unused passwords will be discarded. Dynamic: The user has to have a new password each time he/she logs in. A password is generated to be used only once. User Oriented/Specific: The received password can only be used by the requester, and can only be used on its allocated ATM. Two Factor Authenticity: The confidential information will be secured using two-factor authentication. For extra security, a Pin generating device has been introduced. This will produce an eight digit number that the user has to supply to the mobile application, before requesting for a password. The user can obtain a pin number by inserting his/her Debit/Credit card and the fixed password that has been allocated when the user registers with the system.
Control of an Aerial Manipulator using On-line Parameter Estimator for an Unknown Payload
Venue: arXiv cs.RO 2016
Year: 2016

This paper presents an estimation and control algorithm for an aerial manipulator using a hexacopter with a 2-DOF robotic arm. The unknown parameters of a payload are estimated by an on-line estimator based on parametrization of the aerial manipulator dynamics. With the estimated mass information and the augmented passivity-based controller, the aerial manipulator can fly with the unknown object. Simulation for an aerial manipulator is performed to compare estimation performance between the proposed control algorithm and conventional adaptive sliding mode controller. Experimental results show a successful flight of a custom-made aerial manipulator while the unknown parameters related to an additional payload were estimated satisfactorily.
On Clustering Time Series Using Euclidean Distance and Pearson Correlation
Venue: arXiv cs.LG 2016
Year: 2016

For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is, in fact, equal to a distance based on Pearson Correlation. This has profound impact on many distance-based classification or clustering methods. In addition to this theoretically sound result we also show that the often used k-Means algorithm formally needs a mod ification to keep the interpretation as Pearson correlation strictly valid. Experimental results demonstrate that in many cases the standard k-Means algorithm generally produces the same results.
Wireless Energy Harvesting in a Cognitive Relay Network
Venue: arXiv cs.IT 2016
Year: 2016

Wireless energy harvesting is regarded as a promising energy supply alternative for energy-constrained wireless networks. In this paper, a new wireless energy harvesting protocol is proposed for an underlay cognitive relay network with multiple primary user (PU) transceivers. In this protocol, the secondary nodes can harvest energy from the primary network (PN) while sharing the licensed spectrum of the PN. In order to assess the impact of different system parameters on the proposed network, we first derive an exact expression for the outage probability for the secondary network (SN) subject to three important power constraints: 1) the maximum transmit power at the secondary source (SS) and at the secondary relay (SR), 2) the peak interference power permitted at each PU receiver, and 3) the interference power from each PU transmitter to the SR and to the secondary destination (SD). To obtain practical design insights into the impact of different parameters on successful data transmission of the SN, we derive throughput expressions for both the delay-sensitive and the delay-tolerant transmission modes. We also derive asymptotic closed-form expressions for the outage probability and the delay-sensitive throughput and an asymptotic analytical expression for the delay-tolerant throughput as the number of PU transceivers goes to infinity. The results show that the outage probability improves when PU transmitters are located near SS and sufficiently far from SR and SD. Our results also show that when the number of PU transmitters is large, the detrimental effect of interference from PU transmitters outweighs the benefits of energy harvested from the PU transmitters.
A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models
Venue: arXiv cs.LG 2016
Year: 2016

Conjugate pairs of distributions over infinite dimensional spaces are prominent in statistical learning theory, particularly due to the widespread adoption of Bayesian nonparametric methodologies for a host of models and applications. Much of the existing literature in the learning community focuses on processes possessing some form of computationally tractable conjugacy as is the case for the beta and gamma processes (and, via normalization, the Dirichlet process). For these processes, proofs of conjugacy and requisite derivation of explicit computational formulae for posterior density parameters are idiosyncratic to the stochastic process in question. As such, Bayesian Nonparametric models are currently available for a limited number of conjugate pairs, e.g. the Dirichlet-multinomial and beta-Bernoulli process pairs. In each of these above cases the likelihood process belongs to the class of discrete exponential family distributions. The exclusion of continuous likelihood distributions from the known cases of Bayesian Nonparametric Conjugate models stands as a disparity in the researcher's toolbox. In this paper we first address the problem of obtaining a general construction of prior distributions over infinite dimensional spaces possessing distributional properties amenable to conjugacy. Second, we bridge the divide between the discrete and continuous likelihoods by illustrating a canonical construction for stochastic processes whose Levy measure densities are from positive exponential families, and then demonstrate that these processes in fact form the prior, likelihood, and posterior in a conjugate family. Our canonical construction subsumes known computational formulae for posterior density parameters in the cases where the likelihood is from a discrete distribution belonging to an exponential family.
Privacy-Preserving Shortest Path Computation
Venue: arXiv cs.CR 2016
Year: 2016

Navigation is one of the most popular cloud computing services. But in virtually all cloud-based navigation systems, the client must reveal her location and destination to the cloud service provider in order to learn the fastest route. In this work, we present a cryptographic protocol for navigation on city streets that provides privacy for both the client's location and the service provider's routing data. Our key ingredient is a novel method for compressing the next-hop routing matrices in networks such as city street maps. Applying our compression method to the map of Los Angeles, for example, we achieve over tenfold reduction in the representation size. In conjunction with other cryptographic techniques, this compressed representation results in an efficient protocol suitable for fully-private real-time navigation on city streets. We demonstrate the practicality of our protocol by benchmarking it on real street map data for major cities such as San Francisco and Washington, D.C.
Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering
Venue: arXiv cs.IR 2016
Year: 2016

Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness. Our model is evaluated for two recent case studies on opinion aggregation over time. We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering.
The Effects of Age, Gender and Region on Non-standard Linguistic Variation in Online Social Networks
Venue: arXiv cs.CL 2016
Year: 2016

We present a corpus-based analysis of the effects of age, gender and region of origin on the production of both "netspeak" or "chatspeak" features and regional speech features in Flemish Dutch posts that were collected from a Belgian online social network platform. The present study shows that combining quantitative and qualitative approaches is essential for understanding non-standard linguistic variation in a CMC corpus. It also presents a methodology that enables the systematic study of this variation by including all non-standard words in the corpus. The analyses resulted in a convincing illustration of the Adolescent Peak Principle. In addition, our approach revealed an intriguing correlation between the use of regional speech features and chatspeak features.
Modeling Reusable, Platform-Independent Robot Assembly Processes
Venue: arXiv cs.SE 2016
Year: 2016

Smart factories that allow flexible production of highly individualized goods require flexible robots, usable in efficient assembly lines. Compliant robots can work safely in shared environments with domain experts, who have to program such robots easily for arbitrary tasks. We propose a new domain-specific language and toolchain for robot assembly tasks for compliant manipulators. With the LightRocks toolchain, assembly tasks are modeled on different levels of abstraction, allowing a separation of concerns between domain experts and robotics experts: externally provided, platform-independent assembly plans are instantiated by the domain experts using models of processes and tasks. Tasks are comprised of skills, which combine platform-specific action models provided by robotics experts. Thereby it supports a flexible production and re-use of modeling artifacts for various assembly processes.
Investigating gated recurrent neural networks for speech synthesis
Venue: arXiv cs.CL 2016
Year: 2016

Recently, recurrent neural networks (RNNs) as powerful sequence models have re-emerged as a potential acoustic model for statistical parametric speech synthesis (SPSS). The long short-term memory (LSTM) architecture is particularly attractive because it addresses the vanishing gradient problem in standard RNNs, making them easier to train. Although recent studies have demonstrated that LSTMs can achieve significantly better performance on SPSS than deep feed-forward neural networks, little is known about why. Here we attempt to answer two questions: a) why do LSTMs work well as a sequence model for SPSS; b) which component (e.g., input gate, output gate, forget gate) is most important. We present a visual analysis alongside a series of experiments, resulting in a proposal for a simplified architecture. The simplified architecture has significantly fewer parameters than an LSTM, thus reducing generation complexity considerably without degrading quality.
Minimum Average Delay of Routing Trees
Venue: arXiv cs.CC 2016
Year: 2016

The general communication tree embedding problem is the problem of mapping a set of communicating terminals, represented by a graph G, into the set of vertices of some physical network represented by a tree T. In the case where the vertices of G are mapped into the leaves of the host tree T the underlying tree is called a routing tree and if the internal vertices of T are forced to have degree 3, the host tree is known as layout tree. Different optimization problems have been studied in the class of communication tree problems such as well-known minimum edge dilation and minimum edge congestion problems. In this report we study the less investigate measure i.e. tree length, which is a representative for average edge dilation (communication delay) measure and also for average edge congestion measure. We show that finding a routing tree T for an arbitrary graph G with minimum tree length is an NP-Hard problem.
Group Communication Over LTE : A Radio Access Perspective
Venue: arXiv cs.NI 2016
Year: 2016

Long Term Evolution (LTE), which has its root on commercial mobile communications, recently becomes an influential solution to future public safety communications. To verify the feasibility of LTE for public safety, it is essential to investigate whether an LTE system optimized for one-to-one communications is capable of providing group communication, which is one of the most important service concepts in public safety. In general, a number of first responders for public safety need to form a group for communicating with each other or sharing the common data for collaboration on their mission. In this article, we analyze how the current LTE system can support group communication in a radio access aspect. Based on the requirements for group communication, we validate whether each LTE-enabled radio access method can efficiently support group communication. In addition, we propose a new multicast transmission scheme, termed index-coded Hybrid Automatic Retransmission reQuest (HARQ). By applying the index coding concept to HARQ operations, we show that the LTE system can provide group communication more sophisticatedly in terms of radio resource efficiency and scalability. We finally evaluate the performance of LTE-enabled group communication using several radio access methods and show how the proposed transmission scheme brings the performance enhancement via system level simulations.
Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding
Venue: arXiv cs.RO 2016
Year: 2016

There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and learn to transfer manipulation strategy across different objects by embedding point-cloud, natural language, and manipulation trajectory data into a shared embedding space using a deep neural network. In order to learn semantically meaningful spaces throughout our network, we introduce a method for pre-training its lower layers for multimodal feature embedding and a method for fine-tuning this embedding space using a loss-based margin. In order to collect a large number of manipulation demonstrations for different objects, we develop a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects and appliances with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot with our model can even prepare a cup of a latte with appliances it has never seen before.
IRLS and Slime Mold: Equivalence and Convergence
Venue: arXiv cs.DS 2016
Year: 2016

In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology. The first is the famous Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery while the second is the dynamics of a slime mold (Physarum polycephalum). Both of these dynamics are geared towards finding a minimum l1-norm solution in an affine subspace. Despite its simplicity the convergence of the IRLS method has been shown only for a certain regularization of it and remains an important open problem. Our first result shows that the two dynamics are projections of the same dynamical system in higher dimensions. As a consequence, and building on the recent work on Physarum dynamics, we are able to prove convergence and obtain complexity bounds for a damped version of the IRLS algorithm.
Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking
Venue: arXiv cs.CL 2016
Year: 2016

Re-speaking is a mechanism for obtaining high quality subtitles for use in live broadcast and other public events. Because it relies on humans performing the actual re-speaking, the task of estimating the quality of the results is non-trivial. Most organisations rely on humans to perform the actual quality assessment, but purely automatic methods have been developed for other similar problems, like Machine Translation. This paper will try to compare several of these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER and RIBES. These will then be matched to the human-derived NER metric, commonly used in re-speaking.
Opening Scholarly Communication in Social Sciences: Supporting Open Peer Review with Fidus Writer
Venue: arXiv cs.DL 2016
Year: 2016

Our system will initially provide readers, authors and reviewers with an alternative, thus having the potential to gain wider acceptance and gradually replace the old, incoherent publication process of our journals and of others in related fields. It will make journals more "open" (in terms of reusability) that are open access already, and it has the potential to serve as an incentive for turning "closed" journals into open access ones. In this poster paper we will present the framework of the OSCOSS system and highlight the reviewer use case.