OpenRaiser/Intern-Atlas huggingface dataset:
Coverage in Downlink Heterogeneous mmWave Cellular Networks with User-Centric Small Cell Deployment
Venue: arXiv cs.IT 2017
Year: 2017

A K-tier heterogeneous downlink millimeter wave (mmWave) cellular network with user-centric small cell deployments is studied in this paper. In particular, we consider a heterogeneous network model with user equipments (UEs) being distributed according to a Poisson Cluster Process (PCP). Specifically, we address two cluster processes, namely (i) Thomas cluster process, where the UEs are clustered around the base stations (BSs) and the distances between UEs and the BS are modeled as Gaussian distributed, and (ii) Matern cluster process, where the UEs are scattered according to a uniform distribution. In addition, distinguishing features of mmWave communications including directional beamforming and a sophisticated path loss model incorporating both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions, are taken into account. Initially, the complementary cumulative distribution function (CCDF) and probability density function (PDF) of path loss are provided. Subsequently, using tools from stochastic geometry, we derive a general expression for the signal-to-interference-plus-noise ratio (SINR) coverage probability. Our results demonstrate that coverage probability can be improved by decreasing the size of UE clusters around BSs, decreasing the beamwidth of the main lobe, or increasing the main lobe directivity gain. Moreover, interference has noticeable influence on the coverage performance of our model. We also show that better coverage performance is achieved in the presence of clustered users compared to the case in which the users are distributed according to a Poisson Point Process (PPP).
A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization
Venue: arXiv cs.CL 2017
Year: 2017

We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.
On the Performance of NOMA-Based Cooperative Relaying Systems over Rician Fading Channels
Venue: arXiv cs.IT 2017
Year: 2017

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation (5G) wireless communications. As users with good channel conditions can serve as relays to enhance the system performance by using successive interference cancellation (SIC), the integration of NOMA and cooperative relaying has recently attracted increasing interests. In this paper, a NOMA-based cooperative relaying system is studied, and an analytical framework is developed to evaluate its performance. Specifically, the performance of NOMA over Rician fading channels is studied, and the exact expression of the average achievable rate is derived. Moreover, we also propose an approximation method to calculate the achievable rate by using the Gauss-Chebyshev Integration. Numerical results confirm that our derived analytical results match well with the Monte Carlo simulations.
Joint DOA Estimation and Array Calibration Using Multiple Parametric Dictionary Learning
Venue: arXiv cs.LG 2017
Year: 2017

This letter proposes a multiple parametric dictionary learning algorithm for direction of arrival (DOA) estimation in presence of array gain-phase error and mutual coupling. It jointly solves both the DOA estimation and array imperfection problems to yield a robust DOA estimation in presence of array imperfection errors and off-grid. In the proposed method, a multiple parametric dictionary learning-based algorithm with an steepest-descent iteration is used for learning the parametric perturbation matrices and the steering matrix simultaneously. It also exploits the multiple snapshots information to enhance the performance of DOA estimation. Simulation results show the efficiency of the proposed algorithm when both off-grid problem and array imperfection exist.
Speeding up finite-time consensus via minimal polynomial of a weighted graph - a numerical approach
Venue: arXiv cs.SY 2017
Year: 2017

Reaching consensus among states of a multi-agent system is a key requirement for many distributed control/optimization problems. Such a consensus is often achieved using the standard Laplacian matrix (for continuous system) or Perron matrix (for discrete-time system). Recent interest in speeding up consensus sees the development of finite-time consensus algorithms. This work proposes an approach to speed up finite-time consensus algorithm using the weights of a weighted Laplacian matrix. The approach is an iterative procedure that finds a low-order minimal polynomial that is consistent with the topology of the underlying graph. In general, the lowest-order minimal polynomial achievable for a network system is an open research problem. This work proposes a numerical approach that searches for the lowest order minimal polynomial via a rank minimization problem using a two-step approach: the first being an optimization problem involving the nuclear norm and the second a correction step. Several examples are provided to illustrate the effectiveness of the approach.
Wireless Powered Cooperative Jamming for Secure OFDM System
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies the secrecy communication in an orthogonal frequency division multiplexing (OFDM) system, where a source sends confidential information to a destination in the presence of a potential eavesdropper. We employ wireless powered cooperative jamming to improve the secrecy rate of this system with the assistance of a cooperative jammer, which works in the harvest-then-jam protocol over two time-slots. In the first slot, the source sends dedicated energy signals to power the jammer; in the second slot, the jammer uses the harvested energy to jam the eavesdropper, in order to protect the simultaneous secrecy communication from the source to the destination. In particular, we consider two types of receivers at the destination, namely Type-I and Type-II receivers, which do not have and have the capability of canceling the (a-priori known) jamming signals, respectively. For both types of receivers, we maximize the secrecy rate at the destination by jointly optimizing the transmit power allocation at the source and the jammer over sub-carriers, as well as the time allocation between the two time-slots. First, we present the globally optimal solution to this problem via the Lagrange dual method, which, however, is of high implementation complexity. Next, to balance tradeoff between the algorithm complexity and performance, we propose alternative low-complexity solutions based on minorization maximization and heuristic successive optimization, respectively. Simulation results show that the proposed approaches significantly improve the secrecy rate, as compared to benchmark schemes without joint power and time allocation.
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction
Venue: arXiv cs.CV 2017
Year: 2017

Offline handwritten text line recognition is a hard task that requires both an efficient optical character recognizer and language model. Handwriting recognition state of the art methods are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN) coupled with the use of linguistic knowledge. Most of the proposed approaches in the literature focus on improving one of the two components and use constraint, dedicated to a database lexicon. However, state of the art performance is achieved by combining multiple optical models, and possibly multiple language models with the Recognizer Output Voting Error Reduction (ROVER) framework. Though handwritten line recognition with ROVER has been implemented by combining only few recognizers because training multiple complete recognizers is hard. In this paper we propose a Lexicon Verified ROVER: LV-ROVER, that has a reduce complexity compare to the original one and that can combine hundreds of recognizers without language models. We achieve state of the art for handwritten line text on the RIMES dataset.
Intelligent Vehicle-Trust Point: Reward based Intelligent Vehicle Communication using Blockchain
Venue: arXiv cs.CR 2017
Year: 2017

The Intelligent vehicle (IV) is experiencing revolutionary growth in research and industry, but it still suffers from many security vulnerabilities. Traditional security methods are incapable to provide secure IV communication. The major issues in IV communication, are trust, data accuracy and reliability of communication data in the communication channel. Blockchain technology works for the crypto currency, Bit-coin, which is recently used to build trust and reliability in peer-to-peer networks having similar topologies as IV Communication. In this paper, we are proposing, Intelligent Vehicle-Trust Point (IV-TP) mechanism for IV communication among IVs using Blockchain technology. The IVs communicated data provides security and reliability using our proposed IV-TP. Our IV-TP mechanism provides trustworthiness for vehicles behavior, and vehicles legal and illegal action. Our proposal presents a reward based system, an exchange of some IV-TP among IVs, during successful communication. For the data management of the IV-TP, we are using blockchain technology in the intelligent transportation system (ITS), which stores all IV-TP details of every vehicle and is accessed ubiquitously by IVs. In this paper, we evaluate our proposal with the help of intersection use case scenario for intelligent vehicles communication.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
Venue: arXiv cs.IR 2017
Year: 2017

List-wise learning to rank methods are considered to be the state-of-the-art. One of the major problems with these methods is that the ambiguous nature of relevance labels in learning to rank data is ignored. Ambiguity of relevance labels refers to the phenomenon that multiple documents may be assigned the same relevance label for a given query, so that no preference order should be learned for those documents. In this paper we propose a novel sampling technique for computing a list-wise loss that can take into account this ambiguity. We show the effectiveness of the proposed method by training a 3-layer deep neural network. We compare our new loss function to two strong baselines: ListNet and ListMLE. We show that our method generalizes better and significantly outperforms other methods on the validation and test sets.
'Dark Germany': Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing
Venue: arXiv cs.SI 2017
Year: 2017

The political discourse in Western European countries such as Germany has recently seen a resurgence of the topic of refugees, fueled by an influx of refugees from various Middle Eastern and African countries. Even though the topic of refugees evidently plays a large role in online and offline politics of the affected countries, the fact that protests against refugees stem from the right-wight political spectrum has lead to corresponding media to be shared in a decentralized fashion, making an analysis of the underlying social and mediatic networks difficult. In order to contribute to the analysis of these processes, we present a quantitative study of the social media activities of a contemporary nationwide protest movement against local refugee housing in Germany, which organizes itself via dedicated Facebook pages per city. We analyse data from 136 such protest pages in 2015, containing more than 46,000 posts and more than one million interactions by more than 200,000 users. In order to learn about the patterns of communication and interaction among users of far-right social media sites and pages, we investigate the temporal characteristics of the social media activities of this protest movement, as well as the connectedness of the interactions of its participants. We find several activity metrics such as the number of posts issued, discussion volume about crime and housing costs, negative polarity in comments, and user engagement to peak in late 2015, coinciding with chancellor Angela Merkel's much criticized decision of September 2015 to temporarily admit the entry of Syrian refugees to Germany. Furthermore, our evidence suggests a low degree of direct connectedness of participants in this movement, (i.a., indicated by a lack of geographical collaboration patterns), yet we encounter a strong affiliation of the pages' user base with far-right political parties.
Technical Report: Artificial DNA - a Concept for Self-Building Embedded Systems
Venue: arXiv cs.ET 2017
Year: 2017

This technical report deals with the concept of an artificial DNA which contains a blueprint of the structure and organization of an embedded system. This blueprint can be used to build up the embedded system in a self-organizing manner at run-time. The report describes in detail the basic principles of the the artificial DNA and its relationship to standard design methods for embedded systems. A prototypic implementation is presented and evaluated. Additionally, future work is described and a conclusion is given.
Controlling a remotely located Robot using Hand Gestures in real time: A DSP implementation
Venue: arXiv cs.HC 2017
Year: 2017

Telepresence is a necessity for present time as we can't reach everywhere and also it is useful in saving human life at dangerous places. A robot, which could be controlled from a distant location, can solve these problems. This could be via communication waves or networking methods. Also controlling should be in real time and smooth so that it can actuate on every minor signal in an effective way. This paper discusses a method to control a robot over the network from a distant location. The robot was controlled by hand gestures which were captured by the live camera. A DSP board TMS320DM642EVM was used to implement image pre-processing and fastening the whole system. PCA was used for gesture classification and robot actuation was done according to predefined procedures. Classification information was sent over the network in the experiment. This method is robust and could be used to control any kind of robot over distance.
Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms
Venue: arXiv cs.AI 2017
Year: 2017

Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.
Monitoring Partially Synchronous Distributed Systems using SMT Solvers
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we discuss the feasibility of monitoring partially synchronous distributed systems to detect latent bugs, i.e., errors caused by concurrency and race conditions among concurrent processes. We present a monitoring framework where we model both system constraints and latent bugs as Satisfiability Modulo Theories (SMT) formulas, and we detect the presence of latent bugs using an SMT solver. We demonstrate the feasibility of our framework using both synthetic applications where latent bugs occur at any time with random probability and an application involving exclusive access to a shared resource with a subtle timing bug. We illustrate how the time required for verification is affected by parameters such as communication frequency, latency, and clock skew. Our results show that our framework can be used for real-life applications, and because our framework uses SMT solvers, the range of appropriate applications will increase as these solvers become more efficient over time.
cniCloud: Querying the Cellular Network Information at Scale
Venue: arXiv cs.NI 2017
Year: 2017

This paper presents cniCloud, a cloud-based platform for mobile devices to share and query the fine-grained cellular information at scale. cniCloud extends the single-device cellular analytics via crowdsourcing: It collects the fine-grained cellular network data from massive mobile devices, aggregates them in a cloud-based database, and provides interfaces for end users to run an SQL-like query over the cellular data. It offers efficient and responsive processing by optimizing the database storage and adopting the domain-specific optimizations. Our preliminary deployments and experiments validate its feasibility in performing crowdsourced analytics.
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
Venue: arXiv cs.CR 2017
Year: 2017

The quest for better data analysis and artificial intelligence has lead to more and more data being collected and stored. As a consequence, more data are exposed to malicious entities. This paper examines the problem of privacy in machine learning for classification. We utilize the Ridge Discriminant Component Analysis (RDCA) to desensitize data with respect to a privacy label. Based on five experiments, we show that desensitization by RDCA can effectively protect privacy (i.e. low accuracy on the privacy label) with small loss in utility. On HAR and CMU Faces datasets, the use of desensitized data results in random guess level accuracies for privacy at a cost of 5.14% and 0.04%, on average, drop in the utility accuracies. For Semeion Handwritten Digit dataset, accuracies of the privacy-sensitive digits are almost zero, while the accuracies for the utility-relevant digits drop by 7.53% on average. This presents a promising solution to the problem of privacy in machine learning for classification.
Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams
Venue: arXiv cs.IR 2017
Year: 2017

Time is an important relevance signal when searching streams of social media posts. The distribution of document timestamps from the results of an initial query can be leveraged to infer the distribution of relevant documents, which can then be used to rerank the initial results. Previous experiments have shown that kernel density estimation is a simple yet effective implementation of this idea. This paper explores an alternative approach to mining temporal signals with recurrent neural networks. Our intuition is that neural networks provide a more expressive framework to capture the temporal coherence of neighboring documents in time. To our knowledge, we are the first to integrate lexical and temporal signals in an end-to-end neural network architecture, in which existing neural ranking models are used to generate query-document similarity vectors that feed into a bidirectional LSTM layer for temporal modeling. Our results are mixed: existing neural models for document ranking alone yield limited improvements over simple baselines, but the integration of lexical and temporal signals yield significant improvements over competitive temporal baselines.
Anti-Forensics of Camera Identification and the Triangle Test by Improved Fingerprint-Copy Attack
Venue: arXiv cs.MM 2017
Year: 2017

The fingerprint-copy attack aims to confuse camera identification based on sensor pattern noise. However, the triangle test shows that the forged images undergone fingerprint-copy attack would share a non-PRNU (Photo-response nonuniformity) component with every stolen image, and thus can detect fingerprint-copy attack. In this paper, we propose an improved fingerprint-copy attack scheme. Our main idea is to superimpose the estimated fingerprint into the target image dispersedly, via employing a block-wise method and using the stolen images randomly and partly. We also develop a practical method to determine the strength of the superimposed fingerprint based on objective image quality. In such a way, the impact of non-PRNU component on the triangle test is reduced, and our improved fingerprint-copy attack is difficultly detected. The experiments evaluated on 2,900 images from 4 cameras show that our scheme can effectively fool camera identification, and significantly degrade the performance of the triangle test simultaneously.
Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
Venue: arXiv cs.IR 2017
Year: 2017

The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers. Given that recent architectural innovations are mostly new word interaction layers or attention-based matching mechanisms, it seems to be a well-established fact that these components are mandatory for good performance. Unfortunately, the memory and computation cost incurred by these complex mechanisms are undesirable for practical applications. As such, this paper tackles the question of whether it is possible to achieve competitive performance with simple neural architectures. We propose a simple but novel deep learning architecture for fast and efficient question-answer ranking and retrieval. More specifically, our proposed model, \textsc{HyperQA}, is a parameter efficient neural network that outperforms other parameter intensive models such as Attentive Pooling BiLSTMs and Multi-Perspective CNNs on multiple QA benchmarks. The novelty behind \textsc{HyperQA} is a pairwise ranking objective that models the relationship between question and answer embeddings in Hyperbolic space instead of Euclidean space. This empowers our model with a self-organizing ability and enables automatic discovery of latent hierarchies while learning embeddings of questions and answers. Our model requires no feature engineering, no similarity matrix matching, no complicated attention mechanisms nor over-parameterized layers and yet outperforms and remains competitive to many models that have these functionalities on multiple benchmarks.
Partial Transfer Learning with Selective Adversarial Networks
Venue: arXiv cs.LG 2017
Year: 2017

Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains. Existing domain adversarial networks assume fully shared label space across domains. In the presence of big data, there is strong motivation of transferring both classification and representation models from existing big domains to unknown small domains. This paper introduces partial transfer learning, which relaxes the shared label space assumption to that the target label space is only a subspace of the source label space. Previous methods typically match the whole source domain to the target domain, which are prone to negative transfer for the partial transfer problem. We present Selective Adversarial Network (SAN), which simultaneously circumvents negative transfer by selecting out the outlier source classes and promotes positive transfer by maximally matching the data distributions in the shared label space. Experiments demonstrate that our models exceed state-of-the-art results for partial transfer learning tasks on several benchmark datasets.