OpenRaiser/Intern-Atlas huggingface dataset:
Conceptual Proposal: Frequency Offset Modulation for High-Efficiency Communications
Venue: arXiv cs.IT 2016
Year: 2016

Frequency offset modulation (FOM) is proposed as a new concept to provide both high energy efficiency and high spectral efficiency for communications. In the FOM system, an array of transmitters (TXs) is deployed and only one TX is activated for data transmission at any signaling time instance. The TX index distinguished by a very slight frequency offset among the entire occupied bandwidth is exploited to implicitly convey a bit unit without any power or signal radiation, saving the power and spectral resources. Moreover, the FOM is characterized by removing the stringent requirements on distinguishable spatial channels and perfect priori channel knowledge, while retaining the advantages of no inter-channel interference and no need of inter-antenna synchronization. In addition, a hybrid solution integrating the FOM and the spatial modulation is discussed to further improve the energy efficiency and spectral efficiency. Consequently, the FOM will be an enabling and green solution to support ever-increasing high-capacity data traffic in a variety of interdisciplinary fields.
Authentication Protocols for Internet of Things: A Comprehensive Survey
Venue: arXiv cs.CR 2016
Year: 2016

In this paper, we present a comprehensive survey of authentication protocols for Internet of Things (IoT). Specifically, we select and in-detail examine more than forty authentication protocols developed for or applied in the context of the IoT under four environments, including: (1) Machine to machine communications (M2M), (2) Internet of Vehicles (IoV), (3) Internet of Energy (IoE), and (4) Internet of Sensors (IoS). We start by reviewing all survey articles published in the recent years that focusing on different aspects of the IoT idea. Then, we review threat models, countermeasures, and formal security verification techniques used in authentication protocols for the IoT. In addition, we provide a taxonomy and comparison of authentication protocols for the IoT in form of tables in five terms, namely, network model, goals, main processes, computation complexity, and communication overhead. Based on the current survey, we identify open issues and suggest hints for future research.
Growth of International Cooperation in Science: Revisiting Six Case Studies
Venue: arXiv cs.DL 2016
Year: 2016

International collaboration in science continues to grow at a remarkable rate, but little agreement exists about dynamics of growth and organization at the discipline level. Some suggest that disciplines differ in their collaborative tendencies, reflecting their epistemic culture. This study examines collaborative patterns in six previously studied specialties to add new data and conduct analyses over time. Our findings show that the global network of collaboration continues to add new nations and new participants; each specialty has added many new nations to its lists of collaborating partners since 1990. We also find that the scope of international collaboration is positively related to impact. Network characteristics for the six specialties are notable in that instead of reflecting underlying culture, they tend towards convergence. This observation suggests that the global level may represent next-order dynamics that feed back to the national and local levels (as subsystems) in a complex, networked hierarchy.
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States
Venue: arXiv cs.CV 2016
Year: 2016

Important high-level vision tasks such as human-object interaction, image captioning and robotic manipulation require rich semantic descriptions of objects at part level. Based upon previous work on part localization, in this paper, we address the problem of inferring rich semantics imparted by an object part in still images. We propose to tokenize the semantic space as a discrete set of part states. Our modeling of part state is spatially localized, therefore, we formulate the part state inference problem as a pixel-wise annotation problem. An iterative part-state inference neural network is specifically designed for this task, which is efficient in time and accurate in performance. Extensive experiments demonstrate that the proposed method can effectively predict the semantic states of parts and simultaneously correct localization errors, thus benefiting a few visual understanding applications. The other contribution of this paper is our part state dataset which contains rich part-level semantic annotations.
RRT+ : Fast Planning for High-Dimensional Configuration Spaces
Venue: arXiv cs.RO 2016
Year: 2016

In this paper we propose a new family of RRT based algorithms, named RRT+ , that are able to find faster solutions in high-dimensional configuration spaces compared to other existing RRT variants by finding paths in lower dimensional subspaces of the configuration space. The method can be easily applied to complex hyper-redundant systems and can be adapted by other RRT based planners. We introduce RRT+ and develop some variants, called PrioritizedRRT+ , PrioritizedRRT+-Connect, and PrioritizedBidirectionalT-RRT+ , that use the new sampling technique and we show that our method provides faster results than the corresponding original algorithms. Experiments using the state-of-the-art planners available in OMPL show superior performance of RRT+ for high-dimensional motion planning problems.
Stopping Condition for Greedy Block Sparse Signal Recovery
Venue: arXiv cs.IT 2016
Year: 2016

For greedy block sparse recovery where the sparsity level is unknown, we derive a stopping condition to stop the iteration process. Focused on the block orthogonal matching pursuit (BOMP) algorithm, we model the energy of residual signals at each iteration from a probabilistic perspective. At the iteration when the last supporting block is detected, the resulting energy of residual signals is supposed to suffer an obvious decrease. Based on this, we stop the iteration process when the energy of residual signals is below a given threshold. Compared with other approaches, our derived condition works well for the BOMP recovery. What is more, we promote our approach to the interference cancellation based BOMP (ICBOMP) recovery in paper [1]. Simulation results show that our derived condition can save many unnecessary iterations and at the same time guarantees a favorable recovery accuracy, both for the BOMP and ICBOMP recoveries.
EchoWear: Smartwatch Technology for Voice and Speech Treatments of Patients with Parkinson's Disease
Venue: arXiv cs.CY 2016
Year: 2016

About 90 percent of people with Parkinson's disease (PD) experience decreased functional communication due to the presence of voice and speech disorders associated with dysarthria that can be characterized by monotony of pitch (or fundamental frequency), reduced loudness, irregular rate of speech, imprecise consonants, and changes in voice quality. Speech-language pathologists (SLPs) work with patients with PD to improve speech intelligibility using various intensive in-clinic speech treatments. SLPs also prescribe home exercises to enhance generalization of speech strategies outside of the treatment room. Even though speech therapies are found to be highly effective in improving vocal loudness and speech quality, patients with PD find it difficult to follow the prescribed exercise regimes outside the clinic and to continue exercises once the treatment is completed. SLPs need techniques to monitor compliance and accuracy of their patients exercises at home and in ecologically valid communication situations. We have designed EchoWear, a smartwatch-based system, to remotely monitor speech and voice exercises as prescribed by SLPs. We conducted a study of 6 individuals; three with PD and three healthy controls. To assess the performance of EchoWear technology compared with high quality audio equipment obtained in a speech laboratory. Our preliminary analysis shows promising outcomes for using EchoWear in speech therapies for people with PD. Keywords: Dysarthria; knowledge-based speech processing; Parkinson's disease; smartwatch; speech therapy; wearable system.
Secure Network in Business-to-Business application by using Access Control List (ACL) and Service Level Agreement (SLA)
Venue: arXiv cs.CR 2016
Year: 2016

The motivation behind this paper is to dissecting the secure network for Business to Business (B2B) application by Implementing Access Control List (ACL) and Service Level Agreement (SLA). This data provides the nature of attacks reported as external or internal attacks. This paper presents the initial finding of attacks, types of attacks and their ratio within specific time. It demonstrates the advance technique and methodology to reduce the attacks and vulnerabilities and minimize the ratio of attacks to the networks and application and keep the network secure and runs application smoothly regarding that. It also identifies the location of attacks, the reason behind the attack and the technique used in attacking. The whole field of system security is limitless and in an evolutionary stage. To comprehend the exploration being performed today, foundation learning of the web and assaults, the security is vital and in this way they are investigated. It provides the statistical analytics about various attacks and nature of attacks for acquiring the results through simulation to prove the hypothesis.
Compressed Sensing Algorithms for OFDM Channel Estimation
Venue: arXiv cs.IT 2016
Year: 2016

Radio channels are typically sparse in the delay domain, and ideal for compressed sensing. A new compressed sensing algorithm called eX-OMP is developed that yields performance similar to that of the optimal MMSE estimator. The new algorithm relies on a small amount additional data. Both eX-OMP and the MMSE estimator adaptively balance channel tracking and noise reduction. They perform better than simple estimators such as the linear-interpolator which fix this trade-off a priori. Some wideband measurements are examined, and the channels are found to be represented by a few delays.
The Cycle Structure of LFSR with Arbitrary Characteristic Polynomial over Finite Fields
Venue: arXiv cs.IT 2016
Year: 2016

We determine the cycle structure of linear feedback shift register with arbitrary monic characteristic polynomial over any finite field. For each cycle, a method to find a state and a new way to represent the state are proposed.
Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information
Venue: arXiv cs.CV 2016
Year: 2016

Accurate detection of fingertips in depth image is critical for human-computer interaction. In this paper, we present a novel two-stream convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge image is extracted from raw depth image using random forest. Then the edge information is combined with depth information in our CNN structure. We study several fusion approaches and suggest a slow fusion strategy as a promising way of fingertip detection. As shown in our experiments, our real-time algorithm outperforms state-of-the-art fingertip detection methods on the public dataset HandNet with an average 3D error of 9.9mm, and shows comparable accuracy of fingertip estimation on NYU hand dataset.
JU_KS_Group@FIRE 2016: Consumer Health Information Search
Venue: arXiv cs.IR 2016
Year: 2016

In this paper, we describe the methodology used and the results obtained by us for completing the tasks given under the shared task on Consumer Health Information Search (CHIS) collocated with the Forum for Information Retrieval Evaluation (FIRE) 2016, ISI Kolkata. The shared task consists of two sub-tasks - (1) task1: given a query and a document/set of documents associated with that query, the task is to classify the sentences in the document as relevant to the query or not and (2) task 2: the relevant sentences need to be further classified as supporting the claim made in the query, or opposing the claim made in the query. We have participated in both the sub-tasks. The percentage accuracy obtained by our developed system for task1 was 73.39 which is third highest among the 9 teams participated in the shared task.
An Overview of Mobile Capacitive Touch Technologies Trends
Venue: arXiv cs.ET 2016
Year: 2016

Touch sensing, as a major human/machine interface, is widely used in various commercial products such as smart watches, mobile phones, tablets and TVs. State-of-the-art touch detections are mainly based on mutual capacitive sensing, which requires necessary contact-touch, limiting the mobile user experience. Recently, remote gesture sensing is widely reported in both academy and industry as it can provide additional user-experience for mobile interface. The capacitive remote gesture sensing is mainly based on detecting self-capacitance, achieving high resolution through eliminating the parasitic mutual capacitance. In this work, we overview the different generations of touchscreen technology, comparing the touch and remote gesture sensing technologies difference. In addition, different remote gesture sensing technologies are also compared. The limitations and potentials of different topologies are discussed and a final conclusion about the technology trends is summarized in the end.
Abstractive Headline Generation for Spoken Content by Attentive Recurrent Neural Networks with ASR Error Modeling
Venue: arXiv cs.CL 2016
Year: 2016

Headline generation for spoken content is important since spoken content is difficult to be shown on the screen and browsed by the user. It is a special type of abstractive summarization, for which the summaries are generated word by word from scratch without using any part of the original content. Many deep learning approaches for headline generation from text document have been proposed recently, all requiring huge quantities of training data, which is difficult for spoken document summarization. In this paper, we propose an ASR error modeling approach to learn the underlying structure of ASR error patterns and incorporate this model in an Attentive Recurrent Neural Network (ARNN) architecture. In this way, the model for abstractive headline generation for spoken content can be learned from abundant text data and the ASR data for some recognizers. Experiments showed very encouraging results and verified that the proposed ASR error model works well even when the input spoken content is recognized by a recognizer very different from the one the model learned from.
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
Venue: arXiv cs.CV 2016
Year: 2016

Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density. We propose a novel Signature of Geometric Centroids descriptor, supporting direct shape matching on the scans, without requiring any preprocessing such as scan denoising or converting into a mesh. First, we construct the descriptor by voxelizing the local shape within a uniquely defined local reference frame and concatenating geometric centroid and point density features extracted from each voxel. Second, we compare two descriptors by employing only corresponding voxels that are both non-empty, thus supporting matching incomplete local shape such as those close to scan boundary. Third, we propose a descriptor saliency measure and compute it from a descriptor-graph to improve shape matching performance. We demonstrate the descriptor's robustness and effectiveness for shape matching by comparing it with three state-of-the-art descriptors, and applying it to object/scene reconstruction and 3D object recognition.
Performance Analysis and Compensation of Joint TX/RX I/Q Imbalance in Differential STBC-OFDM
Venue: arXiv cs.IT 2016
Year: 2016

Differential space time block coding (STBC) achieves full spatial diversity and avoids channel estimation overhead. Over highly frequency-selective channels, STBC is integrated with orthogonal frequency division multiplexing (OFDM) to efficiently mitigate intersymbol interference effects. However, low-cost implementation of STBC-OFDM with direct-conversion transceivers is sensitive to In-phase/Quadrature-phase imbalance (IQI). In this paper, we quantify the performance impact of IQI at both the transmitter and receiver radio frequency front-ends on differential STBC-OFDM systems which has not been investigated before in the literature. In addition, we propose a widely-linear compensation algorithm at the receiver to mitigate the performance degradation caused by the IQI at the transmitter and receiver ends. Moreover, a parameter-based generalized algorithm is proposed to extract the IQI parameters and improve the performance under high-mobility. The adaptive compensation algorithms are blind and work in a decision-directed manner without using known pilots or training sequences. Numerical results show that our proposed compensation algorithms can effectively mitigate IQI in differential STBC-OFDM.
A Hybrid Both Filter and Wrapper Feature Selection Method for Microarray Classification
Venue: arXiv cs.LG 2016
Year: 2016

Gene expression data is widely used in disease analysis and cancer diagnosis. However, since gene expression data could contain thousands of genes simultaneously, successful microarray classification is rather difficult. Feature selection is an important pre-treatment for any classification process. Selecting a useful gene subset as a classifier not only decreases the computational time and cost, but also increases classification accuracy. In this study, we applied the information gain method as a filter approach, and an improved binary particle swarm optimization as a wrapper approach to implement feature selection; selected gene subsets were used to evaluate the performance of classification. Experimental results show that by employing the proposed method fewer gene subsets needed to be selected and better classification accuracy could be obtained.
Reduce The Wastage of Data During Movement in Data Warehouse
Venue: arXiv cs.DC 2016
Year: 2016

In this research paper so as to handle Data in warehousing as well as reduce the wastage of data and provide a better results which takes more and more turn into a focal point of the data source business. Data warehousing and on-line analytical processing (OLAP) are vital fundamentals of resolution hold, which has more and more become a focal point of the database manufacturing. Lots of marketable yield and services be at the present accessible, and the entire primary database management organization vendor nowadays have contributions in the area assessment hold up spaces some quite dissimilar necessities on record technology compare to conventional on-line transaction giving out application. This article gives a general idea of data warehousing and OLAP technologies, with the highlighting on top of their latest necessities. So tools which is used for extract, clean-up and load information into back end of a information warehouse; multidimensional data model usual of OLAP; front end client tools for querying and data analysis; server extension for proficient query processing; and tools for data managing and for administration the warehouse. In adding to survey the circumstances of the art, this article also identify a number of capable research issue, a few which are interrelated to data wastage troubles. In this paper use some new techniques to reduce the wastage of data, provide better results. In this paper take some values, put in anova table and give results through graphs which shows performance.
Non-Linear Programming: Maximize SNR for Designing Spreading Sequence - Part II: Conditions for Optimal Spreading Sequences
Venue: arXiv cs.IT 2016
Year: 2016

Signal to Noise Ratio (SNR) is an important index for wireless communications. In CDMA systems, spreading sequences are utilized. This series of papers show the method to derive spreading sequences as the solutions of non-linear programming: maximize SNR. In this paper, we derive the optimization problems with the expression SNR derived in Part I and the necessary conditions for the global solutions. We numerically solve the problems and evaluate the solutions with two factors, mean-square correlations and maximum mean-square correlations.
Characterizing Communicability of Networks formed on Mobile Nodes
Venue: arXiv cs.SI 2016
Year: 2016

Smartphones have become extremely popular by launching wide ubiquitous networks. Nowadays studying of DTN Delay Tolerant Networks (DTN) and Opportunistic Networks where formed over these mobile nodes, is one of the interesting topics in the research community. In this paper, we measure communicability capacity of DTN Network formed over the mobile nodes at a university campus and also an area in Montreal city through exploiting static and temporal graphs. We observed a significant difference between communicability measures in static and temporal cases, especially for short snapshot windows. It implies that analyzing dynamic networks by considering a static model for them may lead to an unrealistic and even mislead results.
Reliability Modeling and Analysis of Communication Networks
Venue: arXiv cs.NI 2016
Year: 2016

In recent times, the functioning of various aspects of modern society---ranging from the various infrastructural utilities such as electrical power, water to socio-economical aspects such as telecommunications, business, commerce, education---has become critically reliant on communication networks, and particularly on the Internet. With the migration of critical facilities to the Internet, it has become vitally important to ensure the reliability and availability of networks. In this paper, we study various modeling and analysis techniques that can aid in the study of reliability of communication networks. In this regard, we provide background on the modeling techniques (such as reliability block diagrams, fault trees, Markov chains, etc.) and analysis techniques (such as mathematical analytical methods, simulation methods, and formal methods). Apart from providing the necessary background, we also critically evaluate the pros and cons of different approaches, and provide a detailed survey of their applications in communication networks. To the best of our knowledge, this is the first in-depth review of the application of reliability modeling and analysis techniques in communication networks.
Distributed Convex Optimization with Inequality Constraints over Time-varying Unbalanced Digraphs
Venue: arXiv cs.DC 2016
Year: 2016

This paper considers a distributed convex optimization problem with inequality constraints over time-varying unbalanced digraphs, where the cost function is a sum of local objectives, and each node of the graph only knows its local objective and inequality constraints. Although there is a vast literature on distributed optimization, most of them require the graph to be balanced, which is quite restrictive and not necessary. Very recently, the unbalanced problem has been resolved only for either time-invariant graphs or unconstrained optimization. This work addresses the unbalancedness by focusing on an epigraph form of the constrained optimization. A striking feature is that this novel idea can be easily used to study time-varying unbalanced digraphs. Under local communications, a simple iterative algorithm is then designed for each node. We prove that if the graph is uniformly jointly strongly connected, each node asymptotically converges to some common optimal solution.