OpenRaiser/Intern-Atlas huggingface dataset:
Mitigation of Human Exposure to RF Fields in Downlink of Millimeter-Wave Cellular Systems
Venue: arXiv cs.NI 2018
Year: 2018

Out of the very few studies that paid proper attention to the harmful health impacts in millimeter-wave (mmW) communications, most of them are concerned about uplink cases due to closer contact with the human body. Our recent study revealed that even the human exposure to radio frequency (RF) fields in downlink mmW technology is not very minimum to be ignored. There were a few RF exposure mitigation techniques for uplinks, but the downlink scenario is hardly paid any attention. However, this paper proposes a downlink protocol for mmW cellular communications that achieves the maximum data rate while keeping the impacts on human health minimized. Our results show that the proposed technique lowers both power density (PD) and specific absorption rate (SAR) compared to the typical protocol, with only slight sacrifice in data rates.
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Computer vision technologies are very attractive for practical applications running on embedded systems. For such an application, it is desirable for the deployed algorithms to run in high-speed and require no offline training. To develop a single-target tracking algorithm with these properties, we propose an ensemble of the kernelized correlation filters (KCF), we call it EnKCF. A committee of KCFs is specifically designed to address the variations in scale and translation of moving objects. To guarantee a high-speed run-time performance, we deploy each of KCFs in turn, instead of applying multiple KCFs to each frame. To minimize any potential drifts between individual KCFs transition, we developed a particle filter. Experimental results showed that the performance of ours is, on average, 70.10% for precision at 20 pixels, 53.00% for success rate for the OTB100 data, and 54.50% and 40.2% for the UAV123 data. Experimental results showed that our method is better than other high-speed trackers over 5% on precision on 20 pixels and 10-20% on AUC on average. Moreover, our implementation ran at 340 fps for the OTB100 and at 416 fps for the UAV123 dataset that is faster than DCF (292 fps) for the OTB100 and KCF (292 fps) for the UAV123. To increase flexibility of the proposed EnKCF running on various platforms, we also explored different levels of deep convolutional features.
Boundary-based Image Forgery Detection by Fast Shallow CNN
Venue: arXiv cs.CV 2018
Year: 2018

Image forgery detection is the task of detecting and localizing forged parts in tampered images. Previous works mostly focus on high resolution images using traces of resampling features, demosaicing features or sharpness of edges. However, a good detection method should also be applicable to low resolution images because compressed or resized images are common these days. To this end, we propose a Shallow Convolutional Neural Network(SCNN), capable of distinguishing the boundaries of forged regions from original edges in low resolution images. SCNN is designed to utilize the information of chroma and saturation. Based on SCNN, two approaches that are named Sliding Windows Detection (SWD) and Fast SCNN, respectively, are developed to detect and localize image forgery region. In this paper, we substantiate that Fast SCNN can detect drastic change of chroma and saturation. In image forgery detection experiments Our model is evaluated on the CASIA 2.0 dataset. The results show that Fast SCNN performs well on low resolution images and achieves significant improvements over the state-of-the-art.
Learning to Speed Up Query Planning in Graph Databases
Venue: arXiv cs.DB 2018
Year: 2018

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing - Query Planning - is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including DBpedia, YAGO, and Freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.
Attentive Recurrent Tensor Model for Community Question Answering
Venue: arXiv cs.CL 2018
Year: 2018

A major challenge to the problem of community question answering is the lexical and semantic gap between the sentence representations. Some solutions to minimize this gap includes the introduction of extra parameters to deep models or augmenting the external handcrafted features. In this paper, we propose a novel attentive recurrent tensor network for solving the lexical and semantic gap in community question answering. We introduce token-level and phrase-level attention strategy that maps input sequences to the output using trainable parameters. Further, we use the tensor parameters to introduce a 3-way interaction between question, answer and external features in vector space. We introduce simplified tensor matrices with L2 regularization that results in smooth optimization during training. The proposed model achieves state-of-the-art performance on the task of answer sentence selection (TrecQA and WikiQA datasets) while outperforming the current state-of-the-art on the tasks of best answer selection (Yahoo! L4) and answer triggering task (WikiQA).
Neural Multi-task Learning in Automated Assessment
Venue: arXiv cs.CL 2018
Year: 2018

Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.
Monocular Imaging-based Autonomous Tracking for Low-cost Quad-rotor Design - TraQuad
Venue: arXiv cs.RO 2018
Year: 2018

TraQuad is an autonomous tracking quadcopter capable of tracking any moving (or static) object like cars, humans, other drones or any other object on-the-go. This article describes the applications and advantages of TraQuad and the reduction in cost (to about 250$) that has been achieved so far using the hardware and software capabilities and our custom algorithms wherever needed. This description is backed by strong data and the research analyses which have been drawn out of extant information or conducted on own when necessary. This also describes the development of completely autonomous (even GPS is optional) low-cost drone which can act as a major platform for further developments in automation, transportation, reconnaissance and more. We describe our ROS Gazebo simulator and our STATUS algorithms which form the core of our development of our object tracking drone for generic purposes.
Time-Delay Origins of Fundamental Tradeoffs Between Risk of Large Fluctuations and Network Connectivity
Venue: arXiv cs.SY 2018
Year: 2018

For the class of noisy time-delay linear consensus networks, we obtain explicit formulas for risk of large fluctuations of a scalar observable as a function of Laplacian spectrum and its eigenvectors. It is shown that there is an intrinsic tradeoff between risk and effective resistance of the underlying coupling graph of the network. The main implication is that increasing network connectivity, increases the risk of large fluctuations. For vector-valued observables, we obtain computationally tractable lower and upper bounds for joint risk measures. Then, we study behavior of risk measures for networks with specific graph topologies and show how risk scales with network size.
Sector-Based Radio Resource Allocation (SBRRA) Algorithm for Better Quality of Service and Experience in Device-to-Device (D2D) Communication
Venue: arXiv cs.NI 2018
Year: 2018

The mounting content sharing among users has resulted in a considerable rise in wireless data traffic, pressurizing the cellular networks to undergo a suitable upheaval. A competent technology of the fifth-generation networks (5G) for efficiently supporting proximity-based applications is Device-to-Device (D2D) communication, underlaying cellular networks. Significant advances have been made till date, for allocating resources to D2D users in cellular networks, such that sharing of spectral resources between cellular and D2D users is carried out in a coordinated manner. In this paper, a sector-based radio resource allocation (SBRRA) algorithm for resource block allocation to D2D pairs has been proposed, where the number of resource blocks (RBs) is allocated to each D2D pair in an adaptive manner, based on the demanded application by each pair. Different applications demand a varying number of RBs, in accordance with their priority. This algorithm focusses on the use of sectored antennas at the base station, for a better performance and low complexity. Extensive simulations are carried out, considering real-time scenario, for ensuring satisfactory Quality of Service (QoS) and Quality of Experience (QoE) by the users. The efficiency of the proposed scheme is proved by comparing it with the RB allocation using Hidden Markov Model (HMM).
Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling
Venue: arXiv cs.DS 2018
Year: 2018

The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree. Our current paper generalizes the result to Markov networks of tree width $\leq k$, for every fixed $k\geq 2$. In particular, we prove that approximation of a finite probabilistic system with such Markov networks has the minimum information loss when the network topology is achieved with a maximum spanning $k$-tree. While constructing a maximum spanning $k$-tree is intractable for even $k=2$, we show that polynomial algorithms can be ensured by a sufficient condition accommodated by many meaningful applications. In particular, we prove an efficient algorithm for learning the optimal topology of higher order correlations among random variables that belong to an underlying linear structure.
On The Effect of Vibration on Shape Sensing of Continuum Manipulators Using Fiber Bragg Gratings
Venue: arXiv cs.RO 2018
Year: 2018

Fiber Bragg Grating (FBG) has shown great potential in shape and force sensing of continuum manipulators (CM) and biopsy needles. In the recent years, many researchers have studied different manufacturing and modeling techniques of FBG-based force and shape sensors for medical applications. These studies mainly focus on obtaining shape and force information in a static (or quasi-static) environment. In this paper, however, we study and evaluate dynamic environments where the FBG data is affected by vibration caused by a harmonic force e.g. a rotational debriding tool harmonically exciting the CM and the FBG-based shape sensor. In such situations, appropriate pre-processing of the FBG signal is necessary in order to infer correct information from the raw signal. We look at an example of such dynamic environments in the less invasive treatment of osteolysis by studying the FBG data both in time- and frequency-domain in presence of vibration due to a debriding tool rotating inside the lumen of the CM.
The Capacity of Degraded Cognitive Interference Channel with Unidirectional Destination Cooperation
Venue: arXiv cs.IT 2018
Year: 2018

Previous works established the capacity region for some special cases of discrete memoryless degraded cognitive interference channel (CIC) with unidirectional destination cooperation (UDC). In this letter, we characterize the capacity region of the general discrete memoryless degraded CIC-UDC. The obtained results imply that the capacity region is achieved by the Gel'fand-Pinsker coding at the cognitive transmitter, superposition coding at the primary transmitter and decode-and-forward at the relay. Furthermore, using this general result and a novel converse analysis, we establish the capacity of the Gaussian degraded CIC-UDC, which had been open until this work.
An Efficient Density-based Clustering Algorithm for Higher-Dimensional Data
Venue: arXiv cs.DB 2018
Year: 2018

DBSCAN is a typically used clustering algorithm due to its clustering ability for arbitrarily-shaped clusters and its robustness to outliers. Generally, the complexity of DBSCAN is O(n^2) in the worst case, and it practically becomes more severe in higher dimension. Grid-based DBSCAN is one of the recent improved algorithms aiming at facilitating efficiency. However, the performance of grid-based DBSCAN still suffers from two problems: neighbour explosion and redundancies in merging, which make the algorithms infeasible in high-dimensional space. In this paper, we propose a novel algorithm named GDPAM attempting to extend Grid-based DBSCAN to higher data dimension. In GDPAM, a bitmap indexing is utilized to manage non-empty grids so that the neighbour grid queries can be performed efficiently. Furthermore, we adopt an efficient union-find algorithm to maintain the clustering information in order to reduce redundancies in the merging. The experimental results on both real-world and synthetic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art exact/approximate DBSCAN and suggests a good scalability.
Extreme Learning Machine with Local Connections
Venue: arXiv cs.LG 2018
Year: 2018

This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen rather than to be learned. To this end, we propose a modified ELM, called ELM-LC (ELM with local connections), which is designed for the sparsification of the input-hidden weights as follows: The hidden nodes and the input nodes are divided respectively into several corresponding groups, and an input node group is fully connected with its corresponding hidden node group, but is not connected with any other hidden node group. As in the usual ELM, the hidden-input weights are randomly given, and the hidden-output weights are obtained through a least square learning. In the numerical simulations on some benchmark problems, the new ELM-CL behaves better than the traditional ELM.
Public Sentiment and Demand for Used Cars after A Large-Scale Disaster: Social Media Sentiment Analysis with Facebook Pages
Venue: arXiv cs.SI 2018
Year: 2018

There have been various studies analyzing public sentiment after a large-scale disaster. However, few studies have focused on the relationship between public sentiment on social media and its results on people's activities in the real world. In this paper, we conduct a long-term sentiment analysis after the Great East Japan Earthquake and Tsunami of 2011 using Facebook Pages with the aim of investigating the correlation between public sentiment and people's actual needs in areas damaged by water disasters. In addition, we try to analyze whether different types of disaster-related communication created different kinds of relationships on people's activities in the physical world. Our analysis reveals that sentiment of geo-info-related communication, which might be affected by sentiment inside a damaged area, had a positive correlation with the prices of used cars in the damaged area. On the other hand, the sentiment of disaster-interest-based-communication, which might be affected more by people who were interested in the disaster, but were outside the damaged area, had a negative correlation with the prices of used cars. The result could be interpreted to mean that when people begin to recover, used-car prices rise because they become more positive in their sentiment. This study suggests that, for long-term disaster-recovery analysis, we need to consider the different characteristics of online communication posted by locals directly affected by the disaster and non-locals not directly affected by the disaster.
ACGreGate: A Framework for Practical Access Control for Applications using Weakly Consistent Databases
Venue: arXiv cs.DB 2018
Year: 2018

Scalable and highly available systems often require data stores that offer weaker consistency guarantees than traditional relational databases systems. The correctness of these applications highly depends on the resilience of the application model against data inconsistencies. In particular regarding application security, it is difficult to determine which inconsistencies can be tolerated and which might lead to security breaches. In this paper, we discuss the problem of how to develop an access control layer for applications using weakly consistent data stores without loosing the performance benefits gained by using weaker consistency models. We present ACGreGate, a Java framework for implementing correct access control layers for applications using weakly consistent data stores. Under certain requirements on the data store, ACGreGate ensures that the access control layer operates correctly with respect to dynamically adaptable security policies. We used ACGreGate to implement the access control layer of a student management system. This case study shows that practically useful security policies can be implemented with the framework incurring little overhead. A comparison with a setup using a centralized server shows the benefits of using ACGreGate for scalability of the service to geo-scale.
BiographyNet: Extracting Relations Between People and Events
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes BiographyNet, a digital humanities project (2012-2016) that brings together researchers from history, computational linguistics and computer science. The project uses data from the Biography Portal of the Netherlands (BPN), which contains approximately 125,000 biographies from a variety of Dutch biographical dictionaries from the eighteenth century until now, describing around 76,000 individuals. BiographyNet's aim is to strengthen the value of the portal and comparable biographical datasets for historical research, by improving the search options and the presentation of its outcome, with a historically justified NLP pipeline that works through a user evaluated demonstrator. The project's main target group are professional historians. The project therefore worked with two key concepts: "provenance" -understood as a term allowing for both historical source criticism and for references to data-management and programming interventions in digitized sources; and "perspective" interpreted as inherent uncertainty concerning the interpretation of historical results.
Invitation to Real Complexity Theory: Algorithmic Foundations to Reliable Numerics with Bit-Costs
Venue: arXiv cs.CC 2018
Year: 2018

While concepts and tools from Theoretical Computer Science are regularly applied to, and significantly support, software development for discrete problems, Numerical Engineering largely employs recipes and methods whose correctness and efficiency is demonstrated empirically. We advertise REAL COMPLEXITY THEORY: a resource-oriented foundation to rigorous computations over continuous universes such as real numbers, vectors, sequences, continuous functions, and Euclidean subsets: in the bit-model by approximation up to given absolute error. It offers sound semantics (e.g. of comparisons/tests), closure under composition, realistic runtime predictions, and proofs of algorithmic optimality by relating to known classes like NP, #P, PSPACE.
SecSens: Secure State Estimation with Application to Localization and Time Synchronization
Venue: arXiv cs.CR 2018
Year: 2018

Research evidence in Cyber-Physical Systems (CPS) shows that the introduced tight coupling of information technology with physical sensing and actuation leads to more vulnerability and security weaknesses. But, the traditional security protection mechanisms of CPS focus on data encryption while neglecting the sensors which are vulnerable to attacks in the physical domain. Accordingly, researchers attach utmost importance to the problem of state estimation in the presence of sensor attacks. In this work, we present SecSens, a novel approach for secure nonlinear state estimation in the presence of modeling and measurement noise. SecSens consists of two independent algorithms, namely, SecEKF and SecOPT, which are based on Extended Kalman Filter and Maximum Likelihood Estimation, respectively. We adopt a holistic approach to introduce security awareness among state estimation algorithms without requiring specialized hardware, or cryptographic techniques. We apply SecSens to securely localize and time synchronize networked mobile devices. SecSens provides good performance at run-time several order of magnitude faster than the state of art solutions under the presence of powerful attacks. Our algorithms are evaluated on a testbed with static nodes and a mobile quadrotor all equipped with commercial ultra-wide band wireless devices.
Word Level Font-to-Font Image Translation using Convolutional Recurrent Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Conversion of one font to another font is very useful in real life applications. In this paper, we propose a Convolutional Recurrent Generative model to solve the word level font transfer problem. Our network is able to convert the font style of any printed text images from its current font to the required font. The network is trained end-to-end for the complete word images. Thus it eliminates the necessary pre-processing steps, like character segmentations. We extend our model to conditional setting that helps to learn one-to-many mapping function. We employ a novel convolutional recurrent model architecture in the Generator that efficiently deals with the word images of arbitrary width. It also helps to maintain the consistency of the final images after concatenating the generated image patches of target font. Besides, the Generator and the Discriminator network, we employ a Classification network to classify the generated word images of converted font style to their subsequent font categories. Most of the earlier works related to image translation are performed on square images. Our proposed architecture is the first work which can handle images of varying widths. Word images generally have varying width depending on the number of characters present. Hence, we test our model on a synthetically generated font dataset. We compare our method with some of the state-of-the-art methods for image translation. The superior performance of our network on the same dataset proves the ability of our model to learn the font distributions.