OpenRaiser/Intern-Atlas huggingface dataset:
How to be Fair and Diverse?
Venue: arXiv cs.LG 2016
Year: 2016

Due to the recent cases of algorithmic bias in data-driven decision-making, machine learning methods are being put under the microscope in order to understand the root cause of these biases and how to correct them. Here, we consider a basic algorithmic task that is central in machine learning: subsampling from a large data set. Subsamples are used both as an end-goal in data summarization (where fairness could either be a legal, political or moral requirement) and to train algorithms (where biases in the samples are often a source of bias in the resulting model). Consequently, there is a growing effort to modify either the subsampling methods or the algorithms themselves in order to ensure fairness. However, in doing so, a question that seems to be overlooked is whether it is possible to produce fair subsamples that are also adequately representative of the feature space of the data set - an important and classic requirement in machine learning. Can diversity and fairness be simultaneously ensured? We start by noting that, in some applications, guaranteeing one does not necessarily guarantee the other, and a new approach is required. Subsequently, we present an algorithmic framework which allows us to produce both fair and diverse samples. Our experimental results on an image summarization task show marked improvements in fairness without compromising feature diversity by much, giving us the best of both the worlds.
Efficient Global Indoor Localization for Micro Aerial Vehicles
Venue: arXiv cs.RO 2016
Year: 2016

Indoor localization for autonomous micro aerial vehicles (MAVs) requires specific localization techniques, since the Global Positioning System (GPS) is usually not available. We present an efficient onboard computer vision approach that estimates 2D positions of an MAV in real-time. This global localization system does not suffer from error accumulation over time and uses a $k$-Nearest Neighbors ($k$-NN) algorithm to predict positions based on textons---small characteristic image patches that capture the texture of an environment. A particle filter aggregates the estimates and resolves positional ambiguities. To predict the performance of the approach in a given setting, we developed an evaluation technique that compares environments and identifies critical areas within them. We conducted flight tests to demonstrate the applicability of our approach. The algorithm has a localization accuracy of approximately 0.6 m on a 5 m$\times$5 m area at a runtime of 32 ms on board of an MAV. Based on random sampling, its computational effort is scalable to different platforms, trading off speed and accuracy.
Intrusion Alert Prediction Using a Hidden Markov Model
Venue: arXiv cs.CR 2016
Year: 2016

Intrusion detection is only a starting step in securing IT infrastructure. Prediction of intrusions is the next step to provide an active defense against incoming attacks. Current intrusion prediction methods focus mainly on prediction of either intrusion type or intrusion category and do not use or provide contextual information such as source and target IP address. In addition most of them are dependant on domain knowledge and specific scenario knowledge. The proposed algorithm employs a bag-of-words model together with a hidden Markov model which not depend on specific domain knowledge. Since this algorithm depends on a training process it is adaptable to different conditions. A key advantage of the proposed algorithm is the inclusion of contextual data such as source IP address, destination IP range, alert type and alert category in its prediction, which is crucial for an eventual response. Experiments conducted using a public data set generated over 2500 alert predictions and achieved accuracy of 81% and 77% for single step and five step predictions respectively for prediction of the next alert cluster. It also achieved an accuracy of prediction of 95% and 92% for single step and five step predictions respectively for prediction of the next alert category. The proposed methods achieved a prediction accuracy improvement of 5% for alert category over existing variable length Markov chain intrusion prediction methods, while providing more information for a possible defense.
Levelized Cost of Energy Calculation for Energy Storage Systems
Venue: arXiv cs.SY 2016
Year: 2016

The levelized cost of energy (LCOE) presents the energy-normalized cost of a generation asset by considering all associated costs (investment and operation) and total generated energy over its life cycle. As LCOE is a levelized value, it provides a quick and easy measure to compare different energy resource technologies with different characteristics. The LCOE calculation for large-scale power plants and distributed generations (DGs) is extensively studied and can be found in the literature. The discussions on the LCOE calculation for energy storage systems, however, is limited. Although still relatively expensive compared to generation technologies, energy storage is gaining significant attention and has been deployed extensively during the past few years, conceivably due to its many benefits such as load shifting, energy arbitrage, and renewable coordination. Therefore, LCOE calculation of energy storage systems plays an important role in economic evaluation of power systems. This paper proposes a method for calculating the LCOE of energy storage, and further provides the sensitivity analysis with respect to changes in capacity, electricity market prices, and efficiency.
A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds
Venue: arXiv cs.CV 2016
Year: 2016

With the development of numerous 3D sensing technologies, object registration on cross-source point cloud has aroused researchers' interests. When the point clouds are captured from different kinds of sensors, there are large and different kinds of variations. In this study, we address an even more challenging case in which the differently-source point clouds are acquired from a real street view. One is produced directly by the LiDAR system and the other is generated by using VSFM software on image sequence captured from RGB cameras. When it confronts to large scale point clouds, previous methods mostly focus on point-to-point level registration, and the methods have many limitations.The reason is that the least mean error strategy shows poor ability in registering large variable cross-source point clouds. In this paper, different from previous ICP-based methods, and from a statistic view, we propose a effective coarse-to-fine algorithm to detect and register a small scale SFM point cloud in a large scale Lidar point cloud. Seen from the experimental results, the model can successfully run on LiDAR and SFM point clouds, hence it can make a contribution to many applications, such as robotics and smart city development.
Interference Management and Power Allocation for NOMA Visible Light Communications Network
Venue: arXiv cs.IT 2016
Year: 2016

To design an efficient interference management and multiple access scheme for visible light communication (VLC) network, this letter leverages the non-orthogonal multiple access (NOMA), which has received significant attention in the $5^{th}$ generation wireless communication. With the residual interference from the successive interference cancellation in NOMA taken into account, we optimize the power allocation for NOMA VLC network to improve the achievable user rate under user quality of service (QoS) constraint. The performance of the proposed approaches is evaluated by the numerical results.
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
Venue: arXiv cs.CV 2016
Year: 2016

The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w.r.t the observed environment (scene). In recent years many vision based systems that perform simultaneous localization and mapping (SLAM) have been presented and released as open source. In this paper, we extend and improve upon a state-of-the-art SLAM to make it applicable to arbitrary, rigidly coupled multi-camera systems (MCS) using the MultiCol model. In addition, we include a performance evaluation on accurate ground truth and compare the robustness of the proposed method to a single camera version of the SLAM system. An open source implementation of the proposed multi-fisheye camera SLAM system can be found on-line https://github.com/urbste/MultiCol-SLAM.
An Efficient Adaptive Boundary Matching Algorithm for Video Error Concealment
Venue: arXiv cs.MM 2016
Year: 2016

Sending compressed video data in error-prone environments (like the Internet and wireless networks) might cause data degradation. Error concealment techniques try to conceal the received data in the decoder side. In this paper, an adaptive boundary matching algorithm is presented for recovering the damaged motion vectors (MVs). This algorithm uses an outer boundary matching or directional temporal boundary matching method to compare every boundary of candidate macroblocks (MBs), adaptively. It gives a specific weight according to the accuracy of each boundary of the damaged MB. Moreover, if each of the adjacent MBs is already concealed, different weights are given to the boundaries. Finally, the MV with minimum adaptive boundary distortion is selected as the MV of the damaged MB. Experimental results show that the proposed algorithm can improve both objective and subjective quality of reconstructed frames without any considerable computational complexity. The average PSNR in some frames of test sequences increases about 5.20, 5.78, 5.88, 4.37, 4.41, and 3.50 dB compared to average MV, classic boundary matching, directional boundary matching, directional temporal boundary matching, outer boundary matching, and dynamical temporal error concealment algorithm, respectively.
Tracking of Wideband Multipath Components in a Vehicular Communication Scenario
Venue: arXiv cs.IT 2016
Year: 2016

A detailed understanding of the dynamic processes of vehicular radio channels is crucial for its realistic modeling. In this paper, we present multipath components (MPCs) tracking results from a channel sounder measurement with 1 GHz bandwidth at a carrier frequency of 5.7 GHz. We describe in detail the applied algorithms and perform a tracking performance evaluation based on artificial channels and on measurement data from a tunnel scenario. The tracking performance of the proposed algorithm is comparable to the tracking performance of the state-of-the-art Gaussian mixture probability hypothesis density filter, yet with a significantly lower complexity. The fluctuation of the measured channel gain is followed very well by the proposed tracking algorithm, with a power loss of only 2.5 dB. We present statistical distributions for the number of MPCs and the birth/death rate. The applied algorithms and tracking results can be used to enhance the development of geometry-based channel models.
Reordering rules for English-Hindi SMT
Venue: arXiv cs.CL 2016
Year: 2016

Reordering is a preprocessing stage for Statistical Machine Translation (SMT) system where the words of the source sentence are reordered as per the syntax of the target language. We are proposing a rich set of rules for better reordering. The idea is to facilitate the training process by better alignments and parallel phrase extraction for a phrase-based SMT system. Reordering also helps the decoding process and hence improving the machine translation quality. We have observed significant improvements in the translation quality by using our approach over the baseline SMT. We have used BLEU, NIST, multi-reference word error rate, multi-reference position independent error rate for judging the improvements. We have exploited open source SMT toolkit MOSES to develop the system.
Automated OCT Segmentation for Images with DME
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a novel automated system that segments six sub-retinal layers from optical coherence tomography (OCT) image stacks of healthy patients and patients with diabetic macular edema (DME). First, each image in the OCT stack is denoised using a Wiener deconvolution algorithm that estimates the additive speckle noise variance using a novel Fourier-domain based structural error. This denoising method enhances the image SNR by an average of 12dB. Next, the denoised images are subjected to an iterative multi-resolution high-pass filtering algorithm that detects seven sub-retinal surfaces in six iterative steps. The thicknesses of each sub-retinal layer for all scans from a particular OCT stack are then compared to the manually marked groundtruth. The proposed system uses adaptive thresholds for denoising and segmenting each image and hence it is robust to disruptions in the retinal micro-structure due to DME. The proposed denoising and segmentation system has an average error of 1.2-5.8 $μm$ and 3.5-26$μm$ for segmenting sub-retinal surfaces in normal and abnormal images with DME, respectively. For estimating the sub-retinal layer thicknesses, the proposed system has an average error of 0.2-2.5 $μm$ and 1.8-18 $μm$ in normal and abnormal images, respectively. Additionally, the average inner sub-retinal layer thickness in abnormal images is estimated as 275$μm (r=0.92)$ with an average error of 9.3 $μm$, while the average thickness of the outer layers in abnormal images is estimated as 57.4$μm (r=0.74)$ with an average error of 3.5 $μm$. The proposed system can be useful for tracking the disease progression for DME over a period of time.
Learning to Reason With Adaptive Computation
Venue: arXiv cs.CL 2016
Year: 2016

Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that learning the correct number of inference steps is difficult. We introduce the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model.
ALPINE: Anytime Mining with Definite Guarantees
Venue: arXiv cs.DB 2016
Year: 2016

ALPINE is to our knowledge the first anytime algorithm to mine frequent itemsets and closed frequent itemsets. It guarantees that all itemsets with support exceeding the current checkpoint's support have been found before it proceeds further. Thus, it is very attractive for extremely long mining tasks with very high dimensional data (for example in genetics) because it can offer intermediate meaningful and complete results. This ANYTIME feature is the most important contribution of ALPINE, which is also fast but not necessarily the fastest algorithm around. Another critical advantage of ALPINE is that it does not require the apriori decided minimum support value.
Sparser Sparse Roadmaps
Venue: arXiv cs.RO 2016
Year: 2016

We present methods for offline generation of sparse roadmap spanners that result in graphs 79% smaller than existing approaches while returning solutions of equivalent path quality. Our method uses a hybrid approach to sampling that combines traditional graph discretization with random sampling. We present techniques that optimize the graph for the L1-norm metric function commonly used in joint-based robotic planning, purposefully choosing a $t$-stretch factor based on the geometry of the space, and removing redundant edges that do not contribute to the graph quality. A high-quality pre-processed sparse roadmap is then available for re-use across many different planning scenarios using standard repair and re-plan methods. Pre-computing the roadmap offline results in more deterministic solutions, reduces the memory requirements by affording complex rejection criteria, and increases the speed of planning in high-dimensional spaces allowing more complex problems to be solved such as multi-modal task planning. Our method is validated through simulated benchmarks against the SPARS2 algorithm. The source code is freely available online as an open source extension to OMPL.
Wirelessly Powered Communication Networks with Short Packets
Venue: arXiv cs.IT 2016
Year: 2016

Wirelessly powered communications will entail short packets due to naturally small payloads, low-latency requirements and/or insufficient energy resources to support longer transmissions. In this paper, a wirelessly powered communication system is investigated where an energy harvesting transmitter, charged by one or more power beacons via wireless energy transfer, attempts to communicate with a receiver over a noisy channel. Under a save-then-transmit protocol, the system performance is characterized using metrics such as the energy supply probability at the transmitter, and the achievable rate at the receiver for the case of short packets. Leveraging the framework of finite-length information theory, tractable analytical expressions are derived for the considered metrics in terms of system parameters such as the harvest blocklength, the transmit blocklength, the harvested power and the transmit power. The analysis provides several useful design guidelines. Though using a small transmit power or a small transmit blocklength helps avoid energy outages, the consequently smaller signal-to-noise ratio or the fewer coding opportunities may cause an information outage. Scaling laws are derived to capture this inherent trade-off between the harvest and transmit blocklengths. Moreover, the asymptotically optimal transmit power is derived in closed-form. Numerical results reveal that power control is essential for improving the achievable rate of the system in the finite blocklength regime. The asymptotically optimal transmit power yields nearly optimal performance in the finite blocklength regime.
Path discovery by Querying the federation of Relational Database and RDF Graph
Venue: arXiv cs.DB 2016
Year: 2016

The class of queries for detecting path is an important as those can extract implicit binary relations over the nodes of input graphs. Most of the path querying languages used by the RDF community, like property paths in W3C SPARQL 1.1 and nested regular expressions in nSPARQL are based on the regular expressions. Federated queries allow for combining graph patterns and relational database that enables the evaluations over several heterogeneous data resources within a single query. Federated queries in W3C SPARQL 1.1 currently evaluated over different SPARQL endpoints. In this paper, we present a federated path querying language as an extension of regular path querying language for supporting RDF graph integration with relational database. The federated path querying language is absolutely more expressive than nested regular expressions and negation-free property paths. Its additional expressivity can be used for capturing the conjunction and federation of nested regular path queries. Despite the increase in expressivity, we also show that federated path queries are still enjoy a low computational complexity and can be evaluated efficiently.
Omnidirectional Space-Time Block Coding for Common Information Broadcasting in Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we design space-time block codes (STBCs) to broadcast the common information omnidirectionally in a massive MIMO downlink. To reduce the burden of the downlink channel estimation and achieve partial spatial diversity from base station (BS) transmit antennas, we propose channel-independently precoded low-dimensional STBC. The precoding matrix and the signal constellation in the low-dimensional STBC are jointly designed to guarantee omnidirectional coverage at any instant time and sufficiently utilize the power amplifier capacities of BS transmit antennas, and at the same time, achieve the full diversity of the low-dimensional STBC. Under this framework, several designs are presented. To provide transmit diversity order of two, a precoded Alamouti code is proposed, which has a fast symbol-wise maximum-likelihood (ML) decoding. To provide transmit diversity order of four, three types of STBCs are proposed, being referred to as precoded orthogonal STBC (OSTBC), precoded quasi-orthogonal STBC (QOSTBC), and precoded coordinate interleaved orthogonal design (CIOD), respectively. The last two codes have the same complexity for pair-wise ML decoding, while precoded QOSTBC has a higher coding gain when the bit rate is lower than or equal to 4 bps/Hz, and precoded CIOD has a higher coding gain when the bit rate is higher than 4 bps/Hz. Precoded OSTBC has a higher decoding complexity and a lower coding gain than the other two codes, since in the precoded OSTBC the information symbols need to be jointly designed and decoded. Moreover, a precoded no-zero-entry Toeplitz code and a precoded no-zero-entry overlapped Alamouti code are also proposed. These two codes can achieve a higher diversity order with linear receivers.
Visual Themes and Sentiment on Social Networks To Aid First Responders During Crisis Events
Venue: arXiv cs.SI 2016
Year: 2016

Online Social Networks explode with activity whenever a crisis event takes place. Most content generated as part of this activity is a mixture of text and images, and is particularly useful for first responders to identify popular topics of interest and gauge the pulse and sentiment of citizens. While multiple researchers have used text to identify, analyze and measure themes and public sentiment during such events, little work has explored visual themes floating on networks in the form of images, and the sentiment inspired by them. Given the potential of visual content for influencing users' thoughts and emotions, we perform a large scale analysis to compare popular themes and sentiment across images and textual content posted on Facebook during the terror attacks that took place in Paris in 2015. Using state-of-the-art image summarization techniques, we discovered multiple visual themes which were popular in images, but were not identifiable through text. We uncovered instances of misinformation and false flag (conspiracy) theories among popular image themes, which were not prominent in user generated textual content, and can be of particular inter- est to first responders. Our analysis also revealed that while textual content posted after the attacks reflected negative sentiment, images inspired positive sentiment. To the best of our knowledge, this is the first large scale study of images posted on social networks during a crisis event.
Generalization Bounds for Weighted Automata
Venue: arXiv cs.LG 2016
Year: 2016

This paper studies the problem of learning weighted automata from a finite labeled training sample. We consider several general families of weighted automata defined in terms of three different measures: the norm of an automaton's weights, the norm of the function computed by an automaton, or the norm of the corresponding Hankel matrix. We present new data-dependent generalization guarantees for learning weighted automata expressed in terms of the Rademacher complexity of these families. We further present upper bounds on these Rademacher complexities, which reveal key new data-dependent terms related to the complexity of learning weighted automata.