OpenRaiser/Intern-Atlas huggingface dataset:
Penta and Hexa Valued Representation of Neutrosophic Information
Venue: arXiv cs.AI 2016
Year: 2016

Starting from the primary representation of neutrosophic information, namely the degree of truth, degree of indeterminacy and degree of falsity, we define a nuanced representation in a penta valued fuzzy space, described by the index of truth, index of falsity, index of ignorance, index of contradiction and index of hesitation. Also, it was constructed an associated penta valued logic and then using this logic, it was defined for the proposed penta valued structure the following operators: union, intersection, negation, complement and dual. Then, the penta valued representation is extended to a hexa valued one, adding the sixth component, namely the index of ambiguity.
Sieve-based Coreference Resolution in the Biomedical Domain
Venue: arXiv cs.CL 2016
Year: 2016

We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution. Domain-general coreference resolution algorithms perform poorly on biomedical documents, because the cues they rely on such as gender are largely absent in this domain, and because they do not encode domain-specific knowledge such as the number and type of participants required in chemical reactions. Moreover, it is difficult to directly encode this knowledge into most coreference resolution algorithms because they are not rule-based. Our rule-based architecture uses sequentially applied hand-designed "sieves", with the output of each sieve informing and constraining subsequent sieves. This architecture provides a 3.2% increase in throughput to our Reach event extraction system with precision parallel to that of the stricter system that relies solely on syntactic patterns for extraction.
Grounding Recursive Aggregates: Preliminary Report
Venue: arXiv cs.AI 2016
Year: 2016

Problem solving in Answer Set Programming consists of two steps, a first grounding phase, systematically replacing all variables by terms, and a second solving phase computing the stable models of the obtained ground program. An intricate part of both phases is the treatment of aggregates, which are popular language constructs that allow for expressing properties over sets. In this paper, we elaborate upon the treatment of aggregates during grounding in Gringo series 4. Consequently, our approach is applicable to grounding based on semi-naive database evaluation techniques. In particular, we provide a series of algorithms detailing the treatment of recursive aggregates and illustrate this by a running example.
On the Capacity and Performance of Generalized Spatial Modulation
Venue: arXiv cs.IT 2016
Year: 2016

Generalized spatial modulation (GSM) uses $N$ antenna elements but fewer radio frequency (RF) chains ($R$) at the transmitter. Spatial modulation and spatial multiplexing are special cases of GSM with $R=1$ and $R=N$, respectively. In GSM, apart from conveying information bits through $R$ modulation symbols, information bits are also conveyed through the indices of the $R$ active transmit antennas. In this paper, we derive lower and upper bounds on the the capacity of a ($N,M,R$)-GSM MIMO system, where $M$ is the number of receive antennas. Further, we propose a computationally efficient GSM encoding (i.e., bits-to-signal mapping) method and a message passing based low-complexity detection algorithm suited for large-scale GSM-MIMO systems.
Robust Supervisors for Intersection Collision Avoidance in the Presence of Uncontrolled Vehicles
Venue: arXiv cs.SY 2016
Year: 2016

We present the design and validation of a centralized controller, called a supervisor, for collision avoidance of multiple human-driven vehicles at a road intersection, considering measurement errors, unmodeled dynamics, and uncontrolled vehicles. We design the supervisor to be least restrictive, that is, to minimize its interferences with human drivers. This performance metric is given a precise mathematical form by splitting the design process into two subproblems: verification problem and supervisor-design problem. The verification problem determines whether an input signal exists that makes controlled vehicles avoid collisions at all future times. The supervisor is designed such that if the verification problem returns yes, it allows the drivers' desired inputs; otherwise, it overrides controlled vehicles to prevent collisions. As a result, we propose exact and efficient supervisors. The exact supervisor solves the verification problem exactly but with combinatorial complexity. In contrast, the efficient supervisor solves the verification problem within a quantified approximation bound in polynomially bounded time with the number of controlled vehicles. We validate the performances of both supervisors through simulation and experimental testing.
Molecular MIMO: From Theory to Prototype
Venue: arXiv cs.ET 2016
Year: 2016

In diffusion-based molecular communication, information transport is governed by diffusion through a fluid medium. The achievable data rates for these channels are very low compared to the radio-based communication system, since diffusion can be a slow process. To improve the data rate, a novel multiple-input multiple-output (MIMO) design for molecular communication is proposed that utilizes multiple molecular emitters at the transmitter and multiple molecular detectors at the receiver (in RF communication these all correspond to antennas). Using particle-based simulators, the channel's impulse response is obtained and mathematically modeled. These models are then used to determine inter-link interference (ILI) and inter-symbol interference (ISI). It is assumed that when the receiver has incomplete information regarding the system and the channel state, low complexity symbol detection methods are preferred since the receiver is small and simple. Thus four detection algorithms are proposed---adaptive thresholding, practical zero forcing with channel models excluding/including the ILI and ISI, and Genie-aided zero forcing. The proposed algorithms are evaluated extensively using numerical and analytical evaluations.
Multimedia Channel Allocation in Cognitive Radio Networks using FDM-FDMA and OFDM-FDMA
Venue: arXiv cs.NI 2016
Year: 2016

In conventional wireless systems, unless a contiguous frequency band with width at least equal to the required bandwidth is obtained, multimedia communication can not be effected with the desired Quality of Service. We propose here a novel channel allocation technique to overcome this limitation in a Cognitive Radio Network which is based on utilizing several non-contiguous channels, each of width smaller than the required bandwidth, but whose sum equals at least the required bandwidth. We present algorithms for channel sensing, channel reservation and channel deallocation along with transmission and reception protocols with two different implementations based on $FDM-FDMA$ and $OFDM-FDMA$ techniques. Simulation results for both these implementations show that the proposed technique outperforms the existing first-fit and best-fit~\cite{b109, b110} allocation techniques in terms of the average number of attempts needed for acquiring the necessary number of channels for all traffic situations ranging from light to extremely heavy traffic. Further, the proposed technique can allocate the required numbers of channels in less than one second with $FDM-FDMA$ ($4.5$ second with $OFDM-FDMA$) even for $96\%$ traffic load, while the first-fit and best-fit techniques fail to allocate any channel in such situations.
Template Adaptation for Face Verification and Identification
Venue: arXiv cs.CV 2016
Year: 2016

Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos. In contrast, the newly released IJB-A face recognition dataset unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for template-based face verification and identification.
On Physical Web Browser
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we present the Physical Web Browser project for web applications depending on the environment. At this moment, many of users all over the world visit websites using their mobile devices only. Any mobile device (e.g., smartphone) has sensors to capture the environmental information. This information (context) could be analyzed and used within the web applications. There are, at least, two models for using this information. Firstly, we can use context information for data gathering requests in a web application. In this case, the output depends on the context. It is the classical model for context-aware data retrieval. In the second model, we can use context-aware data for improving user experience (for changing and tuning user interfaces). In other words, we present a way for the adaptation of web applications depending on the environment.
Space Bounds for Adaptive Renaming
Venue: arXiv cs.DC 2016
Year: 2016

We study the space complexity of implementing long-lived and one-shot adaptive renaming from multi-reader multi-writer registers, in an asynchronous distributed system with $n$ processes. As a result of an $f$-adaptive renaming algorithm each participating process gets a distinct name in the range $\{1,\dots,f(k)\}$ provided $k$ processes participate. Let $f: \{1,\dots,n\} \rightarrow \mathbb{N}$ be a non-decreasing function satisfying $f(1) \leq n-1$ and let $d = \max\{x ~|~ f(x) \leq n-1\}$. We show that any non-deterministic solo-terminating long-lived $f$-adaptive renaming object requires $d + 1$ registers. This implies a lower bound of $n-c$ registers for long-lived $(k+c)$-adaptive renaming, which we observe is tight. We also prove a lower bound of $\lfloor \frac{2(n - c)}{c+2} \rfloor$ registers for implementing any non-deterministic solo-terminating one-shot $(k+c)$-adaptive renaming. We provide two one-shot renaming algorithms: a wait-free algorithm and an obstruction-free algorithm. Each algorithm employs a parameter to depict the tradeoff between space and adaptivity. When these parameters are chosen appropriately, this results in a wait-free one-shot $(\frac{3k^2}{2})$-adaptive renaming algorithm from $\lceil \sqrt{n} \rceil + 1$ registers, and an obstruction-free one-shot $f$-adaptive renaming algorithm from only $\min\{n, x ~|~ f(x) \geq 2n\} + 1$ registers.
Indoor 5G 3GPP-like Channel Models for Office and Shopping Mall Environments
Venue: arXiv cs.IT 2016
Year: 2016

Future mobile communications systems are likely to be very different to those of today with new service innovations driven by increasing data traffic demand, increasing processing power of smart devices and new innovative applications. To meet these service demands the telecommunications industry is converging on a common set of 5G requirements which includes network speeds as high as 10 Gbps, cell edge rate greater than 100 Mbps, and latency of less than 1 msec. To reach these 5G requirements the industry is looking at new spectrum bands in the range up to 100 GHz where there is spectrum availability for wide bandwidth channels. For the development of new 5G systems to operate in bands up to 100 GHz there is a need for accurate radio propagation models which are not addressed by existing channel models developed for bands below 6 GHz. This paper presents a preliminary overview of the 5G channel models for bands up to 100 GHz in indoor offices and shopping malls, derived from extensive measurements across a multitude of bands. These studies have found some extensibility of the existing 3GPP models to the higher frequency bands up to 100 GHz. The measurements indicate that the smaller wavelengths introduce an increased sensitivity of the propagation models to the scale of the environment and show some frequency dependence of the path loss as well as increased occurrence of blockage. Further, the penetration loss is highly dependent on the material and tends to increase with frequency. The small-scale characteristics of the channel such as delay spread and angular spread and the multipath richness is somewhat similar over the frequency range, which is encouraging for extending the existing 3GPP models to the wider frequency range. Further work will be carried out to complete these models, but this paper presents the first steps for an initial basis for the model development.
Design and Implementation of an Improved Carry Increment Adder
Venue: arXiv cs.AR 2016
Year: 2016

A complex digital circuit comprises of adder as a basic unit. The performance of the circuit depends on the design of this basic adder unit. The speed of operation of a circuit is one of the important performance criteria of many digital circuits which ultimately depends on the delay of the basic adder unit. Many research works have been devoted in improving the delay of the adder circuit. In this paper we have proposed an improved carry increment adder (CIA) that improves the delay performance of the circuit. The improvement is achieved by incorporating carry look adder (CLA) in the design of CIA contrary to the previous design of CIA that employs ripple carry adder (RCA). A simulation study is carried out for comparative analysis. The coding is done in Verilog hardware description language (HDL) and the simulation is carried out in Xilinx ISE 13.1 environment.
SSSC-AM: A Unified Framework for Video Co-Segmentation by Structured Sparse Subspace Clustering with Appearance and Motion Features
Venue: arXiv cs.CV 2016
Year: 2016

Video co-segmentation refers to the task of jointly segmenting common objects appearing in a given group of videos. In practice, high-dimensional data such as videos can be conceptually thought as being drawn from a union of subspaces corresponding to categories rather than from a smooth manifold. Therefore, segmenting data into respective subspaces --- subspace clustering --- finds widespread applications in computer vision, including co-segmentation. State-of-the-art methods via subspace clustering seek to solve the problem in two steps: First, an affinity matrix is built from data, with appearance features or motion patterns. Second, the data are segmented by applying spectral clustering to the affinity matrix. However, this process is insufficient to obtain an optimal solution since it does not take into account the {\em interdependence} of the affinity matrix with the segmentation. In this work, we present a novel unified video co-segmentation framework inspired by the recent Structured Sparse Subspace Clustering ($\mathrm{S^{3}C}$) based on the {\em self-expressiveness} model. Our method yields more consistent segmentation results. In order to improve the detectability of motion features with missing trajectories due to occlusion or tracked points moving out of frames, we add an extra-dimensional signature to the motion trajectories. Moreover, we reformulate the $\mathrm{S^{3}C}$ algorithm by adding the affine subspace constraint in order to make it more suitable to segment rigid motions lying in affine subspaces of dimension at most $3$. Our experiments on MOViCS dataset show that our framework achieves the highest overall performance among baseline algorithms and demonstrate its robustness to heavy noise.
Visual Concept Recognition and Localization via Iterative Introspection
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional neural networks have been shown to develop internal representations, which correspond closely to semantically meaningful objects and parts, although trained solely on class labels. Class Activation Mapping (CAM) is a recent method that makes it possible to easily highlight the image regions contributing to a network's classification decision. We build upon these two developments to enable a network to re-examine informative image regions, which we term introspection. We propose a weakly-supervised iterative scheme, which shifts its center of attention to increasingly discriminative regions as it progresses, by alternating stages of classification and introspection. We evaluate our method and show its effectiveness over a range of several datasets, where we obtain competitive or state-of-the-art results: on Stanford-40 Actions, we set a new state-of the art of 81.74%. On FGVC-Aircraft and the Stanford Dogs dataset, we show consistent improvements over baselines, some of which include significantly more supervision.
The Runaway Rectangle Escape Problem
Venue: arXiv cs.CG 2016
Year: 2016

Motivated by the applications of routing in PCB buses, the Rectangle Escape Problem was recently introduced and studied. In this problem, we are given a set of rectangles $\mathcal{S}$ in a rectangular region $R$, and we would like to extend these rectangles to one of the four sides of $R$. Define the density of a point $p$ in $R$ as the number of extended rectangles that contain $p$. The question is then to find an extension with the smallest maximum density. We consider the problem of maximizing the number of rectangles that can be extended when the maximum density allowed is at most $d$. It is known that this problem is polynomially solvable for $d = 1$, and NP-hard for any $d \geq 2$. We consider approximation and exact algorithms for fixed values of $d$. We also show that a very special case of this problem, when all the rectangles are unit squares from a grid, continues to be NP-hard for $d = 2$.
Convergecast and Broadcast by Power-Aware Mobile Agents
Venue: arXiv cs.DC 2016
Year: 2016

A set of identical, mobile agents is deployed in a weighted network. Each agent has a battery -- a power source allowing it to move along network edges. An agent uses its battery proportionally to the distance traveled. We consider two tasks : convergecast, in which at the beginning, each agent has some initial piece of information, and information of all agents has to be collected by some agent; and broadcast in which information of one specified agent has to be made available to all other agents. In both tasks, the agents exchange the currently possessed information when they meet. The objective of this paper is to investigate what is the minimal value of power, initially available to all agents, so that convergecast or broadcast can be achieved. We study this question in the centralized and the distributed settings. In the centralized setting, there is a central monitor that schedules the moves of all agents. In the distributed setting every agent has to perform an algorithm being unaware of the network. In the centralized setting, we give a linear-time algorithm to compute the optimal battery power and the strategy using it, both for convergecast and for broadcast, when agents are on the line. We also show that finding the optimal battery power for convergecast or for broadcast is NP-hard for the class of trees. On the other hand, we give a polynomial algorithm that finds a 2-approximation for convergecast and a 4-approximation for broadcast, for arbitrary graphs. In the distributed setting, we give a 2-competitive algorithm for convergecast in trees and a 4-competitive algorithm for broadcast in trees. The competitive ratio of 2 is proved to be the best for the problem of convergecast, even if we only consider line networks. Indeed, we show that there is no (2 -- $ε$)-competitive algorithm for convergecast or for broadcast in the class of lines, for any $ε$ \textgreater{} 0.
Novel Speech Features for Improved Detection of Spoofing Attacks
Venue: arXiv cs.SD 2016
Year: 2016

Now-a-days, speech-based biometric systems such as automatic speaker verification (ASV) are highly prone to spoofing attacks by an imposture. With recent development in various voice conversion (VC) and speech synthesis (SS) algorithms, these spoofing attacks can pose a serious potential threat to the current state-of-the-art ASV systems. To impede such attacks and enhance the security of the ASV systems, the development of efficient anti-spoofing algorithms is essential that can differentiate synthetic or converted speech from natural or human speech. In this paper, we propose a set of novel speech features for detecting spoofing attacks. The proposed features are computed using alternative frequency-warping technique and formant-specific block transformation of filter bank log energies. We have evaluated existing and proposed features against several kinds of synthetic speech data from ASVspoof 2015 corpora. The results show that the proposed techniques outperform existing approaches for various spoofing attack detection task. The techniques investigated in this paper can also accurately classify natural and synthetic speech as equal error rates (EERs) of 0% have been achieved.
Summation Theory II: Characterizations of $\boldsymbol{RΠΣ^*}$-extensions and algorithmic aspects
Venue: arXiv cs.SC 2016
Year: 2016

Recently, $RΠΣ^*$-extensions have been introduced which extend Karr's $ΠΣ^*$-fields substantially: one can represent expressions not only in terms of transcendental sums and products, but one can work also with products over primitive roots of unity. Since one can solve the parameterized telescoping problem in such rings, covering as special cases the summation paradigms of telescoping and creative telescoping, one obtains a rather flexible toolbox for symbolic summation. This article is the continuation of this work. Inspired by Singer's Galois theory of difference equations we will work out several alternative characterizations of $RΠΣ^*$-extensions: adjoining naively sums and products leads to an $RΠΣ^*$-extension iff the obtained difference ring is simple iff the ring can be embedded into the ring of sequences iff the ring can be given by the interlacing of $ΠΣ^*$-extensions. From the viewpoint of applications this leads to a fully automatic machinery to represent indefinite nested sums and products in such $RΠΣ^*$-rings. In addition, we work out how the parameterized telescoping paradigm can be used to prove algebraic independence of indefinite nested sums. Furthermore, one obtains an alternative reduction tactic to solve the parameterized telescoping problem in basic $RΠΣ^*$-extensions exploiting the interlacing property.
Diversity in Object Proposals
Venue: arXiv cs.CV 2016
Year: 2016

Current top performing object recognition systems build on object proposals as a preprocessing step. Object proposal algorithms are designed to generate candidate regions for generic objects, yet current approaches are limited in capturing the vast variety of object characteristics. In this paper we analyze the error modes of the state-of-the-art Selective Search object proposal algorithm and suggest extensions to broaden its feature diversity in order to mitigate its error modes. We devise an edge grouping algorithm for handling objects without clear boundaries. To further enhance diversity, we incorporate the Edge Boxes proposal algorithm, which is based on fundamentally different principles than Selective Search. The combination of segmentations and edges provides rich image information and feature diversity which is essential for obtaining high quality object proposals for generic objects. For a preset amount of object proposals we achieve considerably better results by using our combination of different strategies than using any single strategy alone.