OpenRaiser/Intern-Atlas huggingface dataset:
Comparative Study of CNN and RNN for Natural Language Processing
Venue: arXiv cs.CL 2017
Year: 2017

Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant features and RNN at modeling units in sequence. The state of the art on many NLP tasks often switches due to the battle between CNNs and RNNs. This work is the first systematic comparison of CNN and RNN on a wide range of representative NLP tasks, aiming to give basic guidance for DNN selection.
Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study
Venue: arXiv cs.CL 2017
Year: 2017

The effectiveness of three stop words lists for Arabic Information Retrieval---General Stoplist, Corpus-Based Stoplist, Combined Stoplist ---were investigated in this study. Three popular weighting schemes were examined: the inverse document frequency weight, probabilistic weighting, and statistical language modelling. The Idea is to combine the statistical approaches with linguistic approaches to reach an optimal performance, and compare their effect on retrieval. The LDC (Linguistic Data Consortium) Arabic Newswire data set was used with the Lemur Toolkit. The Best Match weighting scheme used in the Okapi retrieval system had the best overall performance of the three weighting algorithms used in the study, stoplists improved retrieval effectiveness especially when used with the BM25 weight. The overall performance of a general stoplist was better than the other two lists.
TensorBeat: Tensor Decomposition for Monitoring Multi-Person Breathing Beats with Commodity WiFi
Venue: arXiv cs.CE 2017
Year: 2017

Breathing signal monitoring can provide important clues for human's physical health problems. Comparing to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this paper, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensor data. Then Canonical Polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to find the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multi-person breathing rate monitoring.
Adaptive Neural Control for a Class of Stochastic Nonlinear Systems with Unknown Parameters, Unknown Nonlinear Functions and Stochastic Disturbances
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, adaptive neural control (ANC) is investigated for a class of strict-feedback nonlinear stochastic systems with unknown parameters, unknown nonlinear functions and stochastic disturbances. The new controller of adaptive neural network with state feedback is presented by using a universal approximation of radial basis function neural network and backstepping. An adaptive neural network state-feedback controller is designed by constructing a suitable Lyapunov function. Adaptive bounding design technique is used to deal with the unknown nonlinear functions and unknown parameters. It is shown that, the global asymptotically stable in probability can be achieved for the closed-loop system. The simulation results are presented to demonstrate the effectiveness of the proposed control strategy in the presence of unknown parameters, unknown nonlinear functions and stochastic disturbances.
First Study on Data Readiness Level
Venue: arXiv cs.IR 2017
Year: 2017

We introduce the idea of Data Readiness Level (DRL) to measure the relative richness of data to answer specific questions often encountered by data scientists. We first approach the problem in its full generality explaining its desired mathematical properties and applications and then we propose and study two DRL metrics. Specifically, we define DRL as a function of at least four properties of data: Noisiness, Believability, Relevance, and Coherence. The information-theoretic based metrics, Cosine Similarity and Document Disparity, are proposed as indicators of Relevance and Coherence for a piece of data. The proposed metrics are validated through a text-based experiment using Twitter data.
On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study
Venue: arXiv cs.SD 2017
Year: 2017

Musical source separation methods exploit source-specific spectral characteristics to facilitate the decomposition process. Kernel Additive Modelling (KAM) models a source applying robust statistics to time-frequency bins as specified by a source-specific kernel, a function defining similarity between bins. Kernels in existing approaches are typically defined using metrics between single time frames. In the presence of noise and other sound sources information from a single-frame, however, turns out to be unreliable and often incorrect frames are selected as similar. In this paper, we incorporate a temporal context into the kernel to provide additional information stabilizing the similarity search. Evaluated in the context of vocal separation, our simple extension led to a considerable improvement in separation quality compared to previous kernels.
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
Venue: arXiv cs.CL 2017
Year: 2017

Maybe the single most important goal of representation learning is making subsequent learning faster. Surprisingly, this fact is not well reflected in the way embeddings are evaluated. In addition, recent practice in word embeddings points towards importance of learning specialized representations. We argue that focus of word representation evaluation should reflect those trends and shift towards evaluating what useful information is easily accessible. Specifically, we propose that evaluation should focus on data efficiency and simple supervised tasks, where the amount of available data is varied and scores of a supervised model are reported for each subset (as commonly done in transfer learning). In order to illustrate significance of such analysis, a comprehensive evaluation of selected word embeddings is presented. Proposed approach yields a more complete picture and brings new insight into performance characteristics, for instance information about word similarity or analogy tends to be non--linearly encoded in the embedding space, which questions the cosine-based, unsupervised, evaluation methods. All results and analysis scripts are available online.
Resource Allocation and Relay Selection In Full-Duplex Cooperative Orthogonal Frequency Division Multiple Access Networks
Venue: arXiv cs.IT 2017
Year: 2017

This paper is on relay selection in uplink of an in-band full-duplex (IBFD) cooperative cellular network. Assuming an orthogonal frequency division multiple access (OFDMA) cellular network, we develop a relay selection and resource allocation algorithm for this network. The relay selection algorithms select the best relay based on distance between users and signal to interference plus noise ratio (SINR) that operate in amplify and forward (AF) mode. The optimization problem allocates the optimum subcarriers and powers to all users to maximize the average sum-rate of the network. In addition, the constraints of the optimization problem are quality of service (QoS) and transmit power of each user. Simulation results illustrate the good performance of the proposed method.
Parallel implementation of the coupled harmonic oscillator
Venue: arXiv cs.DC 2017
Year: 2017

This article presents the parallel implementation of the coupled harmonic oscillator. From the analytical solution of the coupled harmonic oscillator, the design parameters are obtained. After that, a numerical integration of the system with MATLAB, which is used as a tool of benchmark evaluation, is performed. Next, parallel implementation is performed using a well-known approach like OpenMP and WinAPI. Taking into account the errors of basic parameters of the simulated process, the generated oscillations of the proposed parallel realization are almost identical to the actual solution of the harmonic oscillator model. Test ways to optimize the parallel architecture of computing processes for software implementations of the considered application is carried out. The developed model is used to study a fixed priority scheduling algorithm for real-time parallel threads execution. The proposed parallel implementation of the considered dynamic system has an independent value and can be considered as a test for determining the characteristics of multi-core systems for time-critical simulation problems. Keywords: Harmonic oscillator, model, SMP, parallel programming, OpenMP;
Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers
Venue: arXiv cs.CY 2017
Year: 2017

The telecommunications industry is highly competitive, which means that the mobile providers need a business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal level of cost in marketing activities. Machine learning applications can be used to provide guidance on marketing strategies. Furthermore, data mining techniques can be used in the process of customer segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling according to their billing and socio-demographic aspects. Results have been experimentally implemented.
Social media mining for identification and exploration of health-related information from pregnant women
Venue: arXiv cs.CL 2017
Year: 2017

Widespread use of social media has led to the generation of substantial amounts of information about individuals, including health-related information. Social media provides the opportunity to study health-related information about selected population groups who may be of interest for a particular study. In this paper, we explore the possibility of utilizing social media to perform targeted data collection and analysis from a particular population group -- pregnant women. We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information. To identify potentially pregnant women, we employ simple rule-based searches that attempt to detect pregnancy announcements with moderate precision. To further filter out false positives and noise, we employ a supervised classifier using a small number of hand-annotated data. We then collect their posts over time to create longitudinal health timelines and attempt to divide the timelines into different pregnancy trimesters. Finally, we assess the usefulness of the timelines by performing a preliminary analysis to estimate drug intake patterns of our cohort at different trimesters. Our rule-based cohort identification technique collected 53,820 users over thirty months from Twitter. Our pregnancy announcement classification technique achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user timelines. Analysis of the timelines revealed that pertinent health-related information, such as drug-intake and adverse reactions can be mined from the data. Our approach to using user timelines in this fashion has produced very encouraging results and can be employed for other important tasks where cohorts, for which health-related information may not be available from other sources, are required to be followed over time to derive population-based estimates.
Clustering For Point Pattern Data
Venue: arXiv cs.LG 2017
Year: 2017

Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources. In this paper, we propose two approaches for clustering point patterns. The first is a non-parametric method based on novel distances for sets. The second is a model-based approach, formulated via random finite set theory, and solved by the Expectation-Maximization algorithm. Numerical experiments show that the proposed methods perform well on both simulated and real data.
Name Disambiguation in Anonymized Graphs using Network Embedding
Venue: arXiv cs.SI 2017
Year: 2017

In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly better than the existing name disambiguation methods working in a similar setting.
Cooperative Coded Data Dissemination for Wireless Sensor Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this poster paper we present a data dissemination transmission abstraction for over the air programming (OAP) protocol which is fundamentally different from the previous hop by hop transmission protocols. Instead of imposing the greedy requirement that at least one node in the ith hop receives all packets before transmitting packets to the next hop and its neighbours, we take advantage of the spatial diversity and broadcast nature of wireless transmission to adopt a cooperative approach in which node broadcast whatever packets it has received with the expectation that it will recover the lost packets with high probability by overhearing the broadcast transmissions of its neighbours. The use of coded transmissions ensures that this does not lead to the broadcast storm problem. We validate the improved performance our of proposed transmission scheme with respect to the previous state of the art OAP protocols on a proof-of-concept two-hops TelosB wireless sensor network testbed.
On Multilevel Coding Schemes Based on Non-Binary LDPC Codes
Venue: arXiv cs.IT 2017
Year: 2017

We address the problem of constructing of coding schemes for the channels with high-order modulations. It is known, that non-binary LDPC codes are especially good for such channels and significantly outperform their binary counterparts. Unfortunately, their decoding complexity is still large. In order to reduce the decoding complexity we consider multilevel coding schemes based on non-binary LDPC codes (NB-LDPC-MLC schemes) over smaller fields. The use of such schemes gives us a reasonable gain in complexity. At the same time the performance of NB-LDPC-MLC schemes is practically the same as the performance of LDPC codes over the field matching the modulation order. In particular by means of simulations we showed that the performance of NB-LDPC-MLC schemes over GF(16) is the same as the performance of non-binary LDPC codes over GF(64) and GF(256) in AWGN channel with QAM64 and QAM256 accordingly. We also perform a comparison with binary LDPC codes.
A bibliometric approach to Systematic Mapping Studies: The case of the evolution and perspectives of community detection in complex networks
Venue: arXiv cs.SI 2017
Year: 2017

Critical analysis of the state of the art is a necessary task when identifying new research lines worthwhile to pursue. To such an end, all the available work related to the field of interest must be taken into account. The key point is how to organize, analyze, and make sense of the huge amount of scientific literature available today on any topic. To tackle this problem, we present here a bibliometric approach to Systematic Mapping Studies (SMS). Thus, a modify SMS protocol is used relying on the scientific references metadata to extract, process and interpret the wealth of information contained in nowadays research literature. As a test case, the procedure is applied to determine the current state and perspectives of community detection in complex networks. Our results show that community detection is a still active, far from exhausted, in development, field. In addition, we find that, by far, the most exploited methods are those related to determining hierarchical community structures. On the other hand, the results show that fuzzy clustering techniques, despite their interest, are underdeveloped as well as the adaptation of existing algorithms to parallel or, more specifically, distributed, computational systems.
A Development of Hybrid Framework for E-Government
Venue: arXiv cs.CY 2017
Year: 2017

Governments all around the world are widely investing on the implementation of e government to advance services to citizens and minimize costs. Governments can progress effectiveness of their operations and can carry their administrative operations efficiently with the help of ICT. Electronic government perceived to provide a way for governments to renovate their operational activities to serve their clients more competently. With improvement in Information and Communication Technology ICT it is now time to device electronic access to government facilities to the variously located citizens. E governments all around the world have different objectives and follow different models for e government development. Present models examined and found less than satisfactory to guide e-government implementation. This research proposed a hybrid model from Citizen comprehensive vision acknowledged the civic idea and The strategic framework of e-government models. The procedure of merging different computational knowledge systems to assemble a solitary crossover show has turned out to be progressively prominent. The execution files of these mixture models have ended up being superior to the individual segments when utilized alone. To ensure that the proposed model is more efficient and beneficial a survey study conducted. Survey based on questionnaires and results calculated by applying statistics on data gathered from survey.
Deterministic Backbone Creation in an SINR Network without Knowledge of Location
Venue: arXiv cs.DC 2017
Year: 2017

For a given network, a backbone is an overlay network consisting of a connected dominating set with additional accessibility properties. Once a backbone is created for a network, it can be utilized for fast communication amongst the nodes of the network. The Signal-to-Interference-plus-Noise-Ratio (SINR) model has become the standard for modeling communication among devices in wireless networks. For this model, the community has pondered what the most realistic solutions for communication problems in wireless networks would look like. Such solutions would have the characteristic that they would make the least number of assumptions about the availability of information about the participating nodes. Solving problems when nothing at all is known about the network and having nodes just start participating would be ideal. However, this is quite challenging and most likely not feasible. The pragmatic approach is then to make meaningful assumptions about the available information and present efficient solutions based on this information. We present a solution for creation of backbone in the SINR model, when nodes do not have access to their physical coordinates or the coordinates of other nodes in the network. This restriction models the deployment of nodes in various situations for sensing hurricanes, cyclones, and so on, where only information about nodes prior to their deployment may be known but not their actual locations post deployment. We assume that nodes have access to knowledge of their label, the labels of nodes within their neighborhood, the range from which labels are taken $[N]$ and the total number of participating nodes $n$. We also assume that nodes wake up spontaneously. We present an efficient deterministic protocol to create a backbone with a round complexity of $O(Δ\lg^2 N)$.
Who Will Win Practical Artificial Intelligence? AI Engineerings in China
Venue: arXiv cs.CY 2017
Year: 2017

Currently, Artificial Intelligence (AI) has won unprecedented attention and is becoming the increasingly popular focus in China. This change can be judged by the impressive record of academic publications, the amount of state-level investment and the presence of nation-wide participation and devotion. In this paper, we place emphasis on discussing the progress of artificial intelligence engineerings in China. We first introduce the focus on AI in Chinese academia, including the supercomputing brain system, Cambrian Period supercomputer of neural networks, and biometric systems. Then, the development of AI in industrial circles and the latest layout of AI products in companies, such as Baidu, Tencent, and Alibaba, are introduced. Last, we bring in the opinions and arguments of the main intelligentsia of China about the future development of AI, including how to examine the relationship between humanity on one side and science and technology on the other.