OpenRaiser/Intern-Atlas huggingface dataset:
Network Utility Aware Traffic Loading Balancing in Backhaul-constrained Cache-enabled Small Cell Networks with Hybrid Power Supplies
Venue: arXiv cs.NI 2014
Year: 2014

Explosive data traffic growth leads to a continuous surge in capacity demands across mobile networks. In order to provision high network capacity, small cell base stations (SCBSs) are widely deployed. Owing to the close proximity to mobile users, SCBSs can effectively enhance the network capacity and offloading traffic load from macro BSs (MBSs). However, the cost-effective backhaul may not be readily available for SCBSs, thus leading to backhaul constraints in small cell networks (SCNs). Enabling cache in BSs may mitigate the backhaul constraints in SCNs. Moreover, the dense deployment of SCBSs may incur excessive energy consumption. To alleviate brown power consumption, renewable energy will be explored to power BSs. In such a network, it is challenging to dynamically balance traffic load among BSs to optimize the network utilities. In this paper, we investigate the traffic load balancing in backhaul-constrained cache-enabled small cell networks powered by hybrid energy sources. We have proposed a network utility aware (NUA) traffic load balancing scheme that optimizes user association to strike a tradeoff between the green power utilization and the traffic delivery latency. On balancing the traffic load, the proposed NUA traffic load balancing scheme considers the green power utilization, the traffic delivery latency in both BSs and their backhaul, and the cache hit ratio. The NUA traffic load balancing scheme allows dynamically adjusting the tradeoff between the green power utilization and the traffic delivery latency. We have proved the convergence and the optimality of the proposed NUA traffic load balancing scheme. Through extensive simulations, we have compared performance of the NUA traffic load balancing scheme with other schemes and showed its advantages in backhaul-constrained cache-enabled small cell networks with hybrid power supplies.
Multicast Group Management for Multi-View 3D Videos in Wireless Networks
Venue: arXiv cs.NI 2014
Year: 2014

With the emergence of 3D mobile devices available in the markets, mobile 3D video services become increasingly important for video service providers, such as Youtube and Netflix, while multi-view 3D videos are potential to bring out varied innovative applications. However, enabling multi-view 3D video services may overwhelm WiFi networks when we multicast every view of a video. In this paper, therefore, we propose to incorporate depth-image-based rendering (DIBR), which allows each mobile client to synthesize the desired view from nearby left and right views, to effectively reduce the bandwidth consumption. Moreover, due to varied channel conditions, each client may suffer from different packet loss probabilities, and retransmissions incur additional bandwidth consumption. To address this issue, we first analyze the merit of view protection via DIBR for multi-view video multicast and then design a new protocol, named Multi-View Group Management Protocol (MVGMP), for the dynamic group management of multicast users. Simulation results manifest that our protocol effectively reduces bandwidth consumption and increases the probability for each client to successfully playback the desired view of a multi-view 3D video.
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback
Venue: arXiv cs.LG 2014
Year: 2014

We present and study a partial-information model of online learning, where a decision maker repeatedly chooses from a finite set of actions, and observes some subset of the associated losses. This naturally models several situations where the losses of different actions are related, and knowing the loss of one action provides information on the loss of other actions. Moreover, it generalizes and interpolates between the well studied full-information setting (where all losses are revealed) and the bandit setting (where only the loss of the action chosen by the player is revealed). We provide several algorithms addressing different variants of our setting, and provide tight regret bounds depending on combinatorial properties of the information feedback structure.
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Venue: arXiv cs.NE 2014
Year: 2014

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.
BitTorrent Sync: Network Investigation Methodology
Venue: arXiv cs.CR 2014
Year: 2014

The volume of personal information and data most Internet users find themselves amassing is ever increasing and the fast pace of the modern world results in most requiring instant access to their files. Millions of these users turn to cloud based file synchronisation services, such as Dropbox, Microsoft Skydrive, Apple iCloud and Google Drive, to enable "always-on" access to their most up-to-date data from any computer or mobile device with an Internet connection. The prevalence of recent articles covering various invasion of privacy issues and data protection breaches in the media has caused many to review their online security practices with their personal information. To provide an alternative to cloud based file backup and synchronisation, BitTorrent Inc. released an alternative cloudless file backup and synchronisation service, named BitTorrent Sync to alpha testers in April 2013. BitTorrent Sync's popularity rose dramatically throughout 2013, reaching over two million active users by the end of the year. This paper outlines a number of scenarios where the network investigation of the service may prove invaluable as part of a digital forensic investigation. An investigation methodology is proposed outlining the required steps involved in retrieving digital evidence from the network and the results from a proof of concept investigation are presented.
A stencil-based implementation of Parareal in the C++ domain specific embedded language STELLA
Venue: arXiv cs.DC 2014
Year: 2014

In view of the rapid rise of the number of cores in modern supercomputers, time-parallel methods that introduce concurrency along the temporal axis are becoming increasingly popular. For the solution of time-dependent partial differential equations, these methods can add another direction for concurrency on top of spatial parallelization. The paper presents an implementation of the time-parallel Parareal method in a C++ domain specific language for stencil computations (STELLA). STELLA provides both an OpenMP and a CUDA backend for a shared memory parallelization, using the CPU or GPU inside a node for the spatial stencils. Here, we intertwine this node-wise spatial parallelism with the time-parallel Parareal. This is done by adding an MPI-based implementation of Parareal, which allows us to parallelize in time across nodes. The performance of Parareal with both backends is analyzed in terms of speedup, parallel efficiency and energy-to-solution for an advection-diffusion problem with a time-dependent diffusion coefficient.
Improving the Performance of English-Tamil Statistical Machine Translation System using Source-Side Pre-Processing
Venue: arXiv cs.CL 2014
Year: 2014

Machine Translation is one of the major oldest and the most active research area in Natural Language Processing. Currently, Statistical Machine Translation (SMT) dominates the Machine Translation research. Statistical Machine Translation is an approach to Machine Translation which uses models to learn translation patterns directly from data, and generalize them to translate a new unseen text. The SMT approach is largely language independent, i.e. the models can be applied to any language pair. Statistical Machine Translation (SMT) attempts to generate translations using statistical methods based on bilingual text corpora. Where such corpora are available, excellent results can be attained translating similar texts, but such corpora are still not available for many language pairs. Statistical Machine Translation systems, in general, have difficulty in handling the morphology on the source or the target side especially for morphologically rich languages. Errors in morphology or syntax in the target language can have severe consequences on meaning of the sentence. They change the grammatical function of words or the understanding of the sentence through the incorrect tense information in verb. Baseline SMT also known as Phrase Based Statistical Machine Translation (PBSMT) system does not use any linguistic information and it only operates on surface word form. Recent researches shown that adding linguistic information helps to improve the accuracy of the translation with less amount of bilingual corpora. Adding linguistic information can be done using the Factored Statistical Machine Translation system through pre-processing steps. This paper investigates about how English side pre-processing is used to improve the accuracy of English-Tamil SMT system.
Cache-aware Performance Modeling and Prediction for Dense Linear Algebra
Venue: arXiv cs.PF 2014
Year: 2014

Countless applications cast their computational core in terms of dense linear algebra operations. These operations can usually be implemented by combining the routines offered by standard linear algebra libraries such as BLAS and LAPACK, and typically each operation can be obtained in many alternative ways. Interestingly, identifying the fastest implementation -- without executing it -- is a challenging task even for experts. An equally challenging task is that of tuning each routine to performance-optimal configurations. Indeed, the problem is so difficult that even the default values provided by the libraries are often considerably suboptimal; as a solution, normally one has to resort to executing and timing the routines, driven by some form of parameter search. In this paper, we discuss a methodology to solve both problems: identifying the best performing algorithm within a family of alternatives, and tuning algorithmic parameters for maximum performance; in both cases, we do not execute the algorithms themselves. Instead, our methodology relies on timing and modeling the computational kernels underlying the algorithms, and on a technique for tracking the contents of the CPU cache. In general, our performance predictions allow us to tune dense linear algebra algorithms within few percents from the best attainable results, thus allowing computational scientists and code developers alike to efficiently optimize their linear algebra routines and codes.
The Optimal Input Distribution for Partial Decode-and-Forward in the MIMO Relay Channel
Venue: arXiv cs.IT 2014
Year: 2014

This paper considers the partial decode-and-forward (PDF) strategy for the Gaussian multiple-input multiple-output (MIMO) relay channel. Unlike for the decode-and-forward (DF) strategy or point-to-point (P2P) transmission, for which Gaussian channel inputs are known to be optimal, the input distribution that maximizes the achievable PDF rate for the Gaussian MIMO relay channel has remained unknown so far. For some special cases, e.g., for relay channels where the optimal PDF strategy reduces to DF or P2P transmission, it could be deduced that Gaussian inputs maximize the PDF rate. For the general case, however, the problem has remained open until now. In this work, we solve this problem by proving that the maximum achievable PDF rate for the Gaussian MIMO relay channel is always attained by Gaussian channel inputs. Our proof relies on the channel enhancement technique, which was originally introduced by Weingarten et al. to derive the (private message) capacity region of the Gaussian MIMO broadcast channel. By combining this technique with a primal decomposition approach, we first establish that jointly Gaussian source and relay inputs maximize the achievable PDF rate for the aligned Gaussian MIMO relay channel. Subsequently, we use a limiting argument to extend this result from the aligned to the general Gaussian MIMO relay channel.
ParetoPrep: Fast computation of Path Skylines Queries
Venue: arXiv cs.DB 2014
Year: 2014

Computing cost optimal paths in network data is a very important task in many application areas like transportation networks, computer networks or social graphs. In many cases, the cost of an edge can be described by various cost criteria. For example, in a road network possible cost criteria are distance, time, ascent, energy consumption or toll fees. In such a multicriteria network, a route or path skyline query computes the set of all paths having pareto optimal costs, i.e. each result path is optimal for different user preferences. In this paper, we propose a new method for computing route skylines which significantly decreases processing time and memory consumption. Furthermore, our method does not rely on any precomputation or indexing method and thus, it is suitable for dynamically changing edge costs. Our experiments demonstrate that our method outperforms state of the art approaches and allows highly efficient path skyline computation without any preprocessing.
Pattern Encoding on the Poincare Sphere
Venue: arXiv cs.CV 2014
Year: 2014

This paper presents a convenient graphical tool for encoding visual patterns (such as image patches and image atoms) as point constellations in a space spanned by perceptual features and with a clear geometrical interpretation. General theory and a practical pattern encoding scheme are presented, inspired by encoding polarization states of a light wave on the Poincare sphere. This new pattern encoding scheme can be useful for many applications in image processing and computer vision. Here, three possible applications are illustrated, in clustering perceptually similar patterns, visualizing properties of learned dictionaries of image atoms and generating new dictionaries of image atoms from spherical codes.
A Morphological Analyzer for Japanese Nouns, Verbs and Adjectives
Venue: arXiv cs.CL 2014
Year: 2014

We present an open source morphological analyzer for Japanese nouns, verbs and adjectives. The system builds upon the morphological analyzing capabilities of MeCab to incorporate finer details of classification such as politeness, tense, mood and voice attributes. We implemented our analyzer in the form of a finite state transducer using the open source finite state compiler FOMA toolkit. The source code and tool is available at https://bitbucket.org/skylander/yc-nlplab/.
Vehicle Parameter Independent Gain Matrix Selection for a Quadrotor using State-Space Controller Design Methods
Venue: arXiv cs.SY 2014
Year: 2014

With quadrotor use seeing extensive growth in recent years, the autonomous control of these Unmanned Aerial Vehicles (UAVs) is an increasing relevant and intersting field. In this paper a linear state-space approach at designing a stable hover controller in the presence of disturbances is presented along with simulation of control system performance. Additionally the design of a tracking system, for linear inertial position and yaw, is presented with simulation results. The gain matrix developed for this control system is independent of the specific quadrotor parameters, meaning that this same gain matrix can be used on a wide variety of quadrotors without modification. The hover and tracking controllers designed in this paper proved to perform well in simulation under perturbation disturbances and normally distributed disturbances on the UAVs linear speeds and angular speeds.
Museum Automation with RFID
Venue: arXiv cs.CY 2014
Year: 2014

By increase of culture and knowledge of the people, request for visiting museums has increased and made the management of these places more complex. Valuable things in a museum or ancient place must be maintained well and also it need to managing visitors. To maintain things we should prevent them from theft, as well as environmental factors such as temperature, humidity, PH, chemical factors and mechanical events should be monitored. And if the conditions are damaging, appropriate alerts or reports to managers and experts should be announced. Visitors should also be monitored, as well as visitors need to be guided and getting information in the environment. By utilizing RFID technology and short-distance network tools, technical solutions for more efficient management and more effective retention in museums can be implemented.
Authentication System for Smart Homes Based on ARM7TDMI-S and IRIS-Fingerprint Recognition Technologies
Venue: arXiv cs.CR 2014
Year: 2014

With the rapid advancement in technology, smart homes have become applicable and so the need arise to solve the security challenges that are accompanied with its operation. Passwords and identity cards have been used as traditional authentication mechanisms in home environments, however, the rise of misuse of these mechanisms are proving them to be less reliable. For instance, ID cards can be misplaced, copied or counterfeited and being misused. Conversely, studies have shown that biometrics authentication systems particularly Iris Recognition Technology (IRT) and Fingerprint Recognition Technology (FRT) have the most reliable mechanisms to date providing tremendous accuracy and speed. As the technology becomes less expensive, application of IRT& FRT in smart-homes becomes more reliable and appropriate solution for security challenges. In this paper, we present our approach to design an authentication system for smart homes based on IRT, FRT and ARM7TDMI.The system employs two biometrics mechanisms for high reliability whereby initially, system users must enroll their fingerprints and eyes into the camera. Iris and fingerprint biometrics are scanned and the images are stored in the database. In the stage of authentication, FRT and IRT fingerprint scan and analyze points of the user's current input iris and fingerprint and match with the database contents. If one or more captured images do not match with the one in the database, then the system will not give authorization.
A massively parallel algorithm for constructing the BWT of large string sets
Venue: arXiv cs.DS 2014
Year: 2014

We present a new scalable, lightweight algorithm to incrementally construct the BWT and FM-index of large string sets such as those produced by Next Generation Sequencing. The algorithm is designed for massive parallelism and can effectively exploit the combination of low capacity high bandwidth memory and slower external system memory typical of GPU accelerated systems. Particularly, for a string set of n characters from an alphabet with σsymbols, it uses a constant amount of high-bandwidth memory and at most 3n log(σ) bits of system memory. Given that deep memory hierarchies are becoming a pervasive trait of high performance computing architectures, we believe this to be a relevant feature. The implementation can handle reads of arbitrary length and is up to 2 and respectively 6.5 times faster than state-of-the-art for short and long genomic reads
Knowledge-Based Automatic Generation of Partitioned Matrix Expressions
Venue: arXiv cs.MS 2014
Year: 2014

In a series of papers it has been shown that for many linear algebra operations it is possible to generate families of algorithms by following a systematic procedure. Although powerful, such a methodology involves complex algebraic manipulation, symbolic computations and pattern matching, making the generation a process challenging to be performed by hand. We aim for a fully automated system that from the sole description of a target operation creates multiple algorithms without any human intervention. Our approach consists of three main stages. The first stage yields the core object for the entire process, the Partitioned Matrix Expression (PME), which establishes how the target problem may be decomposed in terms of simpler sub-problems. In the second stage the PME is inspected to identify predicates, the Loop-Invariants, to be used to set up the skeleton of a family of proofs of correctness. In the third and last stage the actual algorithms are constructed so that each of them satisfies its corresponding proof of correctness. In this paper we focus on the first stage of the process, the automatic generation of Partitioned Matrix Expressions. In particular, we discuss the steps leading to a PME and the knowledge necessary for a symbolic system to perform such steps. We also introduce Cl1ck, a prototype system written in Mathematica that generates PMEs automatically.
Adaptive two-dimensional wavelet transformation based on the Haar system
Venue: arXiv cs.OH 2014
Year: 2014

The purpose is to study qualitative and quantitative rates of image compression by using different Haar wavelet banks. The experimental results of adaptive compression are provided. The paper deals with specific examples of orthogonal Haar bases generated by multiresolution analysis. Bases consist of three piecewise constant wavelet functions with a support $[0,1] \times [0,1] $.
Feature Learning from Incomplete EEG with Denoising Autoencoder
Venue: arXiv cs.CV 2014
Year: 2014

An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb-Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect.
In Situ Calibration of Six-Axes Force Torque Sensors using Accelerometer Measurements
Venue: arXiv cs.RO 2014
Year: 2014

This paper proposes techniques to calibrate six-axis force-torque sensors that can be performed in situ, i.e., without removing the sensor from the hosting system. We assume that the force-torque sensor is attached to a rigid body equipped with an accelerometer. Then, the proposed calibration technique uses the measurements of the accelerometer, but requires neither the knowledge of the inertial parameters nor the orientation of the rigid body. The proposed method exploits the geometry induced by the model between the raw measurements of the sensor and the corresponding force-torque. The validation of the approach is performed by calibrating two six-axis force-torque sensors of the iCub humanoid robot.