OpenRaiser/Intern-Atlas huggingface dataset:
An Exploration of Graphical Password Authentication for Children
Venue: arXiv cs.HC 2016
Year: 2016

In this paper, we explore graphical passwords as a child-friendly alternative for user authentication. We evaluate the usability of three variants of the PassTiles graphical password scheme for children, and explore the similarities and differences in performance and preferences between children and adults while using these schemes. Children were most successful at recalling passwords containing images of distinct objects. Both children and adults prefer graphical passwords to their existing schemes, but password memorization strategies differ considerably between the two groups. Based on our findings, we provide recommendations for designing more child-friendly authentication schemes.
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we describe an end to end Neural Model for Named Entity Recognition NER) which is based on Bi-Directional RNN-LSTM. Almost all NER systems for Hindi use Language Specific features and handcrafted rules with gazetteers. Our model is language independent and uses no domain specific features or any handcrafted rules. Our models rely on semantic information in the form of word vectors which are learnt by an unsupervised learning algorithm on an unannotated corpus. Our model attained state of the art performance in both English and Hindi without the use of any morphological analysis or without using gazetteers of any sort.
ARAPrototyper: Enabling Rapid Prototyping and Evaluation for Accelerator-Rich Architectures
Venue: arXiv cs.AR 2016
Year: 2016

Compared to conventional general-purpose processors, accelerator-rich architectures (ARAs) can provide orders-of-magnitude performance and energy gains and are emerging as one of the most promising solutions in the age of dark silicon. However, many design issues related to the complex interaction between general-purpose cores, accelerators, customized on-chip interconnects, and memory systems remain unclear and difficult to evaluate. In this paper we design and implement the ARAPrototyper to enable rapid design space explorations for ARAs in real silicons and reduce the tedious prototyping efforts far down to manageable efforts. First, ARAPrototyper provides a reusable baseline prototype with a highly customizable memory system, including interconnect between accelerators and buffers, interconnect between buffers and last-level cache (LLC) or DRAM, coherency choice at LLC or DRAM, and address translation support. Second, ARAPrototyper provides a clean interface to quickly integrate users' own accelerators written in high-level synthesis (HLS) code. The whole design flow is highly automated to generate a prototype of ARA on an FPGA system-on-chip (SoC). Third, to quickly develop applications that run seamlessly on the ARA prototype, ARAPrototyper provides a system software stack, abstracts the accelerators as software libraries, and provides APIs for software developers. Our experimental results demonstrate that ARAPrototyper enables a wide range of design space explorations for ARAs at manageable prototyping efforts, which has 4,000X to 10,000X faster evaluation time than full-system simulations. We believe that ARAPrototyper can be an attractive alternative for ARA design and evaluation.
Generalized Solution for the Demodulation of Reaction Shift Keying Signals in Molecular Communication Networks
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers a diffusion-based molecular communication system where the transmitter uses Reaction Shift Keying (RSK) as the modulation scheme. We focus on the demodulation of RSK signal at the receiver. The receiver consists of a front-end molecular circuit and a back-end demodulator. The front-end molecular circuit is a set of chemical reactions consisting of multiple chemical species. The optimal demodulator computes the posteriori probability of the transmitted symbols given the history of the observation. The derivation of the optimal demodulator requires the solution to a specific Bayesian filtering problem. The solution to this Bayesian filtering problem had been derived for a few specific molecular circuits and specific choice(s) of observed chemical species. The derivation of such solution is also lengthy. The key contribution of this paper is to present a general solution to this Bayesian filtering problem which can be applied to any molecular circuit and any choice of observed species.
Finding Minimum and Maximum Termination Time of Timed Automata Models with Cyclic Behaviour
Venue: arXiv cs.FL 2016
Year: 2016

The paper presents a novel algorithm for computing best and worst case execution times (BCET/WCET) of timed automata models with cyclic behaviour. The algorithms can work on any arbitrary diagonal-free TA and can handle more cases than previously existing algorithms for BCET/WCET computations, as it can handle cycles in TA and decide whether they lead to an infinite WCET. We show soundness of the proposed algorithm and study its complexity. To our knowledge, this is the first model checking algorithm that addresses comprehensively the BCET/WCET problem of systems with cyclic behaviour. Behrmann et al. provide an algorithm for computing the minimum cost/time of reaching a goal state in priced timed automata (PTA). The algorithm has been implemented in the well-known model checking tool UPPAAL to compute the minimum time for termination of an automaton. However, we show that in certain circumstances, when infinite cycles exist, the algorithm implemented in UPPAAL may not terminate, and we provide examples which UPPAAL fails to verify.
Maximal Packing with Interference Constraints
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we study the problem of scheduling a maximal set of transmitters subjected to an interference constraint across all the nodes. Given a set of nodes, the problem reduces to finding the maximum cardinality of a subset of nodes that can concurrently transmit without violating interference constraints. The resulting packing problem is a binary optimization problem and is NP hard. We propose a semi-definite relaxation (SDR) for this problem and provide bounds on the relaxation.
Ontology Verbalization using Semantic-Refinement
Venue: arXiv cs.AI 2016
Year: 2016

We propose a rule-based technique to generate redundancy-free NL descriptions of OWL entities.The existing approaches which address the problem of verbalizing OWL ontologies generate NL text segments which are close to their counterpart OWL statements.Some of these approaches also perform grouping and aggregating of these NL text segments to generate a more fluent and comprehensive form of the content.Restricting our attention to description of individuals and concepts, we find that the approach currently followed in the available tools is that of determining the set of all logical conditions that are satisfied by the given individual/concept name and translate these conditions verbatim into corresponding NL descriptions.Human-understandability of such descriptions is affected by the presence of repetitions and redundancies, as they have high fidelity to their OWL representation.In the literature, no efforts had been taken to remove redundancies and repetitions at the logical-level before generating the NL descriptions of entities and we find this to be the main reason for lack of readability of the generated text.Herein, we propose a technique called semantic-refinement(SR) to generate meaningful and easily-understandable descriptions of individuals and concepts of a given OWLontology.We identify the combinations of OWL/DL constructs that lead to repetitive/redundant descriptions and propose a series of refinement rules to rewrite the conditions that are satisfied by an individual/concept in a meaning-preserving manner.The reduced set of conditions are then employed for generating NL descriptions.Our experiments show that, SR leads to significantly improved descriptions of ontology entities.We also test the effectiveness and usefulness of the the generated descriptions for the purpose of validating the ontologies and find that the proposed technique is indeed helpful in the context.
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
Venue: arXiv cs.CL 2016
Year: 2016

We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We model the output vocabulary of about 100,000 words directly using deep bi-directional LSTM RNNs with CTC loss. The model is trained on 125,000 hours of semi-supervised acoustic training data, which enables us to alleviate the data sparsity problem for word models. We show that the CTC word models work very well as an end-to-end all-neural speech recognition model without the use of traditional context-dependent sub-word phone units that require a pronunciation lexicon, and without any language model removing the need to decode. We demonstrate that the CTC word models perform better than a strong, more complex, state-of-the-art baseline with sub-word units.
How CSMA/CA With Deferral Affects Performance and Dynamics in Power-Line Communications
Venue: arXiv cs.NI 2016
Year: 2016

Power-line communications (PLC) are becoming a key component in home networking, because they provide easy and high-throughput connectivity. The dominant MAC protocol for high data-rate PLC, the IEEE 1901, employs a CSMA/CA mechanism similar to the backoff process of 802.11. Existing performance evaluation studies of this protocol assume that the backoff processes of the stations are independent (the so-called decoupling assumption). However, in contrast to 802.11, 1901 stations can change their state after sensing the medium busy, which is regulated by the so-called deferral counter. This mechanism introduces strong coupling between the stations and, as a result, makes existing analyses inaccurate. In this paper, we propose a performance model for 1901, which does not rely on the decoupling assumption. We prove that our model admits a unique solution for a wide range of configurations and confirm the accuracy of the model using simulations. Our results show that we outperform current models based on the decoupling assumption. In addition to evaluating the performance in steady state, we further study the transient dynamics of 1901, which is also affected by the deferral counter.
ConfocalGN : a minimalistic confocal image simulator
Venue: arXiv cs.CV 2016
Year: 2016

SUMMARY : We developed a user-friendly software to generate synthetic confocal microscopy images from a ground truth specified as a 3D bitmap with pixels of arbitrary size. The software can analyze a real confocal stack to derivate noise parameters and will use them directly to generate new images with similar noise characteristics. Such synthetic images can then be used to assert the quality and robustness of an image analysis pipeline, as well as be used to train machine-learning image analysis procedures. We illustrate the approach with closed curves corresponding to the microtubule ring present in blood platelet. AVAILABILITY AND IMPLEMENTATION: ConfocalGN is written in Matlab but does not require any toolbox. The source code is distributed under the GPL 3.0 licence on https://github.com/SergeDmi/ConfocalGN.
Unfolding Genus-2 Orthogonal Polyhedra with Linear Refinement
Venue: arXiv cs.CG 2016
Year: 2016

We show that every orthogonal polyhedron of genus at most 2 can be unfolded without overlap while using only a linear number of orthogonal cuts (parallel to the polyhedron edges). This is the first result on unfolding general orthogonal polyhedra beyond genus-0. Our unfolding algorithm relies on the existence of at most 2 special leaves in what we call the "unfolding tree" (which ties back to the genus), so unfolding polyhedra of genus 3 and beyond requires new techniques.
Deep fusion of visual signatures for client-server facial analysis
Venue: arXiv cs.CV 2016
Year: 2016

Facial analysis is a key technology for enabling human-machine interaction. In this context, we present a client-server framework, where a client transmits the signature of a face to be analyzed to the server, and, in return, the server sends back various information describing the face e.g. is the person male or female, is she/he bald, does he have a mustache, etc. We assume that a client can compute one (or a combination) of visual features; from very simple and efficient features, like Local Binary Patterns, to more complex and computationally heavy, like Fisher Vectors and CNN based, depending on the computing resources available. The challenge addressed in this paper is to design a common universal representation such that a single merged signature is transmitted to the server, whatever be the type and number of features computed by the client, ensuring nonetheless an optimal performance. Our solution is based on learning of a common optimal subspace for aligning the different face features and merging them into a universal signature. We have validated the proposed method on the challenging CelebA dataset, on which our method outperforms existing state-of-the-art methods when rich representation is available at test time, while giving competitive performance when only simple signatures (like LBP) are available at test time due to resource constraints on the client.
Best-Buddies Tracking
Venue: arXiv cs.CV 2016
Year: 2016

Best-Buddies Tracking (BBT) applies the Best-Buddies Similarity measure (BBS) to the problem of model-free online tracking. BBS was introduced as a similarity measure between two point sets and was shown to be very effective for template matching. Originally, BBS was designed to work with point sets of equal size, and we propose a modification that lets it handle point sets of different size. The modified BBS is better suited to handle scale changes in the template size, as well as support a variable number of template images. We embed the modified BBS in a particle filter framework and obtain good results on a number of standard benchmarks.
Multiuser Media-based Modulation for Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider {\em media-based modulation (MBM)}, an attractive modulation scheme which is getting increased research attention recently, for the uplink of a massive MIMO system. Each user is equipped with one transmit antenna with multiple radio frequency (RF) mirrors (parasitic elements) placed near it. The base station (BS) is equipped with tens to hundreds of receive antennas. MBM with $m_{rf}$ RF mirrors and $n_r$ receive antennas over a multipath channel has been shown to asymptotically (as $m_{rf}\rightarrow \infty$) achieve the capacity of $n_r$ parallel AWGN channels. This suggests that MBM can be attractive for use in massive MIMO systems which typically employ a large number of receive antennas at the BS. In this paper, we investigate the potential performance advantage of multiuser MBM (MU-MBM) in a massive MIMO setting. Our results show that multiuser MBM (MU-MBM) can significantly outperform other modulation schemes. For example, a bit error performance achieved using 500 receive antennas at the BS in a massive MIMO system using conventional modulation can be achieved using just 128 antennas using MU-MBM. Even multiuser spatial modulation, and generalized spatial modulation in the same massive MIMO settings require more than 200 antennas to achieve the same bit error performance. Also, recognizing that the MU-MBM signal vectors are inherently sparse, we propose an efficient MU-MBM signal detection scheme that uses compressive sensing based reconstruction algorithms like orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), and subspace pursuit (SP).
Sliding Dictionary Based Sparse Representation For Action Recognition
Venue: arXiv cs.CV 2016
Year: 2016

The task of action recognition has been in the forefront of research, given its applications in gaming, surveillance and health care. In this work, we propose a simple, yet very effective approach which works seamlessly for both offline and online action recognition using the skeletal joints. We construct a sliding dictionary which has the training data along with their time stamps. This is used to compute the sparse coefficients of the input action sequence which is divided into overlapping windows and each window gives a probability score for each action class. In addition, we compute another simple feature, which calibrates each of the action sequences to the training sequences, and models the deviation of the action from the each of the training data. Finally, a score level fusion of the two heterogeneous but complementary features for each window is obtained and the scores for the available windows are successively combined to give the confidence scores of each action class. This way of combining the scores makes the approach suitable for scenarios where only part of the sequence is available. Extensive experimental evaluation on three publicly available datasets shows the effectiveness of the proposed approach for both offline and online action recognition tasks.
Nonlinear Frequency-Division Multiplexing in the Focusing Regime
Venue: arXiv cs.IT 2016
Year: 2016

Achievable rates of the nonlinear frequency-division multiplexing (NFDM) and wavelength-division multiplexing (WDM) subject to the same power and bandwidth constraints are computed as a function of transmit power in the standard single-mode fiber. NFDM achieves higher rates than WDM.
Surrogate-Assisted Partial Order-based Evolutionary Optimisation
Venue: arXiv cs.NE 2016
Year: 2016

In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models - it dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population. We introduce variants that differ in terms of the risk they allow when doing survival selection. Here, the anytime performance of different SAPEO variants is evaluated in conjunction with an SMS-EMOA using the BBOB bi-objective benchmark. We compare the obtained results with the performance of the regular SMS-EMOA, as well as another surrogate-assisted approach. The results open up general questions about the applicability and required conditions for surrogate-assisted multi-objective evolutionary algorithms to be tackled in the future.
Detecting Affordances by Visuomotor Simulation
Venue: arXiv cs.AI 2016
Year: 2016

The term "affordance" denotes the behavioral meaning of objects. We propose a cognitive architecture for the detection of affordances in the visual modality. This model is based on the internal simulation of movement sequences. For each movement step, the resulting sensory state is predicted by a forward model, which in turn triggers the generation of a new (simulated) motor command by an inverse model. Thus, a series of mental images in the sensory and in the motor domain is evoked. Starting from a real sensory state, a large number of such sequences is simulated in parallel. Final affordance detection is based on the generated motor commands. We apply this model to a real-world mobile robot which is faced with obstacle arrangements some of which are passable (corridor) and some of which are not (dead ends). The robot's task is to detect the right affordance ("pass-through-able" or "non-pass-through-able"). The required internal models are acquired in a hierarchical training process. Afterwards, the robotic agent is able to distinguish reliably between corridors and dead ends. This real-world result enhances the validity of the proposed mental simulation approach. In addition, we compare several key factors in the simulation process regarding performance and efficiency.
Dictionary Integration using 3D Morphable Face Models for Pose-invariant Collaborative-representation-based Classification
Venue: arXiv cs.CV 2016
Year: 2016

The paper presents a dictionary integration algorithm using 3D morphable face models (3DMM) for pose-invariant collaborative-representation-based face classification. To this end, we first fit a 3DMM to the 2D face images of a dictionary to reconstruct the 3D shape and texture of each image. The 3D faces are used to render a number of virtual 2D face images with arbitrary pose variations to augment the training data, by merging the original and rendered virtual samples to create an extended dictionary. Second, to reduce the information redundancy of the extended dictionary and improve the sparsity of reconstruction coefficient vectors using collaborative-representation-based classification (CRC), we exploit an on-line elimination scheme to optimise the extended dictionary by identifying the most representative training samples for a given query. The final goal is to perform pose-invariant face classification using the proposed dictionary integration method and the on-line pruning strategy under the CRC framework. Experimental results obtained for a set of well-known face datasets demonstrate the merits of the proposed method, especially its robustness to pose variations.
Critical success factors for m-commerce in Saudi Arabia's private sector -- a multiple case study analysis
Venue: arXiv cs.CY 2016
Year: 2016

Many developing country firms are investing huge money in the sector of mobile commerce m commerce Simplifying and understanding the factors which can impact on m commerce success enables the organisations managers to focus their efforts on the key areas of their m commerce businesses thereby contributing to the successful implementation of m commerce This study provides a clear understanding of m commerce in the private sector in the Kingdom of Saudi Arabia and identifies the critical success factors of implementing m commerce within the local business environment A case study approach will be used for five Saudi companies which use mcommerce represented by Alrajhi Bank Souqcom Saudi Electricity Company Saudi telecom company STC and Saudi Airlines This study represents a research in progress and interviews based on the literature to identify the key success factors for these companies in particular and in Saudi Arabia s private sector in general
Computing Skylines on Distributed Data
Venue: arXiv cs.DB 2016
Year: 2016

In this paper we study skyline queries in the distributed computational model, where we have $s$ remote sites and a central coordinator (the query node); each site holds a piece of data, and the coordinator wants to compute the skyline of the union of the $s$ datasets. The computation is in terms of rounds, and the goal is to minimize both the total communication cost and the round cost. Viewing data objects as points in the Euclidean space, we consider both the horizontal data partition case where each site holds a subset of points, and the vertical data partition case where each site holds one coordinate of all the points. We give a set of algorithms that have provable theoretical guarantees, and complement them with information theoretical lower bounds. We also demonstrate the superiority of our algorithms over existing heuristics by an extensive set of experiments on both synthetic and real world datasets.