OpenRaiser/Intern-Atlas huggingface dataset:
Towards a Realistic Assessment of Multiple Antenna HCNs: Residual Additive Transceiver Hardware Impairments and Channel Aging
Venue: arXiv cs.IT 2017
Year: 2017

Given the critical dependence of broadcast channels by the accuracy of channel state information at the transmitter (CSIT), we develop a general downlink model with zero-forcing (ZF) precoding, applied in realistic heterogeneous cellular systems with multiple antenna base stations (BSs). Specifically, we take into consideration imperfect CSIT due to pilot contamination, channel aging due to users relative movement, and unavoidable residual additive transceiver hardware impairments (RATHIs). Assuming that the BSs are Poisson distributed, the main contributions focus on the derivations of the upper bound of the coverage probability and the achievable user rate for this general model. We show that both the coverage probability and the user rate are dependent on the imperfect CSIT and RATHIs. More concretely, we quantify the resultant performance loss of the network due to these effects. We depict that the uplink RATHIs have equal impact, but the downlink transmit BS distortion has a greater impact than the receive hardware impairment of the user. Thus, the transmit BS hardware should be of better quality than user's receive hardware. Furthermore, we characterise both the coverage probability and user rate in terms of the time variation of the channel. It is shown that both of them decrease with increasing user mobility, but after a specific value of the normalised Doppler shift, they increase again. Actually, the time variation, following the Jakes autocorrelation function, mirrors this effect on coverage probability and user rate. Finally, we consider space division multiple access (SDMA), single user beamforming (SU-BF), and baseline single-input single-output (SISO) transmission. A comparison among these schemes reveals that the coverage by means of SU-BF outperforms SDMA in terms of coverage.
Development of a passive Rehabilitation Robot for the wrist joint through the implementation of an Arduino UNO microcontroller
Venue: arXiv cs.RO 2017
Year: 2017

In this research was implemented the use of an Arduino UNO R3 microcontroller to control the movements of a prototype robotic functional developed to perform rehabilitation exercises in the wrist joint; This device can be used to assist the physiatrist to rehabilitate the tendinitis, synovitis, rheumatoid arthritis and for pre-operative and post-operative therapy in this joint. During the design stage of the functional prototype, the methodology of the industrial design process was used from a concurrent engineering approach, through which anthropometric studies could be performed related to the dimensions and angles of movement of the wrist joint in the population Venezuelan from the information collected, the design proposal was elaborated, and the use of CAD programs defined the different forms, geometries and materials of the components of the rehabilitation device, which were later analyzed using the finite element method for the determination The tensional state of efforts and safety factors through the use of CAE programs. In addition, a software was developed for the acquisition, registration, reproduction and execution of the different movements produced during the rehabilitation therapy. Through the research developed, a device was designed that will help the rehabilitation of the wrist joint allowing the combination of dorsal-palmar flexion and ulnar-radial movements to recover the joint function of various pathologies presented in the Venezuelan population.
SUT System Description for NIST SRE 2016
Venue: arXiv cs.SD 2017
Year: 2017

This paper describes the submission to fixed condition of NIST SRE 2016 by Sharif University of Technology (SUT) team. We provide a full description of the systems that were included in our submission. We start with an overview of the datasets that were used for training and development. It is followed by describing front-ends which contain different VAD and feature types. UBM and i-vector extractor training are the next details in this paper. As one of the important steps in system preparation, preconditioning the i-vectors are explained in more details. Then, we describe the classifier and score normalization methods. And finally, some results on SRE16 evaluation dataset are reported and analyzed.
Probabilistic Model Checking of Incomplete Models
Venue: arXiv cs.LO 2017
Year: 2017

It is crucial for accurate model checking that the model be a complete and faithful representation of the system. Unfortunately, this is not always possible, mainly because of two reasons: (i) the model is still under development and (ii) the correctness of implementation of some modules is not established. In such circumstances, is it still possible to get correct answers for some model checking queries? This paper is a step towards answering this question. We formulate this problem for the Discrete Time Markov Chains (DTMC) modeling formalism and the Probabilistic Computation Tree Logic (PCTL) query language. We then propose a simple solution by modifying DTMC and PCTL to accommodate three valued logic. The technique builds on existing model checking algorithms and tools, obviating the need for new ones to account for three valued logic. One of the most useful and popular techniques for modeling complex systems is through discrete event simulation. Discrete event simulators are essentially code in some programming language. We show an application of our approach on a piece of code that contains a module of unknown correctness. A preliminary version of this paper appears in the proceedings of Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques (ISoLA 2016), LNCS 9952, Springer. Keywords: Probabilistic models, Probabilistic Model checking Three-valued Logic, Discrete Time Markov Chain, Probabilistic Computation Tree Logic.
The Role of Gender in Social Network Organization
Venue: arXiv cs.CY 2017
Year: 2017

The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majority of studies focus on a single data stream in isolation. Here we use a dataset of high resolution data collected using mobile phones, as well as detailed questionnaires, to study gender differences in a large cohort. We consider mobility behavior and individual personality traits among a group of more than $800$ university students. We also investigate interactions among them expressed via person-to-person contacts, interactions on online social networks, and telecommunication. Thus, we are able to study the differences between male and female behavior captured through a multitude of channels for a single cohort. We find that while the two genders are similar in a number of aspects, there are robust deviations that include multiple facets of social interactions, suggesting the existence of inherent behavioral differences. Finally, we quantify how aspects of an individual's characteristics and social behavior reveals their gender by posing it as a classification problem. We ask: How well can we distinguish between male and female study participants based on behavior alone? Which behavioral features are most predictive?
A Mixture Model for Learning Multi-Sense Word Embeddings
Venue: arXiv cs.CL 2017
Year: 2017

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.
One Model To Learn Them All
Venue: arXiv cs.LG 2017
Year: 2017

Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.
The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge
Venue: arXiv cs.CV 2017
Year: 2017

This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge. The dataset used in this challenge is a large-scale benchmark for multi-label video classification. We extend the work in [1] and propose several improvements for frame sequence modeling. We propose a network structure called Chaining that can better capture the interactions between labels. Also, we report our approaches in dealing with multi-scale information and attention pooling. In addition, We find that using the output of model ensemble as a side target in training can boost single model performance. We report our experiments in bagging, boosting, cascade, and stacking, and propose a stacking algorithm called attention weighted stacking. Our final submission is an ensemble that consists of 74 sub models, all of which are listed in the appendix.
A Fully Trainable Network with RNN-based Pooling
Venue: arXiv cs.CV 2017
Year: 2017

Pooling is an important component in convolutional neural networks (CNNs) for aggregating features and reducing computational burden. Compared with other components such as convolutional layers and fully connected layers which are completely learned from data, the pooling component is still handcrafted such as max pooling and average pooling. This paper proposes a learnable pooling function using recurrent neural networks (RNN) so that the pooling can be fully adapted to data and other components of the network, leading to an improved performance. Such a network with learnable pooling function is referred to as a fully trainable network (FTN). Experimental results have demonstrated that the proposed RNN-based pooling can well approximate the existing pooling functions and improve the performance of the network. Especially for small networks, the proposed FTN can improve the performance by seven percentage points in terms of error rate on the CIFAR-10 dataset compared with the traditional CNN.
New Results on Edge Partitions of 1-plane Graphs
Venue: arXiv cs.DM 2017
Year: 2017

A $1$-plane graph is a graph embedded in the plane such that each edge is crossed at most once. A NIC-plane graph is a $1$-plane graph such that any two pairs of crossing edges share at most one end-vertex. An edge partition of a $1$-plane graph $G$ is a coloring of the edges of $G$ with two colors, red and blue, such that both the graph induced by the red edges and the graph induced by the blue edges are plane graphs. We prove the following: $(i)$ Every NIC-plane graph admits an edge partition such that the red graph has maximum vertex degree three; this bound on the vertex degree is worst-case optimal. $(ii)$ Deciding whether a $1$-plane graph admits an edge partition such that the red graph has maximum vertex degree two is NP-complete. $(iii)$ Deciding whether a $1$-plane graph admits an edge partition such that the red graph has maximum vertex degree one, and computing one in the positive case, can be done in quadratic time. Applications of these results to graph drawing are also discussed.
Improving Scalability of Inductive Logic Programming via Pruning and Best-Effort Optimisation
Venue: arXiv cs.AI 2017
Year: 2017

Inductive Logic Programming (ILP) combines rule-based and statistical artificial intelligence methods, by learning a hypothesis comprising a set of rules given background knowledge and constraints for the search space. We focus on extending the XHAIL algorithm for ILP which is based on Answer Set Programming and we evaluate our extensions using the Natural Language Processing application of sentence chunking. With respect to processing natural language, ILP can cater for the constant change in how we use language on a daily basis. At the same time, ILP does not require huge amounts of training examples such as other statistical methods and produces interpretable results, that means a set of rules, which can be analysed and tweaked if necessary. As contributions we extend XHAIL with (i) a pruning mechanism within the hypothesis generalisation algorithm which enables learning from larger datasets, (ii) a better usage of modern solver technology using recently developed optimisation methods, and (iii) a time budget that permits the usage of suboptimal results. We evaluate these improvements on the task of sentence chunking using three datasets from a recent SemEval competition. Results show that our improvements allow for learning on bigger datasets with results that are of similar quality to state-of-the-art systems on the same task. Moreover, we compare the hypotheses obtained on datasets to gain insights on the structure of each dataset.
From Propositional Logic to Plausible Reasoning: A Uniqueness Theorem
Venue: arXiv cs.AI 2017
Year: 2017

We consider the question of extending propositional logic to a logic of plausible reasoning, and posit four requirements that any such extension should satisfy. Each is a requirement that some property of classical propositional logic be preserved in the extended logic; as such, the requirements are simpler and less problematic than those used in Cox's Theorem and its variants. As with Cox's Theorem, our requirements imply that the extended logic must be isomorphic to (finite-set) probability theory. We also obtain specific numerical values for the probabilities, recovering the classical definition of probability as a theorem, with truth assignments that satisfy the premise playing the role of the "possible cases."
Set-Constrained Delivery Broadcast: Definition, Abstraction Power, and Computability Limits
Venue: arXiv cs.DC 2017
Year: 2017

This paper introduces a new communication abstraction, called Set-Constrained Delivery Broadcast (SCD-broadcast), whose aim is to provide its users with an appropriate abstraction level when they have to implement objects or distributed tasks in an asynchronous message-passing system prone to process crash failures. This abstraction allows each process to broadcast messages and deliver a sequence of sets of messages in such a way that, if a process delivers a set of messages including a message m and later delivers a set of messages including a message m ' , no process delivers first a set of messages including m ' and later a set of message including m. After having presented an algorithm implementing SCD-broadcast, the paper investigates its programming power and its computability limits. On the "power" side it presents SCD-broadcast-based algorithms, which are both simple and efficient, building objects (such as snapshot and conflict-free replicated data), and distributed tasks. On the "computability limits" side it shows that SCD-broadcast and read/write registers are computationally equivalent.
Precoded Chebyshev-NLMS based pre-distorter for nonlinear LED compensation in NOMA-VLC
Venue: arXiv cs.IT 2017
Year: 2017

Visible light communication (VLC) is one of the main technologies driving the future 5G communication systems due to its ability to support high data rates with low power consumption, thereby facilitating high speed green communications. To further increase the capacity of VLC systems, a technique called non-orthogonal multiple access (NOMA) has been suggested to cater to increasing demand for bandwidth, whereby users' signals are superimposed prior to transmission and detected at each user equipment using successive interference cancellation (SIC). Some recent results on NOMA exist which greatly enhance the achievable capacity as compared to orthogonal multiple access techniques. However, one of the performance-limiting factors affecting VLC systems is the nonlinear characteristics of a light emitting diode (LED). This paper considers the nonlinear LED characteristics in the design of pre-distorter for cognitive radio inspired NOMA in VLC, and proposes singular value decomposition based Chebyshev precoding to improve performance of nonlinear multiple-input multiple output NOMA-VLC. A novel and generalized power allocation strategy is also derived in this work, which is valid even in scenarios when users experience similar channels. Additionally, in this work, analytical upper bounds for the bit error rate of the proposed detector are derived for square $M$-quadrature amplitude modulation.
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation
Venue: arXiv cs.CV 2017
Year: 2017

Rapid and low power computation of optical flow (OF) is potentially useful in robotics. The dynamic vision sensor (DVS) event camera produces quick and sparse output, and has high dynamic range, but conventional OF algorithms are frame-based and cannot be directly used with event-based cameras. Previous DVS OF methods do not work well with dense textured input and are designed for implementation in logic circuits. This paper proposes a new block-matching based DVS OF algorithm which is inspired by motion estimation methods used for MPEG video compression. The algorithm was implemented both in software and on FPGA. For each event, it computes the motion direction as one of 9 directions. The speed of the motion is set by the sample interval. Results show that the Average Angular Error can be improved by 30\% compared with previous methods. The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11 clock cycles), 20 times faster than a Java software implementation running on a desktop PC. Sample data is shown that the method works on scenes dominated by edges, sparse features, and dense texture.
Rotation Invariance Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using those invariance.
dSDiVN: a distributed Software-Defined Networking architecture for Infrastructure-less Vehicular Networks
Venue: arXiv cs.NI 2017
Year: 2017

In the last few years, the emerging network architecture paradigm of Software-Defined Networking (SDN), has become one of the most important technology to manage large scale networks such as Vehicular Ad-hoc Networks (VANETs). Recently, several works have shown interest in the use of SDN paradigm in VANETs. SDN brings flexibility, scalability and management facility to current VANETs. However, almost all of proposed Software-Defined VANET (SDVN) architectures are infrastructure-based. This paper will focus on how to enable SDN in infrastructure-less vehicular environments. For this aim, we propose a novel distributed SDN-based architecture for uncovered infrastructure-less vehicular scenarios. It is a scalable cluster-based architecture with distributed mobile controllers and a reliable fall back recovery mechanism based on self-organized clustering and failure anticipation.
Secure and Private Cloud Storage Systems with Random Linear Fountain Codes
Venue: arXiv cs.IT 2017
Year: 2017

An information theoretic approach to security and privacy called Secure And Private Information Retrieval (SAPIR) is introduced. SAPIR is applied to distributed data storage systems. In this approach, random combinations of all contents are stored across the network. Our coding approach is based on Random Linear Fountain (RLF) codes. To retrieve a content, a group of servers collaborate with each other to form a Reconstruction Group (RG). SAPIR achieves asymptotic perfect secrecy if at least one of the servers within an RG is not compromised. Further, a Private Information Retrieval (PIR) scheme based on random queries is proposed. The PIR approach ensures the users privately download their desired contents without the servers knowing about the requested contents indices. The proposed scheme is adaptive and can provide privacy against a significant number of colluding servers.
Joint resource allocation in SWIPT-based multi-antenna decode-and-forward relay networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider relay-assisted simultaneous wireless information and power transfer (SWIPT) for two-hop cooperative transmission, where a half-duplex multi-antenna relay adopts decode-and-forward (DF) relaying strategy for information forwarding. The relay is assumed to be energy-free and needs to harvest energy from the source node. By embedding power splitting (PS) at each relay antenna to coordinate the received energy and information, joint problem of determining PS ratios and power allocation at the multi-antenna relay node is formulated to maximize the end-to-end achievable rate. We show that the multi-antenna relay is equivalent to a virtual single-antenna relay in such a SWIPT system, and the problem is optimally solved with closed-form. To reduce the hardware cost of the PS scheme, we further propose the antenna clustering scheme, where the multiple antennas at the relay are partitioned into two disjoint groups which are exclusively used for information decoding and energy harvesting, respectively. Optimal clustering algorithm is first proposed but with exponential complexity. Then a greedy clustering algorithms is introduced with linear complexity and approaching to the optimal performance. Several valuable insights are provided via theoretical analysis and simulation results.
The impact of Entropy and Solution Density on selected SAT heuristics
Venue: arXiv cs.AI 2017
Year: 2017

In a recent article [Oh'15], Oh examined the impact of various key heuristics (e.g., deletion strategy, restart policy, decay factor, database reduction) in competitive SAT solvers. His key findings are that their expected success depends on whether the input formula is satisfiable or not. To further investigate these findings, we focused on two properties of satisfiable formulas: the entropy of the formula, which approximates the freedom we have in assigning the variables, and the solution density, which is the number of solutions divided by the search space. We found that both predict better the effect of these heuristics, and that satisfiable formulas with small entropy `behave' similarly to unsatisfiable formulas.