OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Detection and Query of Wireless Spectrum Events from Streaming Data
Venue: arXiv cs.NI 2018
Year: 2018

Several alternatives for more efficient spectrum management have been proposed over the last decade, resulting in new techniques for automatic wideband spectrum sensing. However, while spectrum sensing technology is important, understanding, using and taking actions on this data for better spectrum and network resource management is at least equally important. In this paper, we propose a system that is able to automatically detect wireless spectrum events from streaming spectrum sensing data, and enables the consumption of the events as they are produced, as a statistical report or on a per-query basis. The proposed system is referred to as spectrum streamer and is wireless technology agnostic, scalable, able to deliver actionable information to humans and machines and also enables application development by custom querying of the detected events.
5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View
Venue: arXiv cs.NI 2018
Year: 2018

The grand objective of 5G wireless technology is to support three generic services with vastly heterogeneous requirements: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Service heterogeneity can be accommodated by network slicing, through which each service is allocated resources to provide performance guarantees and isolation from the other services. Slicing of the Radio Access Network (RAN) is typically done by means of orthogonal resource allocation among the services. This work studies the potential advantages of allowing for non-orthogonal sharing of RAN resources in uplink communications from a set of eMBB, mMTC and URLLC devices to a common base station. The approach is referred to as Heterogeneous Non-Orthogonal Multiple Access (H-NOMA), in contrast to the conventional NOMA techniques that involve users with homogeneous requirements and hence can be investigated through a standard multiple access channel. The study devises a communication-theoretic model that accounts for the heterogeneous requirements and characteristics of the three services. The concept of reliability diversity is introduced as a design principle that leverages the different reliability requirements across the services in order to ensure performance guarantees with non-orthogonal RAN slicing. This study reveals that H-NOMA can lead, in some regimes, to significant gains in terms of performance trade-offs among the three generic services as compared to orthogonal slicing.
A new robust feature selection method using variance-based sensitivity analysis
Venue: arXiv cs.LG 2018
Year: 2018

Excluding irrelevant features in a pattern recognition task plays an important role in maintaining a simpler machine learning model and optimizing the computational efficiency. Nowadays with the rise of large scale datasets, feature selection is in great demand as it becomes a central issue when facing high-dimensional datasets. The present study provides a new measure of saliency for features by employing a Sensitivity Analysis (SA) technique called the extended Fourier amplitude sensitivity test, and a well-trained Feedforward Neural Network (FNN) model, which ultimately leads to the selection of a promising optimal feature subset. Ideas of the paper are mainly demonstrated based on adopting FNN model for feature selection in classification problems. But in the end, a generalization framework is discussed in order to give insights into the usage in regression problems as well as expressing how other function approximate models can be deployed. Effectiveness of the proposed method is verified by result analysis and data visualization for a series of experiments over several well-known datasets drawn from UCI machine learning repository.
O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images
Venue: arXiv cs.CV 2018
Year: 2018

Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years. Despite this growing interest, the scientific community is still lacking a reference dataset to evaluate objectively and quantitatively the performance of proposed dehazing methods. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. To address this limitation, we introduce the first outdoor scenes database (named O-HAZE) composed of pairs of real hazy and corresponding haze-free images. In practice, hazy images have been captured in presence of real haze, generated by professional haze machines, and OHAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. To illustrate its usefulness, O-HAZE is used to compare a representative set of state-of-the-art dehazing techniques, using traditional image quality metrics such as PSNR, SSIM and CIEDE2000. This reveals the limitations of current techniques, and questions some of their underlying assumptions.
Select, Attend, and Transfer: Light, Learnable Skip Connections
Venue: arXiv cs.CV 2018
Year: 2018

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients. They equip encoder-decoder-like networks with richer feature representations, but at the cost of higher memory usage, computation, and possibly resulting in transferring non-discriminative feature maps. In this paper, we focus on improving skip connections used in segmentation networks (e.g., U-Net, V-Net, and The One Hundred Layers Tiramisu (DensNet) architectures). We propose light, learnable skip connections which learn to first select the most discriminative channels and then attend to the most discriminative regions of the selected feature maps. The output of the proposed skip connections is a unique feature map which not only reduces the memory usage and network parameters to a high extent, but also improves segmentation accuracy. We evaluate the proposed method on three different 2D and volumetric datasets and demonstrate that the proposed light, learnable skip connections can outperform the traditional heavy skip connections in terms of segmentation accuracy, memory usage, and number of network parameters.
Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding
Venue: arXiv cs.AI 2018
Year: 2018

Knowledge Graphs (KGs) are becoming essential to information systems that require access to structured data. Several approaches have been recently proposed, for obtaining vector representations of KGs suitable for Machine Learning tasks, based on identifying and extracting relevant graph substructures using uniform and biased random walks. However, such approaches lead to representations comprising mostly "popular", instead of "relevant", entities in the KG. In KGs, in which different types of entities often exist (such as in Linked Open Data), a given target entity may have its own distinct set of most "relevant" nodes and edges. We propose specificity as an accurate measure of identifying most relevant, entity-specific, nodes and edges. We develop a scalable method based on bidirectional random walks to compute specificity. Our experimental evaluation results show that specificity-based biased random walks extract more "meaningful" (in terms of size and relevance) RDF substructures compared to the state-of-the-art and, the graph embedding learned from the extracted substructures, outperform existing techniques in the task of entity recommendation in DBpedia.
Hybrid RF/VLC Systems under QoS Constraints
Venue: arXiv cs.IT 2018
Year: 2018

The coexistence of radio frequency (RF) and visible light communications (VLC) in typical indoor environments can be leveraged to support vast user quality-of-service (QoS) needs. In this paper, we target a hybrid RF/VLC network in which data transmission is provided via either an RF access point or a VLC luminary based on a selection process. We employ the concept of effective capacity, which defines the maximum constant arrival data rate at the transmitter buffer when the QoS needs are imposed as limits on the buffer overflow and delay violation probabilities, as the main selection criteria. We initially formulate the effective capacity of both channels with respect to channel gains and user distribution. Under the assumption of uniform user distribution within the VLC cell, we then provide a closed-form approximation for the effective capacity of the VLC channel. We further investigate the effects of illumination needs and line-of-sight blockage on the VLC performance. In addition, we explore the non-asymptotic bounds regarding the buffering delay by capitalizing on the effective capacity. Through simulation results, we show the impacts of different physical aspects and data-link QoS needs on the effective capacity and delay bounds.
An Unsupervised Approach to Detect Spam Campaigns that Use Botnets on Twitter
Venue: arXiv cs.SI 2018
Year: 2018

In recent years, Twitter has seen a proliferation of automated accounts or bots that send spam, offer clickbait, compromise security using malware, and attempt to skew public opinion. Previous research estimates that around 9% to 17% of Twitter accounts are bots contributing to between 16% to 56% of tweets on the medium. This paper introduces an unsupervised approach to detect Twitter spam campaigns in real-time. The bot groups we detect tweet duplicate content with shortened embedded URLs over extended periods of time. Our experiments with the detection protocol reveal that bots consistently account for 10% to 50% of tweets generated from 7 popular URL shortening services on Twitter. More importantly, we discover that bots using shortened URLs are connected to large scale spam campaigns that control thousands of domains. There appear to be two distinct mechanisms used to control bot groups and we investigate both in this paper. Our detection system runs 24/7 and actively collects bots involved in spam campaigns and adds them to an evolving database of malicious bots. We make our database of detected bots available for query through a REST API so others can filter tweets from malicious bots to get high quality Twitter datasets for analysis.
Overlapping Coalition Formation via Probabilistic Topic Modeling
Venue: arXiv cs.GT 2018
Year: 2018

Research in cooperative games often assumes that agents know the coalitional values with certainty, and that they can belong to one coalition only. By contrast, this work assumes that the value of a coalition is based on an underlying collaboration structure emerging due to existing but unknown relations among the agents; and that agents can form overlapping coalitions. Specifically, we first propose Relational Rules, a novel representation scheme for cooperative games with overlapping coalitions, which encodes the aforementioned relations, and which extends the well-known MC-nets representation to this setting. We then present a novel decision-making method for decentralized overlapping coalition formation, which exploits probabilistic topic modeling, and in particular, online Latent Dirichlet Allocation. By interpreting formed coalitions as documents, agents can effectively learn topics that correspond to profitable collaboration structures.
Dimensions: re-discovering the ecosystem of scientific information
Venue: arXiv cs.DL 2018
Year: 2018

The overarching aim of this work is to provide a detailed description of the free version of Dimensions (new bibliographic database produced by Digital Science and launched in January 2018). To do this, the work is divided into two differentiated blocks. First, its characteristics, operation and features are described, focusing on its main strengths and weaknesses. Secondly, an analysis of its coverage is carried out (comparing it Scopus and Google Scholar) in order to determine whether the bibliometric indicators offered by Dimensions have an order of magnitude significant enough to be used. To this end, an analysis is carried out at three levels: journals (sample of 20 publications in 'Library & Information Science'), documents (276 articles published by the Journal of informetrics between 2013 and 2015) and authors (28 people awarded with the Derek de Solla Price prize). Preliminary results indicate that Dimensions has coverage of the recent literature superior to Scopus although inferior to Google Scholar. With regard to the number of citations received, Dimensions offers slightly lower figures than Scopus. Despite this, the number of citations in Dimensions exhibits a strong correlation with Scopus and somewhat less (although still significant) with Google Scholar. For this reason, it is concluded that Dimensions is an alternative for carrying out citation studies, being able to rival Scopus (greater coverage and free of charge) and with Google Scholar (greater functionalities for the treatment and data export).
A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI
Venue: arXiv cs.CV 2018
Year: 2018

Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-Magnetic Resonance Imaging (MRI) sheds light on the mechanisms of normal and pathological muscle coordination patterns, yielding improvement in surgical planning, treatment, or rehabilitation procedures. Here, to mine this information, we propose a matrix factorization and probabilistic graphical model framework to produce building blocks and their associated weighting map using motion quantities extracted from tagged-MRI. Our tagged-MRI imaging and accurate voxel-level tracking provide previously unavailable internal tongue motion patterns, thus revealing the inner workings of the tongue during speech or other lingual behaviors. We then employ spectral clustering on the weighting map to identify the cohesive regions defined by the tongue motion that may involve multiple or undocumented regions. To evaluate our method, we perform a series of experiments. We first use two-dimensional images and synthetic data to demonstrate the accuracy of our method. We then use three-dimensional synthetic and \textit{in vivo} tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.
A Deterministic Distributed Algorithm for Exact Weighted All-Pairs Shortest Paths in $\tilde{O}(n^{3/2})$ Rounds
Venue: arXiv cs.DS 2018
Year: 2018

We present a deterministic distributed algorithm to compute all-pairs shortest paths(APSP) in an edge-weighted directed or undirected graph. Our algorithm runs in $\tilde{O}(n^{3/2})$ rounds in the Congest model, where $n$ is the number of nodes in the graph. This is the first $o(n^2)$ rounds deterministic distributed algorithm for the weighted APSP problem. Our algorithm is fairly simple and incorporates a deterministic distributed algorithm we develop for computing a `blocker set' \cite{King99}, which has been used earlier in sequential dynamic computation of APSP.
Review on Optical Image Hiding and Watermarking Techniques
Venue: arXiv cs.CV 2018
Year: 2018

Information security is a critical issue in modern society and image watermarking can effectively prevent unauthorized information access. Optical image watermarking techniques generally have advantages of parallel high-speed processing and multi-dimensional capabilities compared with digital approaches. This paper provides a comprehensive review on the research works related to optical image hiding and watermarking techniques conducted in the past decade. The past research works are focused on two major aspects, various optical systems for image hiding and the methods for embedding optical system output into a host image. A summary of the state-of-the-art works is made from these two perspectives.
Computing Information Quantity as Similarity Measure for Music Classification Task
Venue: arXiv cs.SD 2018
Year: 2018

This paper proposes a novel method that can replace compression-based dissimilarity measure (CDM) in composer estimation task. The main features of the proposed method are clarity and scalability. First, since the proposed method is formalized by the information quantity, reproduction of the result is easier compared with the CDM method, where the result depends on a particular compression program. Second, the proposed method has a lower computational complexity in terms of the number of learning data compared with the CDM method. The number of correct results was compared with that of the CDM for the composer estimation task of five composers of 75 piano musical scores. The proposed method performed better than the CDM method that uses the file size compressed by a particular program.
A survey of comics research in computer science
Venue: arXiv cs.MM 2018
Year: 2018

Graphical novels such as comics and mangas are well known all over the world. The digital transition started to change the way people are reading comics, more and more on smartphones and tablets and less and less on paper. In the recent years, a wide variety of research about comics has been proposed and might change the way comics are created, distributed and read in future years. Early work focuses on low level document image analysis: indeed comic books are complex, they contains text, drawings, balloon, panels, onomatopoeia, etc. Different fields of computer science covered research about user interaction and content generation such as multimedia, artificial intelligence, human-computer interaction, etc. with different sets of values. We propose in this paper to review the previous research about comics in computer science, to state what have been done and to give some insights about the main outlooks.
Automatic Rain and Cicada Chorus Filtering of Bird Acoustic Data
Venue: arXiv cs.SD 2018
Year: 2018

Recording and analysing environmental audio recordings has become a common approach for monitoring the environment. A current problem with performing analyses of environmental recordings is interference from noise that can mask sounds of interest. This makes detecting these sounds more difficult and can require additional resources. While some work has been done to remove stationary noise from environmental recordings, there has been little effort to remove noise from non-stationary sources, such as rain, wind, engines, and animal vocalisations that are not of interest. In this paper, we address the challenge of filtering noise from rain and cicada choruses from recordings containing bird sound. We improve upon previously established classification approaches using acoustic indices and Mel Frequency Cepstral Coefficients (MFCCs) as acoustic features to detect these noise sources, approaching the problem with the motivation of removing these sounds. We investigate the use of acoustic indices, and machine learning classifiers to find the most effective filters. The approach we use enables users to set thresholds to increase or decrease the sensitivity of classification, based on the prediction probability outputted by classifiers. We also propose a novel approach to remove cicada choruses using band-pass filters Our threshold-based approach (Random Forest with Acoustic Indices and Mel Frequency Cepstral Coefficients (MFCCs)) for rain detection achieves an AUC of 0.9881 and is more accurate than existing approaches when set to the same sensitivities. We also detect cicada choruses in our training set with 100% accuracy using 10-folds cross validation. Our cicada filtering approach greatly increased the median signal to noise ratios of affected recordings from 0.53 for unfiltered audio to 1.86 to audio filtered by both the cicada filter and a stationary noise filter.
Multimodal Dynamic Journey Planning
Venue: arXiv cs.DS 2018
Year: 2018

We present multimodal DTM, a new model for multimodal journey planning in public (schedule-based) transport networks. Multimodal DTM constitutes an extension of the dynamic timetable model (DTM), developed originally for unimodal journey planning. Multimodal DTM exhibits a very fast query algorithm, meeting the request for real-time response to best journey queries and an extremely fast update algorithm for updating the timetable information in case of delays. In particular, an experimental study on real-world metropolitan networks demonstrates that our methods compare favorably with other state-of-the-art approaches when public transport along with unrestricted w.r.t. departing time traveling (walking and electric vehicles) is considered.
A Clonal Selection Algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM
Venue: arXiv cs.IR 2018
Year: 2018

Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and Clonal Selection Algorithm with Immunological Memory Cells in this paper.
Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency
Venue: arXiv cs.PF 2018
Year: 2018

Cloud multi-tenancy is typically constrained to a single interactive service colocated with one or more batch, low-priority services, whose performance can be sacrificed when deemed necessary. Approximate computing applications offer the opportunity to enable tighter colocation among multiple applications whose performance is important. We present Pliant, a lightweight cloud runtime that leverages the ability of approximate computing applications to tolerate some loss in their output quality to boost the utilization of shared servers. During periods of high resource contention, Pliant employs incremental and interference-aware approximation to reduce contention in shared resources, and prevent QoS violations for co-scheduled interactive, latency-critical services. We evaluate Pliant across different interactive and approximate computing applications, and show that it preserves QoS for all co-scheduled workloads, while incurring a 2.1\% loss in output quality, on average.