OpenRaiser/Intern-Atlas huggingface dataset:
A simple and efficient feedback control strategy for wastewater denitrification
Venue: arXiv cs.SY 2017
Year: 2017

Due to severe mathematical modeling and calibration difficulties open-loop feedforward control is mainly employed today for wastewater denitrification, which is a key ecological issue. In order to improve the resulting poor performances a new model-free control setting and its corresponding "intelligent" controller are introduced. The pitfall of regulating two output variables via a single input variable is overcome by introducing also an open-loop knowledge-based control deduced from the plant behavior. Several convincing computer simulations are presented and discussed.
Deducing the severity of psychiatric symptoms from the human voice
Venue: arXiv cs.SD 2017
Year: 2017

Psychiatric illnesses are often associated with multiple symptoms, whose severity must be graded for accurate diagnosis and treatment. This grading is usually done by trained clinicians based on human observations and judgments made within doctor-patient sessions. Current research provides sufficient reason to expect that the human voice may carry biomarkers or signatures of many, if not all, these symptoms. Based on this conjecture, we explore the possibility of objectively and automatically grading the symptoms of psychiatric illnesses with reference to various standard psychiatric rating scales. Using acoustic data from several clinician-patient interviews within hospital settings, we use non-parametric models to learn and predict the relations between symptom-ratings and voice. In the process, we show that different articulatory-phonetic units of speech are able to capture the effects of different symptoms differently, and use this to establish a plausible methodology that could be employed for automatically grading psychiatric symptoms for clinical purposes.
Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy serveral merits. Firstly, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable biometrics.
Measuring Cognitive Conflict in Virtual Reality with Feedback-Related Negativity
Venue: arXiv cs.HC 2017
Year: 2017

As virtual reality (VR) emerges as a mainstream platform, designers have started to experiment new interaction techniques to enhance the user experience. This is a challenging task because designers not only strive to provide designs with good performance but also carefully ensure not to disrupt users' immersive experience. There is a dire need for a new evaluation tool that extends beyond traditional quantitative measurements to assist designers in the design process. We propose an EEG-based experiment framework that evaluates interaction techniques in VR by measuring intentionally elicited cognitive conflict. Through the analysis of the feedback-related negativity (FRN) as well as other quantitative measurements, this framework allows designers to evaluate the effect of the variables of interest. We studied the framework by applying it to the fundamental task of 3D object selection using direct 3D input, i.e. tracked hand in VR. The cognitive conflict is intentionally elicited by manipulating the selection radius of the target object. Our first behavior experiment validated the framework in line with the findings of conflict-induced behavior adjustments like those reported in other classical psychology experiment paradigms. Our second EEG-based experiment examines the effect of the appearance of virtual hands. We found that the amplitude of FRN correlates with the level of realism of the virtual hands, which concurs with the Uncanny Valley theory.
Multi-User Millimeter Wave Channel Estimation Using Generalized Block OMP Algorithm
Venue: arXiv cs.IT 2017
Year: 2017

In a multi-user millimeter (mm) wave communication system, we consider the problem of estimating the channel response between the central node (base station) and each of the user equipments (UE). We propose three different strategies: 1) Each UE estimates its channel separately, 2) Base station estimates all the UEs channels jointly, and 3) Two stage process with estimation done at both UE and base station. Exploiting the low rank nature of the mm wave channels, we propose a generalized block orthogonal matching pursuit (G.BOMP) framework for channel estimation in all the three strategies. Our simulation results show that, the average beamforming gain of the G.BOMP algorithm is higher than that of the conventional OMP algorithm and other existing works on the multi-user mm wave system.
Empirical Analysis on Comparing the Performance of Alpha Miner Algorithm in SQL Query Language and NoSQL Column-Oriented Databases Using Apache Phoenix
Venue: arXiv cs.DB 2017
Year: 2017

Process-Aware Information Systems (PAIS) is an IT system that support business processes and generate large amounts of event logs from the execution of business processes. An event log is represented as a tuple of CaseID, Timestamp, Activity and Actor. Process Mining is a new and emerging field that aims at analyzing the event logs to discover, enhance and improve business processes and check conformance between run time and design time business processes. The large volume of event logs generated are stored in the databases. Relational databases perform well for a certain class of applications. However, there are a certain class of applications for which relational databases are not able to scale. To handle such class of applications, NoSQL database systems emerged. Discovering a process model (workflow model) from event logs is one of the most challenging and important Process Mining task. The $α$-miner algorithm is one of the first and most widely used Process Discovery technique. Our objective is to investigate which of the databases (Relational or NoSQL) performs better for a Process Discovery application under Process Mining. We implement the $α$-miner algorithm on relational (row-oriented) and NoSQL (column-oriented) databases in database query languages so that our algorithm is tightly coupled to the database. We present a performance benchmarking and comparison of the $α$-miner algorithm on row-oriented database and NoSQL column-oriented database so that we can compare which database can efficiently store massive event logs and analyze it in seconds to discover a process model.
VieM v1.00 -- Vienna Mapping and Sparse Quadratic Assignment User Guide
Venue: arXiv cs.DC 2017
Year: 2017

This paper severs as a user guide to the mapping framework VieM (Vienna Mapping and Sparse Quadratic Assignment). We give a rough overview of the techniques used within the framework and describe the user interface as well as the file formats used.
Treating Smoothness and Balance during Data Exchange in Explicit Simulator Coupling or Cosimulation
Venue: arXiv cs.CE 2017
Year: 2017

Cosimulation methods allow combination of simulation tools of physical systems running in parallel to act as a single simulation environment for a big system. As data is passed across subsystem boundaries instead of solving the system as one single equation system, it is not ensured that systemwide balances are fulfilled. If the exchanged data is a flow of a conserved quantity, approximation errors can accumulate and make simulation results inaccurate. The problem of approximation errors is typically addressed with extrapolation of exchanged data. Nevertheless balance errors occur as extrapolation is approximation. This problem can be handled with balance correction methods which compensate these errors by adding corrections for the balances to the signal in next coupling time step. This work aims at combining extrapolation of exchanged data and balance correction in a way that the exchanged signal not only remains smooth, meaning the existence of continuous derivatives, but even in a way reducing the derivatives, in order to avoid unphysical dynamics caused by the coupling. To this end, suitable switch and hat functions are constructed and applied to the problem.
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Venue: arXiv cs.CV 2017
Year: 2017

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.
Concatenated LDPC-Polar Codes Decoding Through Belief Propagation
Venue: arXiv cs.IT 2017
Year: 2017

Owing to their capacity-achieving performance and low encoding and decoding complexity, polar codes have drawn much research interests recently. Successive cancellation decoding (SCD) and belief propagation decoding (BPD) are two common approaches for decoding polar codes. SCD is sequential in nature while BPD can run in parallel. Thus BPD is more attractive for low latency applications. However BPD has some performance degradation at higher SNR when compared with SCD. Concatenating LDPC with Polar codes is one popular approach to enhance the performance of BPD , where a short LDPC code is used as an outer code and Polar code is used as an inner code. In this work we propose a new way to construct concatenated LDPC-Polar code, which not only outperforms conventional BPD and existing concatenated LDPC-Polar code but also shows a performance improvement of 0.5 dB at higher SNR regime when compared with SCD.
Ultimate Positivity of Diagonals of Quasi-rational Functions
Venue: arXiv cs.SC 2017
Year: 2017

The problem to decide whether a given multivariate (quasi-)rational function has only positive coefficients in its power series expansion has a long history. It dates back to Szego in 1933 who showed certain quasi-rational function to be positive, in the sense that all the series coefficients are positive, using an involved theory of special functions. In contrast to the simplicity of the statement, the method was surprisingly difficult. This dependency motivated further research for positivity of (quasi-)rational functions. More and more (quasi-)rational functions have been proven to be positive, and some of the proofs are even quite simple. However, there are also others whose positivity are still open conjectures. In this talk, we focus on a less difficult but also interesting question to decide whether the diagonal of a given quasi-rational function is ultimately positive, especially for the one conjectured to be positive by Kauers in 2007. To solve this question, it suffices to compute the asymptotics of the diagonal coefficients, which can be done by the multivariate singularity analysis developed by Baryshnikov, Pemantle and Wilson. Note that the ultimate positivity is a necessary condition for the positivity, and therefore can be used to either exclude the nonpositive cases or further support the conjectural positivity.
Multimodal Language Specification for Human Adaptive Mechatronics
Venue: arXiv cs.HC 2017
Year: 2017

Designing and building automated systems with which people can interact naturally is one of the emerging objective of Mechatronics. In this perspective multimodality and adaptivity represent focal issues, enabling users to communicate more freely and naturally with automated systems. One of the basic problem of multimodal interaction is the fusion process. Current approaches to fusion are mainly two: the former implements the multimodal fusion at dialogue management level, whereas the latter at grammar level. In this paper, we propose a multimodal attribute grammar, that provides constructions both for representing input symbols from different modalities and for modeling semantic and temporal features of multimodal input symbols, enabling the specification of multimodal languages. Moreover, an application of the proposed approach in the context of a multimodal language specification to control a driver assistance system, as robots using different integrated interaction modalities, is given.
Enhancing Coexistence in the Unlicensed Band with Massive MIMO
Venue: arXiv cs.IT 2017
Year: 2017

We consider cellular base stations (BSs) equipped with a large number of antennas and operating in the unlicensed band. We denote such system as massive MIMO unlicensed (mMIMO-U). We design the key procedures required to guarantee coexistence between a cellular BS and nearby Wi-Fi devices. These include: neighboring Wi-Fi channel covariance estimation, allocation of spatial degrees of freedom for interference suppression, and enhanced channel sensing and data transmission phases. We evaluate the performance of the so-designed mMIMO-U, showing that it allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. The same is not true for conventional listen-before-talk (LBT) operations. As a result, mMIMO-U boosts the aggregate cellular-plus-Wi-Fi data rate in the unlicensed band with respect to conventional LBT, exhibiting increasing gains as the number of BS antennas grows.
Simultaneous Wireless Information and Power Transfer in MISO Full-Duplex Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates a multiuser multiple-input single-output (MISO) full-duplex (FD) system for simultaneous wireless information and power transfer (SWIPT), in which a multi-antenna base station (BS) simultaneously sends wirelessly information and power to a set of single-antenna mobile stations (MSs) using power splitters (PSs) in the downlink and receives information in the uplink in FD mode. In particular, we address the joint design of the receive PS ratio and the transmit power at the MSs, and the beamforming matrix at the BS under signal-to-interference-plus-noise ratio (SINR) and the harvested power constraints. Using semidefinite relaxation (SDR), we obtain the solution to the problem with imperfect channel state information (CSI) of the self-interfering channels. Furthermore, we propose another suboptimal zero-forcing (ZF) based solution by separating the optimization of the transmit beamforming vector and the PS ratio. Simulation results are provided to evaluate the performance of the proposed beamforming designs.
Reservoir Computing and Extreme Learning Machines using Pairs of Cellular Automata Rules
Venue: arXiv cs.NE 2017
Year: 2017

A framework for implementing reservoir computing (RC) and extreme learning machines (ELMs), two types of artificial neural networks, based on 1D elementary Cellular Automata (CA) is presented, in which two separate CA rules explicitly implement the minimum computational requirements of the reservoir layer: hyperdimensional projection and short-term memory. CAs are cell-based state machines, which evolve in time in accordance with local rules based on a cells current state and those of its neighbors. Notably, simple single cell shift rules as the memory rule in a fixed edge CA afforded reasonable success in conjunction with a variety of projection rules, potentially significantly reducing the optimal solution search space. Optimal iteration counts for the CA rule pairs can be estimated for some tasks based upon the category of the projection rule. Initial results support future hardware realization, where CAs potentially afford orders of magnitude reduction in size, weight, and power (SWaP) requirements compared with floating point RC implementations.
Implementing the Availability Model of a Software-Defined Backbone Network in Möbius
Venue: arXiv cs.NI 2017
Year: 2017

Software-defined networking (SDN) promises to improve the programmability and flexibility of networks, but it may bring also new challenges that need to be explored. One open issue is the quantitative assessment of the properties of SDN backbone networks to determine whether they can provide similar availability to the traditional IP backbone networks. To achieve this goal, a two-level availability model that is able to capture the global network connectivity without neglecting the essential details and which includes a failure correlation assessment should be considered. The two-level availability model is composed by a structural model and the dynamic models of the principal minimal-cut sets of the network. The purpose of this technical report is to extensively present the implementation on Möbius of the Stochastic Activity Network (SAN) availability model of the network elements and the principal minimal-cut sets of a SDN backbone network and the corresponding traditional backbone network.
Robust Power System Dynamic State Estimator with Non-Gaussian Measurement Noise: Part II--Implementation and Results
Venue: arXiv cs.SY 2017
Year: 2017

This paper is the second of a two-part series that discusses the implementation issues and test results of a robust Unscented Kalman Filter (UKF) for power system dynamic state estimation with non-Gaussian synchrophasor measurement noise. The tuning of the parameters of our Generalized Maximum-Likelihood-type robust UKF (GM-UKF) is presented and discussed in a systematic way. Using simulations carried out on the IEEE 39-bus system, its performance is evaluated under different scenarios, including i) the occurrence of two different types of noises following thick-tailed distributions, namely the Laplace or Cauchy probability distributions for real and reactive power measurements; ii) the occurrence of observation and innovation outliers; iii) the occurrence of PMU measurement losses due to communication failures; iv) cyber attacks; and v) strong system nonlinearities. It is also compared to the UKF and the Generalized Maximum-Likelihood-type robust iterated EKF (GM-IEKF). Simulation results reveal that the GM-UKF outperforms the GM-IEKF and the UKF in all scenarios considered. In particular, when the system is operating under stressed conditions, inducing system nonlinearities, the GM-IEKF and the UKF diverge while our GM-UKF does converge. In addition, when the power measurement noises obey a Cauchy distribution, our GM-UKF converges to a state estimate vector that exhibits a much higher statistical efficiency than that of the GM-IEKF; by contrast, the UKF fails to converge. Finally, potential applications and future work of the proposed GM-UKF are discussed in concluding remarks section.
The Ebb and Flow of Controversial Debates on Social Media
Venue: arXiv cs.SI 2017
Year: 2017

We explore how the polarization around controversial topics evolves on Twitter - over a long period of time (2011 to 2016), and also as a response to major external events that lead to increased related activity. We find that increased activity is typically associated with increased polarization; however, we find no consistent long-term trend in polarization over time among the topics we study.
Semi-Supervised Deep Learning for Fully Convolutional Networks
Venue: arXiv cs.CV 2017
Year: 2017

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.
Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
Venue: arXiv cs.AI 2017
Year: 2017

We discuss the computational complexity of approximating maximum a posteriori inference in sum-product networks. We first show NP-hardness in trees of height two by a reduction from maximum independent set; this implies non-approximability within a sublinear factor. We show that this is a tight bound, as we can find an approximation within a linear factor in networks of height two. We then show that, in trees of height three, it is NP-hard to approximate the problem within a factor $2^{f(n)}$ for any sublinear function $f$ of the size of the input $n$. Again, this bound is tight, as we prove that the usual max-product algorithm finds (in any network) approximations within factor $2^{c \cdot n}$ for some constant $c < 1$. Last, we present a simple algorithm, and show that it provably produces solutions at least as good as, and potentially much better than, the max-product algorithm. We empirically analyze the proposed algorithm against max-product using synthetic and realistic networks.
An FPTAS for the Knapsack Problem with Parametric Weights
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we investigate the parametric weight knapsack problem, in which the item weights are affine functions of the form $w_i(λ) = a_i + λ\cdot b_i$ for $i \in \{1,\ldots,n\}$ depending on a real-valued parameter $λ$. The aim is to provide a solution for all values of the parameter. It is well-known that any exact algorithm for the problem may need to output an exponential number of knapsack solutions. We present the first fully polynomial-time approximation scheme (FPTAS) for the problem that, for any desired precision $\varepsilon \in (0,1)$, computes $(1-\varepsilon)$-approximate solutions for all values of the parameter. Our FPTAS is based on two different approaches and achieves a running time of $\mathcal{O}(n^3/\varepsilon^2 \cdot \min\{ \log^2 P, n^2 \} \cdot \min\{\log M, n \log (n/\varepsilon) / \log(n \log (n/\varepsilon) )\})$ where $P$ is an upper bound on the optimal profit and $M := \max\{W, n \cdot \max\{a_i,b_i: i \in \{1,\ldots,n\}\}\}$ for a knapsack with capacity $W$.