OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive dictionary based approach for background noise and speaker classification and subsequent source separation
Venue: arXiv cs.SD 2016
Year: 2016

A judicious combination of dictionary learning methods, block sparsity and source recovery algorithm are used in a hierarchical manner to identify the noises and the speakers from a noisy conversation between two people. Conversations are simulated using speech from two speakers, each with a different background noise, with varied SNR values, down to -10 dB. Ten each of randomly chosen male and female speakers from the TIMIT database and all the noise sources from the NOISEX database are used for the simulations. For speaker identification, the relative value of weights recovered is used to select an appropriately small subset of the test data, assumed to contain speech. This novel choice of using varied amounts of test data results in an improvement in the speaker recognition rate of around 15% at SNR of 0 dB. Speech and noise are separated using dictionaries of the estimated speaker and noise, and an improvement of signal to distortion ratios of up to 10% is achieved at SNR of 0 dB. K-medoid and cosine similarity based dictionary learning methods lead to better recognition of the background noise and the speaker. Experiments are also conducted on cases, where either the background noise or the speaker is outside the set of trained dictionaries. In such cases, adaptive dictionary learning leads to performance comparable to the other case of complete dictionaries.
Real-time Predictive Analytics for Improving Public Transportation Systems' Resilience
Venue: arXiv cs.CY 2016
Year: 2016

Public transit systems are a critical component of major metropolitan areas. However, in the face of increasing demand, most of these systems are operating close to capacity. Under normal operating conditions, station crowding and boarding denial are becoming a major concern for transit agencies. As such, any disruption in service will have even more severe consequences, affecting huge number of passengers. Considering the aging infrastructure of many large cities, such as New York and London, these disruptions are to be expected, amplifying the need for better demand management and strategies to deal with congested transit facilities. Opportunistic sensors such as smart cards (AFC), automatic vehicle location systems (AVL), GPS, etc. provide a wealth of information about system's performance and passengers' trip making patterns. We develop a hybrid data/model-driven decision support system, using real-time predictive models, to help transit operators manage and respond proactively to disruptions and mitigate consequences in a timely fashion. These models include station arrival and origin-destination predictions in real-time to help transit agencies, and predictive information systems for assisting passengers' trip making decisions.
Bit-permuted coded modulation for polar codes
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of using polar codes with higher order modulation over AWGN channels. Unlike prior work, we focus on using modulation independent polar codes. That is, the polar codes are not re-designed based on the modulation used. Instead, we propose bit-permuted coded modulation (BPCM): a technique for using the multilevel coding (MLC) approach for an arbitrary polar code. The BPCM technique exploits a natural connection between MLC and polar codes. It involves applying bit permutations prior to mapping the polar code to a higher order modulation. The bit permutations are designed, via density evolution, to match the rates provided by various bit levels of the higher order modulation to that of the polar code. We demonstrate performance of the BPCM technique using link simulations and density evolution for the AWGN channel. We compare the BPCM technique with the bit-interleaved coded modulation (BICM) technique. When using polar codes designed for BPSK modulation, we show gains for BPCM over BICM with random interleaver of up to 0.2 dB, 0.7 dB and 1.4 dB for 4-ASK, 8-ASK, and 16-ASK respectively.
Stepping Stabilization Using a Combination of DCM Tracking and Step Adjustment
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, a method for stabilizing biped robots stepping by a combination of Divergent Component of Motion (DCM) tracking and step adjustment is proposed. In this method, the DCM trajectory is generated, consistent with the predefined footprints. Furthermore, a swing foot trajectory modification strategy is proposed to adapt the landing point, using DCM measurement. In order to apply the generated trajectories to the full robot, a Hierarchical Inverse Dynamics (HID) is employed. The HID enables us to use different combinations of the DCM tracking and step adjustment for stabilizing different biped robots. Simulation experiments on two scenarios for two different simulated robots, one with active ankles and the other with passive ankles, are carried out. Simulation results demonstrate the effectiveness of the proposed method for robots with both active and passive ankles.
Bacterial Foraging Optimized STATCOM for Stability Assessment in Power System
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents a study of improvement in stability in a single machine connected to infinite bus (SMIB) power system by using static compensator (STATCOM). The gains of Proportional-Integral-Derivative (PID) controller in STATCOM are being optimized by heuristic technique based on Particle swarm optimization (PSO). Further, Bacterial Foraging Optimization (BFO) as an alternative heuristic method is also applied to select optimal gains of PID controller. The performance of STATCOM with the above soft-computing techniques are studied and compared with the conventional PID controller under various scenarios. The simulation results are accompanied with performance indices based quantitative analysis. The analysis clearly signifies the robustness of the new scheme in terms of stability and voltage regulation when compared with conventional PID.
Optimal Energy-Delay Tradeoff for Opportunistic Spectrum Access in Cognitive Radio Networks
Venue: arXiv cs.NI 2016
Year: 2016

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. Basic CR features allow SUs to transmit only when the licensed primary channel is not occupied by PUs. However, waiting for idle time slot may include large packet delay and high energy consumption. We further consider that the SU may decide, at any moment, to use another dedicated way of communication (3G) in order to transmit its packets. Thus, we consider an Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy consumption as well as packets' delay, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. For implementation purposes, we propose online learning mechanisms that estimate the PU activity and determine the appropriate threshold strategy on the fly. In particular, numerical illustrations validate our theoretical findings.
Exploiting Universal Redundancy
Venue: arXiv cs.DC 2016
Year: 2016

Fault tolerance is essential for building reliable services; however, it comes at the price of redundancy, mainly the "replication factor" and "diversity". With the increasing reliance on Internet-based services, more machines (mainly servers) are needed to scale out, multiplied with the extra expense of replication. This paper revisits the very fundamentals of fault tolerance and presents "artificial redundancy": a formal generalization of "exact copy" redundancy in which new sources of redundancy are exploited to build fault tolerant systems. On this concept, we show how to build "artificial replication" and design "artificial fault tolerance" (AFT). We discuss the properties of these new techniques showing that AFT extends current fault tolerant approaches to use other forms of redundancy aiming at reduced cost and high diversity.
Outlier Detection from Network Data with Subnetwork Interpretation
Venue: arXiv cs.AI 2016
Year: 2016

Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computing the anomalous degree of a network sample is generally not sufficient. In fact, explaining why the network is exceptional, expressed in the form of subnetwork, is also equally important. In this paper, we develop a novel algorithm to address these two key problems. We treat each network sample as a potential outlier and identify subnetworks that mostly discriminate it from nearby regular samples. The algorithm is developed in the framework of network regression combined with the constraints on both network topology and L1-norm shrinkage to perform subnetwork discovery. Our method thus goes beyond subspace/subgraph discovery and we show that it converges to a global optimum. Evaluation on various real-world network datasets demonstrates that our algorithm not only outperforms baselines in both network and high dimensional setting, but also discovers highly relevant and interpretable local subnetworks, further enhancing our understanding of anomalous networks.
PTAS for Ordered Instances of Resource Allocation Problems with Restrictions on Inclusions
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of allocating a set $I$ of $m$ indivisible resources (items) to a set $P$ of $n$ customers (players) competing for the resources. Each resource $j \in I$ has a same value $v_j > 0$ for a subset of customers interested in $j$, and zero value for the remaining customers. The utility received by each customer is the sum of the values of the resources allocated to her. The goal is to find a feasible allocation of the resources to the interested customers such that for the Max-Min allocation problem (Min-Max allocation problem) the minimum of the utilities (maximum of the utilities) received by the customers is maximized (minimized). The Max-Min allocation problem is also known as the \textit{Fair Allocation problem}, or the \textit{Santa Claus problem}. The Min-Max allocation problem is the problem of Scheduling on Unrelated Parallel Machines, and is also known as the $R \, | \, | C_{\max}$ problem. In this paper, we are interested in instances of the problem that admit a Polynomial Time Approximation Scheme (PTAS). We show that an ordering property on the resources and the customers is important and paves the way for a PTAS. For the Max-Min allocation problem, we start with instances of the problem that can be viewed as a \textit{convex bipartite graph}; a bipartite graph for which there exists an ordering of the resources such that each customer is interested in (has a positive evaluation for) a set of \textit{consecutive} resources. We demonstrate a PTAS for the inclusion-free cases. This class of instances is equivalent to the class of bipartite permutation graphs. For the Min-Max allocation problem, we also obtain a PTAS for inclusion-free instances. These instances are not only of theoretical interest but also have practical applications.
Latent Tree Analysis
Venue: arXiv cs.LG 2016
Year: 2016

Latent tree analysis seeks to model the correlations among a set of random variables using a tree of latent variables. It was proposed as an improvement to latent class analysis --- a method widely used in social sciences and medicine to identify homogeneous subgroups in a population. It provides new and fruitful perspectives on a number of machine learning areas, including cluster analysis, topic detection, and deep probabilistic modeling. This paper gives an overview of the research on latent tree analysis and various ways it is used in practice.
Consistency Ensuring in Social Web Services Based on Commitments Structure
Venue: arXiv cs.SI 2016
Year: 2016

Web Service is one of the most significant current discussions in information sharing technologies and one of the examples of service oriented processing. To ensure accurate execution of web services operations, it must be adaptable with policies of the social networks in which it signs up. This adaptation implements using controls called 'Commitment'. This paper describes commitments structure and existing research about commitments and social web services, then suggests an algorithm for consistency of commitments in social web services. As regards the commitments may be executed concurrently, a key challenge in web services execution based on commitment structure is consistency ensuring in execution time. The purpose of this research is providing an algorithm for consistency ensuring between web services operations based on commitments structure.
Dithen: A Computation-as-a-Service Cloud Platform For Large-Scale Multimedia Processing
Venue: arXiv cs.DC 2016
Year: 2016

We present Dithen, a novel computation-as-a-service (CaaS) cloud platform specifically tailored to the parallel execution of large-scale multimedia tasks. Dithen handles the upload/download of both multimedia data and executable items, the assignment of compute units to multimedia workloads, and the reactive control of the available compute units to minimize the cloud infrastructure cost under deadline-abiding execution. Dithen combines three key properties: (i) the reactive assignment of individual multimedia tasks to available computing units according to availability and predetermined time-to-completion constraints; (ii) optimal resource estimation based on Kalman-filter estimates; (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of units servicing workloads. The deployment of Dithen over Amazon EC2 spot instances is shown to be capable of processing more than 80,000 video transcoding, face detection and image processing tasks (equivalent to the processing of more than 116 GB of compressed data) for less than $1 in billing cost from EC2. Moreover, the proposed AIMD-based control mechanism, in conjunction with the Kalman estimates, is shown to provide for more than 27% reduction in EC2 spot instance cost against methods based on reactive resource estimation. Finally, Dithen is shown to offer a 38% to 500% reduction of the billing cost against the current state-of-the-art in CaaS platforms on Amazon EC2 (Amazon Lambda and Amazon Autoscale). A baseline version of Dithen is currently available at http://www.dithen.com under the "AutoScale" option.
How Transferable are CNN-based Features for Age and Gender Classification?
Venue: arXiv cs.CV 2016
Year: 2016

Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for age and gender classification. The generic AlexNet-like architecture and domain specific VGG-Face CNN model are employed and fine-tuned with the Adience dataset prepared for age and gender classification in uncontrolled environments. In addition, task specific GilNet CNN model has also been utilized and used as a baseline method in order to compare with transferred models. Experimental results show that both transferred deep CNN models outperform the GilNet CNN model, which is the state-of-the-art age and gender classification approach on the Adience dataset, by an absolute increase of 7% and 4.5% in accuracy, respectively. This outcome indicates that transferring a deep CNN model can provide better classification performance than a task specific CNN model, which has a limited number of layers and trained from scratch using a limited amount of data as in the case of GilNet. Domain specific VGG-Face CNN model has been found to be more useful and provided better performance for both age and gender classification tasks, when compared with generic AlexNet-like model, which shows that transfering from a closer domain is more useful.
Real Stability Testing
Venue: arXiv cs.DS 2016
Year: 2016

We give a strongly polynomial time algorithm which determines whether or not a bivariate polynomial is real stable. As a corollary, this implies an algorithm for testing whether a given linear transformation on univariate polynomials preserves real-rootedness. The proof exploits properties of hyperbolic polynomials to reduce real stability testing to testing nonnegativity of a finite number of polynomials on an interval.
L-Convex Polyominoes are Recognizable in Real Time by 2D Cellular Automata
Venue: arXiv cs.FL 2016
Year: 2016

A polyomino is said to be L-convex if any two of its cells are connected by a 4-connected inner path that changes direction at most once. The 2-dimensional language representing such polyominoes has been recently proved to be recognizable by tiling systems by S. Brocchi, A. Frosini, R. Pinzani and S. Rinaldi. In an attempt to compare recognition power of tiling systems and cellular automata, we have proved that this language can be recognized by 2-dimensional cellular automata working on the von Neumann neighborhood in real time. Although the construction uses a characterization of L-convex polyominoes that is similar to the one used for tiling systems, the real time constraint which has no equivalent in terms of tilings requires the use of techniques that are specific to cellular automata.
Nonlinear Control of a Tethered UAV: the Taut Cable case
Venue: arXiv cs.SY 2016
Year: 2016

This paper focuses on the design of a stabilizing control law for an aerial vehicle which is physically connected to a ground station by means of a tether cable. By taking advantage of the tensile force acting along the taut cable, it is shown that the tethered UAV is able to maintain a non-zero attitude while hovering in a constant position. The control objective is to stabilize the desired configuration while simultaneously ensuring that the cable remains taut at all times. This leads to a nonlinear control problem subject to constraints. This paper provides a two-step solution. First, the system is stabilized using a cascade control scheme based on thrust vectoring. Then, constraint satisfaction is guaranteed using a novel Reference Governor scheme.
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
Venue: arXiv cs.LG 2016
Year: 2016

Recovery from the Flint Water Crisis has been hindered by uncertainty in both the water testing process and the causes of contamination. In this work, we develop an ensemble of predictive models to assess the risk of lead contamination in individual homes and neighborhoods. To train these models, we utilize a wide range of data sources, including voluntary residential water tests, historical records, and city infrastructure data. Additionally, we use our models to identify the most prominent factors that contribute to a high risk of lead contamination. In this analysis, we find that lead service lines are not the only factor that is predictive of the risk of lead contamination of water. These results could be used to guide the long-term recovery efforts in Flint, minimize the immediate damages, and improve resource-allocation decisions for similar water infrastructure crises.
Embedded Systems Security
Venue: arXiv cs.CR 2016
Year: 2016

Not long ago, it was thought that only software applications and general purpose digital systems i.e. computers were prone to various types of attacks against their security. The underlying hardware, hardware implementations of these software applications, embedded systems, and hardware devices were considered to be secure and out of reach of these attacks. However, during the previous few years, it has been demonstrated that novel attacks against the hardware and embedded systems can also be mounted. Not only viruses, but worms and Trojan horses have been developed for them, and they have also been demonstrated to be effective. Whereas a lot of research has already been done in the area of security of general purpose computers and software applications, hardware and embedded systems security is a relatively new and emerging area of research. This chapter provides details of various types of existing attacks against hardware devices and embedded systems, analyzes existing design methodologies for their vulnerability to new types of attacks, and along the way describes solutions and countermeasures against them for the design and development of secure systems.
Traversing Environments Using Possibility Graphs with Multiple Action Types
Venue: arXiv cs.RO 2016
Year: 2016

Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments. Footstep planners are typically only designed for one mode of locomotion, but traversing unfavorable environments may require several forms of locomotion to be sequenced together, such as walking, crawling, and jumping. Multi-modal motion planners can be used to address some of these problems, but existing implementations tend to be time-consuming and are limited to quasi-static actions. This paper presents a motion planning method to traverse complex environments using multiple categories of continuous actions. To this end, this paper formulates and exploits the Possibility Graph---which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions---to utilize lower-level single-action motion planners more effectively. We show that the Possibility Graph can quickly find routes through several different challenging environments which require various combinations of actions in order to traverse.
Multiuser Rate-Diverse Network-Coded Multiple Access
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents the first Network-Coded Multiple Access (NCMA) system with multiple users adopting different signal modulations, referred to as rate-diverse NCMA. A distinguishing feature of NCMA is the joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) to boost throughput of multipacket reception systems. In previous NCMA systems, users adopt the same modulation regardless of their individual channel conditions. This leads to suboptimal throughput for many practical scenarios, especially when different users have widely varying channel conditions. A rate-diverse NCMA system allows different users to use modulations that are commensurate with their channel conditions. A key challenge is the design of the PNC mapping and decoding mechanisms in NCMA when different users adopt different modulations. While there have been past work on non-channel-coded rate-diverse PNC, this paper is the first attempt to design channel-coded rate-diverse PNC to ensure the reliability of the overall NCMA system. Specifically, we put forth a symbol-splitting channel coding and modulation design so that PNC/NCMA can work over different modulations. We implemented our rate-diverse NCMA system on software-defined radios. Experimental results show that the throughput of rate-diverse NCMA can outperform the state-of-the-art rate-homogeneous NCMA by 80%. Overall, the introduction of rate diversity significantly boosts the NCMA system throughput in practical scenarios.