OpenRaiser/Intern-Atlas huggingface dataset:
Two-Finger Keyboard Layout Problem: An Application On Turkish Language
Venue: arXiv cs.HC 2016
Year: 2016

Smart phone and tablet usage has sharply increased for the last decade. While entering test on these devices, virtual keyboards are generally used instead of conventional hardware keyboards. In this study, a new problem which is two-finger keyboard layout problem and solution approach is presented for increasing user test entrance performance, especially on virtual keyboards. Defined two-finger keyboard layout problem is modeled as Quadratic Assignment Problem. Because of combinatorial structure of the problem a genetic algorithm is developed. Its result is given to mathematical model as initial solution for finding better solutions with mathematical model. Proposed approach is applied on Turkish language. The new two finger keyboard layout for Turkish language is compared with F and QWERTY keyboard layouts based on certain performance measurement techniques.
The New Multi-Edge Metric-Constrained PEG/QC-PEG Algorithms for Designing the Binary LDPC Codes With Better Cycle-Structures
Venue: arXiv cs.IT 2016
Year: 2016

To obtain a better cycle-structure is still a challenge for the low-density parity-check (LDPC) code design. This paper formulates two metrics firstly so that the progressive edge-growth (PEG) algorithm and the approximate cycle extrinsic message degree (ACE) constrained PEG algorithm are unified into one integrated algorithm, called the metric-constrained PEG algorithm (M-PEGA). Then, as an improvement for the M-PEGA, the multi-edge metric-constrained PEG algorithm (MM-PEGA) is proposed based on two new concepts, the multi-edge local girth and the edge-trials. The MM-PEGA with the edge-trials, say a positive integer $r$, is called the $r$-edge M-PEGA, which constructs each edge of the non-quasi-cyclic (non-QC) LDPC code graph through selecting a check node whose $r$-edge local girth is optimal. In addition, to design the QC-LDPC codes with any predefined valid design parameters, as well as to detect and even to avoid generating the undetectable cycles in the QC-LDPC codes designed by the QC-PEG algorithm, the multi-edge metric constrained QC-PEG algorithm (MM-QC-PEGA) is proposed lastly. It is verified by the simulation results that increasing the edge-trials of the MM-PEGA/MM-QC-PEGA is expected to have a positive effect on the cycle-structures and the error performances of the LDPC codes designed by the MM-PEGA/MM-QC-PEGA.
User Scheduling and Optimal Power Allocation for Full-Duplex Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

The problem of user scheduling and power allocation in full-duplex (FD) cellular networks is considered, where a FD base station communicates simultaneously with one half-duplex (HD) user on each downlink and uplink channel. First, we propose low complexity user scheduling algorithms aiming at maximizing the sum rate of the considered FD system. Second, we derive the optimal power allocation for the two communication links, which is then exploited to introduce efficient metrics for FD/HD mode switching in the scheduling procedure, in order to further boost the system rate performance. We analyze the average sum rate of the proposed algorithms over Rayleigh fading and provide closed-form expressions. Our representative performance evaluation results for the algorithms with and without optimal power control offer useful insights on the interplay among rate, transmit powers, self-interference (SI) cancellation capability, and available number of users in the system.
Tweet Acts: A Speech Act Classifier for Twitter
Venue: arXiv cs.CL 2016
Year: 2016

Speech acts are a way to conceptualize speech as action. This holds true for communication on any platform, including social media platforms such as Twitter. In this paper, we explored speech act recognition on Twitter by treating it as a multi-class classification problem. We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features. We trained and tested a logistic regression classifier using a data set of manually labelled tweets. Our method achieved a state-of-the-art performance with an average F1 score of more than $0.70$. We also explored classifiers with three different granularities (Twitter-wide, type-specific and topic-specific) in order to find the right balance between generalization and overfitting for our task.
Siamese convolutional networks based on phonetic features for cognate identification
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, we explore the use of convolutional networks (ConvNets) for the purpose of cognate identification. We compare our architecture with binary classifiers based on string similarity measures on different language families. Our experiments show that convolutional networks achieve competitive results across concepts and across language families at the task of cognate identification.
Visual TASK: A Collaborative Cognitive Aid for Acute Care Resuscitation
Venue: arXiv cs.HC 2016
Year: 2016

Preventable medical errors are a severe problem in healthcare, causing over 400,000 deaths per year in the US in hospitals alone. In acute care, the branch of medicine encompassing the emergency department (ED) and intensive care units (ICU), error rates may be higher to due low situational awareness among clinicians performing resuscitation on patients. To support cognition, novice team leaders may rely on reference guides to direct and anticipate future steps. However, guides often act as a fixation point, diverting the leader's attention away from the team. To address this issue, we conducted a qualitative study that evaluates a collaborative cognitive aid co-designed with clinicians called Visual TASK. Our study explored the use of Visual TASK in three simulations employing a projected shared display with two different interaction modalities: the Microsoft Kinect and a touchscreen. Our results suggest that tools like the Kinect, while useful in other areas of acute care like the OR, are unsuitable for use in high-stress situations like resuscitation. We also observed that fixation may not be constrained to reference guides alone, and may extend to other objects in the room. We present our findings, and a discussion regarding future avenues in which collaborative cognitive aids may help in improving situational awareness in resuscitation.
Fast Concurrent Cuckoo Kick-Out Eviction Schemes for High-Density Tables
Venue: arXiv cs.DS 2016
Year: 2016

Cuckoo hashing guarantees constant-time lookups regardless of table density, making it a viable candidate for high-density tables. Cuckoo hashing insertions perform poorly at high table densities, however. In this paper, we mitigate this problem through the introduction of novel kick-out eviction algorithms. Experimentally, our algorithms reduce the number of bins viewed per insertion for high-density tables by as much as a factor of ten. We also introduce an optimistic concurrency scheme for transactional multi-writer cuckoo hash tables (not using hardware transactional memory). For delete-light workloads, one of our kick-out algorithms avoids all competition between insertions with high probability, and significantly reduces transaction-abort frequency. This result is extended to arbitrary workloads using a new synchronization mechanism called a claim flag.
Real-time Eye Gaze Direction Classification Using Convolutional Neural Network
Venue: arXiv cs.CV 2016
Year: 2016

Estimation eye gaze direction is useful in various human-computer interaction tasks. Knowledge of gaze direction can give valuable information regarding users point of attention. Certain patterns of eye movements known as eye accessing cues are reported to be related to the cognitive processes in the human brain. We propose a real-time framework for the classification of eye gaze direction and estimation of eye accessing cues. In the first stage, the algorithm detects faces using a modified version of the Viola-Jones algorithm. A rough eye region is obtained using geometric relations and facial landmarks. The eye region obtained is used in the subsequent stage to classify the eye gaze direction. A convolutional neural network is employed in this work for the classification of eye gaze direction. The proposed algorithm was tested on Eye Chimera database and found to outperform state of the art methods. The computational complexity of the algorithm is very less in the testing phase. The algorithm achieved an average frame rate of 24 fps in the desktop environment.
Large Sets of Orthogonal Sequences Suitable for Applications in CDMA Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we employ the so-called semi-bent functions to achieve significant improvements over currently known methods regarding the number of orthogonal sequences per cell that can be assigned to a regular tessellation of hexagonal cells, typical for certain code-division multiple-access (CDMA) systems. Our initial design method generates a large family of orthogonal sets of sequences derived from vectorial semi-bent functions. A modification of the original approach is proposed to avoid a hard combinatorial problem of allocating several such orthogonal sets to a single cell of a regular hexagonal network, while preserving the orthogonality to adjacent cells. This modification increases the number of users per cell by starting from shorter codewords and then extending the length of these codewords to the desired length. The specification and assignment of these orthogonal sets to a regular tessellation of hexagonal cells have been solved regardless of the parity and size of $m$ (where $2^m$ is the length of the codewords). In particular, when the re-use distance is $D=4$ the number of users per cell is $2^{m-2}$ for almost all $m$, which is twice as many as can be obtained by the best known methods.
Audio Features Affected by Music Expressiveness
Venue: arXiv cs.IR 2016
Year: 2016

Within a Music Information Retrieval perspective, the goal of the study presented here is to investigate the impact on sound features of the musician's affective intention, namely when trying to intentionally convey emotional contents via expressiveness. A preliminary experiment has been performed involving $10$ tuba players. The recordings have been analysed by extracting a variety of features, which have been subsequently evaluated by combining both classic and machine learning statistical techniques. Results are reported and discussed.
On the Evaluation of Dialogue Systems with Next Utterance Classification
Venue: arXiv cs.CL 2016
Year: 2016

An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data. Recent work has proposed Next-Utterance-Classification (NUC) as a surrogate task for building dialogue systems from text data. In this paper we investigate the performance of humans on this task to validate the relevance of NUC as a method of evaluation. Our results show three main findings: (1) humans are able to correctly classify responses at a rate much better than chance, thus confirming that the task is feasible, (2) human performance levels vary across task domains (we consider 3 datasets) and expertise levels (novice vs experts), thus showing that a range of performance is possible on this type of task, (3) automated dialogue systems built using state-of-the-art machine learning methods have similar performance to the human novices, but worse than the experts, thus confirming the utility of this class of tasks for driving further research in automated dialogue systems.
The Blockchain Anomaly
Venue: arXiv cs.DC 2016
Year: 2016

Most popular blockchain solutions, like Bitcoin, rely on proof-of-work, guaranteeing that the output of the consensus is agreed upon with high probability. However, this probability depends on the delivery of messages and that the computational power of the system is sufficiently scattered among pools of nodes in the network so that no pool can mine more blocks faster than the crowd. New approaches, like Ethereum, generalise the proof-of-work approach by letting individuals deploy their own private blockchain with high transaction throughput. As companies are starting to deploy private chains, it has become crucial to better understand the guarantees blockchains offer in such a small and controlled environment. In this paper, we present the \emph{Blockchain Anomaly}, an execution that we experienced when building our private chain at NICTA/Data61. Even though this anomaly has never been acknowledged before, it may translate into dramatic consequences for the user of blockchains. Named after the infamous Paxos anomaly, this anomaly makes dependent transactions, like "Bob sends money to Carole after he received money from Alice" impossible. This anomaly relies on the fact that existing blockchains do not ensure consensus safety deterministically: there is no way for Bob to make sure that Alice actually sent him coins without Bob using an external mechanism, like converting these coins into a fiat currency that allows him to withdraw. We also explore smart contracts as a potential alternative to transactions in order to freeze coins, and show implementations of smart contract that can suffer from the Blockchain anomaly and others that may cope with it.
A note on the shortest common superstring of NGS reads
Venue: arXiv cs.DM 2016
Year: 2016

The Shortest Superstring Problem (SSP) consists, for a set of strings S = {s_1,...,s_n}, to find a minimum length string that contains all s_i, 1 <= i <= k, as substrings. This problem is proved to be NP-Complete and APX-hard. Guaranteed approximation algorithms have been proposed, the current best ratio being 2+11/23, which has been achieved following a long and difficult quest. However, SSP is highly used in practice on next generation sequencing (NGS) data, which plays an increasingly important role in sequencing. In this note, we show that the SSP approximation ratio can be improved on NGS reads by assuming specific characteristics of NGS data that are experimentally verified on a very large sampling set.
A Compositional Neuro-Controller for Advanced Motor Control Tasks
Venue: arXiv cs.RO 2016
Year: 2016

Humans and animals developed a sophisticated motor control apparatus and there is much evidence that it has a modular structure. The modularity offers a range of benefits, e.g. ability to learn dissociable motion styles without interference and forgetting, fast adaptation and de-adaptation to changes conditions. However in robotics, building a controller that can efficiently incrementally learn new motion styles and provide switching between them is a formidable challenge. In this paper we address the problem by proposing a novel biologically inspired compositional neuro-controller. We have shown that the compositional controller is able to reproduce a set of trajectories more efficiently comparing to a simple controller, exploiting incremental learning benefits. Second, we have demonstrated that the proposed controller is able to learn different locomotion styles and switch between them in a simulated robot-snake.
A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors
Venue: arXiv cs.CV 2016
Year: 2016

Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed. The interest in feature-based applications continues to grow and has thus rendered the task of characterizing the performance of various feature detection methods an important issue in vision research. Inspired by the good practices of electronic system design, a generic framework based on the repeatability measure is presented in this paper that allows assessment of the upper and lower bounds of detector performance and finds statistically significant performance differences between detectors as a function of image transformation amount by introducing a new variant of McNemars test in an effort to design more reliable and effective vision systems. The proposed framework is then employed to establish operating and guarantee regions for several state-of-the-art detectors and to identify their statistical performance differences for three specific image transformations: JPEG compression, uniform light changes and blurring. The results are obtained using a newly acquired, large image database (20482) images with 539 different scenes. These results provide new insights into the behaviour of detectors and are also useful from the vision systems design perspective.
Robust Reserve Capacity Provision and Peak Load Reduction from Buildings in Smart Grids
Venue: arXiv cs.SY 2016
Year: 2016

This paper proposes a robust demand-side control algorithm in a smart grid environment for heating, ventilation and air conditioning (HVAC) systems. A robust model predictive control (RMPC) scheme in a receding horizon fashion is deployed, which optimizes electricity cost and capacity market participation of the HVAC system, while satisfying comfort and operational constraints of the building and utility, respectively. Thermal load uncertainties experienced by the HVAC system are included to perform a realistic assessment of the developed controller. The National Electricity Market of Singapore (NEMS) is used as a case study and the developed RMPC scheme is tested for various price signals and scenarios. Numerical simulation results show the effectiveness of the developed framework to be readily adopted by utilities -- interested in realizing a grid-friendly and economicaly eficient demand response (DR) strategy.
Heuristics for Planning, Plan Recognition and Parsing
Venue: arXiv cs.AI 2016
Year: 2016

In a recent paper, we have shown that Plan Recognition over STRIPS can be formulated and solved using Classical Planning heuristics and algorithms. In this work, we show that this formulation subsumes the standard formulation of Plan Recognition over libraries through a compilation of libraries into STRIPS theories. The libraries correspond to AND/OR graphs that may be cyclic and where children of AND nodes may be partially ordered. These libraries include Context-Free Grammars as a special case, where the Plan Recognition problem becomes a parsing with missing tokens problem. Plan Recognition over the standard libraries become Planning problems that can be easily solved by any modern planner, while recognition over more complex libraries, including Context-Free Grammars (CFGs), illustrate limitations of current Planning heuristics and suggest improvements that may be relevant in other Planning problems too.
The Design of the NetBSD I/O Subsystems
Venue: arXiv cs.OS 2016
Year: 2016

This book describes the source code of the NetBSD Operating System Release 1.6 in SUN UltraSPARC 64-bit platform by annotating related excerpts from references and user manuals on the NetBSD Operating System. The goal of this book is to provide necessary information to understand the operation and the implementation of I/O subsystems in the kernel as well as to design and implement a new filesystem on the NetBSD platform.
Coded Pilot Access: A Random Access Solution for Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

We present a novel access protocol for crowd scenarios in massive MIMO (Multiple-input multiple-output) systems. Crowd scenarios are characterized by a large number of users with intermittent access behavior, whereby orthogonal scheduling is infeasible. In such scenarios, random access is a natural choice. The proposed access protocol relies on two essential properties of a massive MIMO system, namely asymptotic orthogonality between user channels and asymptotic invariance of channel powers. Signal processing techniques that take advantage of these properties allow us to view a set of contaminated pilot signals as a graph code on which iterative belief propagation can be performed. This makes it possible to decontaminate pilot signals and increase the throughput of the system. Numerical evaluations show that the proposed access protocol increases the throughput with 36%, when having 400 antennas at the base station, compared to the conventional method of slotted ALOHA. With 1024 antennas, the throughput is increased by 85%.
Routing in all optical networks using recursive state space technique
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we have minimized the effects of failures on network performance, by using suitable Routing and Wavelenghth Assignment(RWA) method without disturbing other performance criteria such as blocking probability(BP) and network management(NM). The computation complexity is reduced by using Kalaman Filter(KF) techniques. The minimum reconfiguration probability routing (MRPR) algorithm must be able to select most reliable routes and assign wavelengths to connections in a manner that utilizes the light path(LP) established efficiently considering all possible requests.
Two Quasi Orthogonal Space-Time Block Codes with Better Performance and Low Complexity Decoder
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents two new space time block codes (STBCs) with quasi orthogonal structure for an open loop multi-input single-output (MISO) systems. These two codes have been designed to transmit from three or four antennas at the transmitter and be given to one antenna at the receiver. First, the proposed codes are introduced and their structures are investigated. This is followed by the demonstration of how the decoder decodes half of transmitted symbols independent of the other half. The last part of this paper discusses the simulation results, makes performance comparison against other popular approaches and concludes that the proposed solutions offer superiority.
Re-ranking Object Proposals for Object Detection in Automatic Driving
Venue: arXiv cs.CV 2016
Year: 2016

Object detection often suffers from a plenty of bootless proposals, selecting high quality proposals remains a great challenge. In this paper, we propose a semantic, class-specific approach to re-rank object proposals, which can consistently improve the recall performance even with less proposals. We first extract features for each proposal including semantic segmentation, stereo information, contextual information, CNN-based objectness and low-level cue, and then score them using class-specific weights learnt by Structured SVM. The advantages of the proposed model are twofold: 1) it can be easily merged to existing generators with few computational costs, and 2) it can achieve high recall rate uner strict critical even using less proposals. Experimental evaluation on the KITTI benchmark demonstrates that our approach significantly improves existing popular generators on recall performance. Moreover, in the experiment conducted for object detection, even with 1,500 proposals, our approach can still have higher average precision (AP) than baselines with 5,000 proposals.
Tongue contour extraction from ultrasound images based on deep neural network
Venue: arXiv cs.CV 2016
Year: 2016

Studying tongue motion during speech using ultrasound is a standard procedure, but automatic ultrasound image labelling remains a challenge, as standard tongue shape extraction methods typically require human intervention. This article presents a method based on deep neural networks to automatically extract tongue contour from ultrasound images on a speech dataset. We use a deep autoencoder trained to learn the relationship between an image and its related contour, so that the model is able to automatically reconstruct contours from the ultrasound image alone. In this paper, we use an automatic labelling algorithm instead of time-consuming hand-labelling during the training process, and estimate the performances of both automatic labelling and contour extraction as compared to hand-labelling. Observed results show quality scores comparable to the state of the art.