OpenRaiser/Intern-Atlas huggingface dataset:
A Semi-Supervised Approach to Detecting Stance in Tweets
Venue: arXiv cs.CL 2017
Year: 2017

Stance classification aims to identify, for a particular issue under discussion, whether the speaker or author of a conversational turn has Pro (Favor) or Con (Against) stance on the issue. Detecting stance in tweets is a new task proposed for SemEval-2016 Task6, involving predicting stance for a dataset of tweets on the topics of abortion, atheism, climate change, feminism and Hillary Clinton. Given the small size of the dataset, our team created our own topic-specific training corpus by developing a set of high precision hashtags for each topic that were used to query the twitter API, with the aim of developing a large training corpus without additional human labeling of tweets for stance. The hashtags selected for each topic were predicted to be stance-bearing on their own. Experimental results demonstrate good performance for our features for opinion-target pairs based on generalizing dependency features using sentiment lexicons.
Learning Dilation Factors for Semantic Segmentation of Street Scenes
Venue: arXiv cs.CV 2017
Year: 2017

Contextual information is crucial for semantic segmentation. However, finding the optimal trade-off between keeping desired fine details and at the same time providing sufficiently large receptive fields is non trivial. This is even more so, when objects or classes present in an image significantly vary in size. Dilated convolutions have proven valuable for semantic segmentation, because they allow to increase the size of the receptive field without sacrificing image resolution. However, in current state-of-the-art methods, dilation parameters are hand-tuned and fixed. In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image
Venue: arXiv cs.CV 2017
Year: 2017

Recently, more and more attention is drawn to the field of medical image synthesis across modalities. Among them, the synthesis of computed tomography (CT) image from T1-weighted magnetic resonance (MR) image is of great importance, although the mapping between them is highly complex due to large gaps of appearances of the two modalities. In this work, we aim to tackle this MR-to-CT synthesis by a novel deep embedding convolutional neural network (DECNN). Specifically, we generate the feature maps from MR images, and then transform these feature maps forward through convolutional layers in the network. We can further compute a tentative CT synthesis from the midway of the flow of feature maps, and then embed this tentative CT synthesis back to the feature maps. This embedding operation results in better feature maps, which are further transformed forward in DECNN. After repeat-ing this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN. We have validated our proposed method on both brain and prostate datasets, by also compar-ing with the state-of-the-art methods. Experimental results suggest that our DECNN (with repeated embedding op-erations) demonstrates its superior performances, in terms of both the perceptive quality of the synthesized CT image and the run-time cost for synthesizing a CT image.
Composition by Conversation
Venue: arXiv cs.SD 2017
Year: 2017

Most musical programming languages are developed purely for coding virtual instruments or algorithmic compositions. Although there has been some work in the domain of musical query languages for music information retrieval, there has been little attempt to unify the principles of musical programming and query languages with cognitive and natural language processing models that would facilitate the activity of composition by conversation. We present a prototype framework, called MusECI, that merges these domains, permitting score-level algorithmic composition in a text editor while also supporting connectivity to existing natural language processing frameworks.
A Backward-traversal-based Approach for Symbolic Model Checking of Uniform Strategies for Constrained Reachability
Venue: arXiv cs.LO 2017
Year: 2017

Since the introduction of Alternating-time Temporal Logic (ATL), many logics have been proposed to reason about different strategic capabilities of the agents of a system. In particular, some logics have been designed to reason about the uniform memoryless strategies of such agents. These strategies are the ones the agents can effectively play by only looking at what they observe from the current state. ATL_ir can be seen as the core logic to reason about such uniform strategies. Nevertheless, its model-checking problem is difficult (it requires a polynomial number of calls to an NP oracle), and practical algorithms to solve it appeared only recently. This paper proposes a technique for model checking uniform memoryless strategies. Existing techniques build the strategies from the states of interest, such as the initial states, through a forward traversal of the system. On the other hand, the proposed approach builds the winning strategies from the target states through a backward traversal, making sure that only uniform strategies are explored. Nevertheless, building the strategies from the ground up limits its applicability to constrained reachability objectives only. This paper describes the approach in details and compares it experimentally with existing approaches implemented into a BDD-based framework. These experiments show that the technique is competitive on the cases it can handle.
On the Complexity of ATL and ATL* Module Checking
Venue: arXiv cs.LO 2017
Year: 2017

Module checking has been introduced in late 1990s to verify open systems, i.e., systems whose behavior depends on the continuous interaction with the environment. Classically, module checking has been investigated with respect to specifications given as CTL and CTL* formulas. Recently, it has been shown that CTL (resp., CTL*) module checking offers a distinctly different perspective from the better-known problem of ATL (resp., ATL*) model checking. In particular, ATL (resp., ATL*) module checking strictly enhances the expressiveness of both CTL (resp., CTL*) module checking and ATL (resp. ATL*) model checking. In this paper, we provide asymptotically optimal bounds on the computational cost of module checking against ATL and ATL*, whose upper bounds are based on an automata-theoretic approach. We show that module-checking for ATL is EXPTIME-complete, which is the same complexity of module checking against CTL. On the other hand, ATL* module checking turns out to be 3EXPTIME-complete, hence exponentially harder than CTL* module checking.
Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE
Venue: arXiv cs.IT 2017
Year: 2017

In cellular massive Machine-Type Communications (MTC), a device can transmit directly to the base station (BS) or through an aggregator (intermediate node). While direct device-BS communication has recently been in the focus of 5G/3GPP research and standardization efforts, the use of aggregators remains a less explored topic. In this paper we analyze the deployment scenarios in which aggregators can perform cellular access on behalf of multiple MTC devices. We study the effect of packet bundling at the aggregator, which alleviates overhead and resource waste when sending small packets. The aggregators give rise to a tradeoff between access congestion and resource starvation and we show that packet bundling can minimize resource starvation, especially for smaller numbers of aggregators. Under the limitations of the considered model, we investigate the optimal settings of the network parameters, in terms of number of aggregators and packet-bundle size. Our results show that, in general, data aggregation can benefit the uplink massive MTC in LTE, by reducing the signalling overhead.
An iterative approximate method of solving boundary value problems using dual Bernstein polynomials
Venue: arXiv cs.NA 2017
Year: 2017

In this paper, we present a new iterative approximate method of solving boundary value problems. The idea is to compute approximate polynomial solutions in the Bernstein form using least squares approximation combined with some properties of dual Bernstein polynomials which guarantee high efficiency of our approach. The method can deal with both linear and nonlinear differential equations. Moreover, not only second order differential equations can be solved but also higher order differential equations. Illustrative examples confirm the versatility of our method.
Maximizing the Smallest Eigenvalue of a Symmetric Matrix: A Submodular Optimization Approach
Venue: arXiv cs.SY 2017
Year: 2017

This paper studies the problem of selecting a submatrix of a positive definite matrix in order to achieve a desired bound on the smallest eigenvalue of the submatrix. Maximizing this smallest eigenvalue has applications to selecting input nodes in order to guarantee consensus of networks with negative edges as well as maximizing the convergence rate of distributed systems. We develop a submodular optimization approach to maximizing the smallest eigenvalue by first proving that positivity of the eigenvalues of a submatrix can be characterized using the probability distribution of the quadratic form induced by the submatrix. We then exploit that connection to prove that positive-definiteness of a submatrix can be expressed as a constraint on a submodular function. We prove that our approach results in polynomial-time algorithms with provable bounds on the size of the submatrix. We also present generalizations to non-symmetric matrices, alternative sufficient conditions for the smallest eigenvalue to exceed a desired bound that are valid for Laplacian matrices, and a numerical evaluation.
Multi-modal Conditional Attention Fusion for Dimensional Emotion Prediction
Venue: arXiv cs.CV 2017
Year: 2017

Continuous dimensional emotion prediction is a challenging task where the fusion of various modalities usually achieves state-of-the-art performance such as early fusion or late fusion. In this paper, we propose a novel multi-modal fusion strategy named conditional attention fusion, which can dynamically pay attention to different modalities at each time step. Long-short term memory recurrent neural networks (LSTM-RNN) is applied as the basic uni-modality model to capture long time dependencies. The weights assigned to different modalities are automatically decided by the current input features and recent history information rather than being fixed at any kinds of situation. Our experimental results on a benchmark dataset AVEC2015 show the effectiveness of our method which outperforms several common fusion strategies for valence prediction.
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
Venue: arXiv cs.CV 2017
Year: 2017

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method.
A Neural Network Based on Synchronized Pairs of Nano-Oscillators
Venue: arXiv cs.ET 2017
Year: 2017

Artificial neural networks are intensively used to perform cognitive tasks such as image classification on traditional computers. With the end of CMOS scaling and increasing demand for efficient neural networks, alternative architectures implementing neural functions efficiently are being studied. This study leverages the demonstrated frequency tuning capabilities of compact nano-oscillators and their synchronization dynamics to implement a neuron using a pair of synchronized oscillators, and which features an unconventional response curve. We show that this compact neuron can naturally implement generic logic gates, including XOR. A simulated oscillator-based neural network is then shown to achieve results equivalent to standard approaches on two reference classification tasks. Finally, the performance of the system is evaluated in the presence of oscillator phase noise, an important issue of oscillating nanodevices. These results open the way for the design of alternative architectures adapted to efficient neural network execution.
Learning from lions: inferring the utility of agents from their trajectories
Venue: arXiv cs.AI 2017
Year: 2017

We build a model using Gaussian processes to infer a spatio-temporal vector field from observed agent trajectories. Significant landmarks or influence points in agent surroundings are jointly derived through vector calculus operations that indicate presence of sources and sinks. We evaluate these influence points by using the Kullback-Leibler divergence between the posterior and prior Laplacian of the inferred spatio-temporal vector field. Through locating significant features that influence trajectories, our model aims to give greater insight into underlying causal utility functions that determine agent decision-making. A key feature of our model is that it infers a joint Gaussian process over the observed trajectories, the time-varying vector field of utility and canonical vector calculus operators. We apply our model to both synthetic data and lion GPS data collected at the Bubye Valley Conservancy in southern Zimbabwe.
Automated Dyadic Data Recorder (ADDR) Framework and Analysis of Facial Cues in Deceptive Communication
Venue: arXiv cs.HC 2017
Year: 2017

We developed an online framework that can automatically pair two crowd-sourced participants, prompt them to follow a research protocol, and record their audio and video on a remote server. The framework comprises two web applications: an Automatic Quality Gatekeeper for ensuring only high quality crowd-sourced participants are recruited for the study, and a Session Controller which directs participants to play a research protocol, such as an interrogation game. This framework was used to run a research study for analyzing facial expressions during honest and deceptive communication using a novel interrogation protocol. The protocol gathers two sets of nonverbal facial cues in participants: features expressed during questions relating to the interrogation topic and features expressed during control questions. The framework and protocol were used to gather 151 dyadic conversations (1.3 million video frames). Interrogators who were lied to expressed the smile-related lip corner puller cue more often than interrogators who were being told the truth, suggesting that facial cues from interrogators may be useful in evaluating the honesty of witnesses in some contexts. Overall, these results demonstrate that this framework is capable of gathering high quality data which can identify statistically significant results in a communication study.
Intelligent Disaster Response via Social Media Analysis - A Survey
Venue: arXiv cs.SI 2017
Year: 2017

The success of a disaster relief and response process is largely dependent on timely and accurate information regarding the status of the disaster, the surrounding environment, and the affected people. This information is primarily provided by first responders on-site and can be enhanced by the firsthand reports posted in real-time on social media. Many tools and methods have been developed to automate disaster relief by extracting, analyzing, and visualizing actionable information from social media. However, these methods are not well integrated in the relief and response processes and the relation between the two requires exposition for further advancement. In this survey, we review the new frontier of intelligent disaster relief and response using social media, show stages of disasters which are reflected on social media, establish a connection between proposed methods based on social media and relief efforts by first responders, and outline pressing challenges and future research directions.
Addendum to: Edge-Unfolding Nearly Flat Convex Caps
Venue: arXiv cs.CG 2017
Year: 2017

This addendum to [O'R17] establishes that a nearly flat acutely triangulated convex cap in the sense of that paper can be edge-unfolded even if closed to a polyhedron by adding the convex polygonal base under the cap.
Predicting the Popularity of Topics based on User Sentiment in Microblogging Websites
Venue: arXiv cs.SI 2017
Year: 2017

Behavioral economics show us that emotions play an important role in individual behavior and decision-making. Does this also affect collective decision making in a community? Here we investigate whether the community sentiment energy of a topic is related to the spreading popularity of the topic. To compute the community sentiment energy of a topic, we first analyze the sentiment of a user on the key phrases of the topic based on the recent tweets of the user. Then we compute the total sentiment energy of all users in the community on the topic based on the Markov Random Field (MRF) model and graph entropy model. Experiments on two communities find the linear correlation between the community sentiment energy and the real spreading popularity of topics. Based on the finding, we proposed two models to predict the popularity of topics. Experimental results show the effectiveness of the two models and the helpful of sentiment in predicting the popularity of topics. Experiments also show that community sentiment affects collective decision making of spreading a topic or not in the community.
Intelligent Subset Selection of Power Generators for Economic Dispatch
Venue: arXiv cs.CE 2017
Year: 2017

Sustainable and economical generation of electrical power is an essential and mandatory component of infrastructure in today's world. Optimal generation (generator subset selection) of power requires a careful evaluation of various factors like type of source, generation, transmission & storage capacities, congestion among others which makes this a difficult task. We created a grid to simulate various conditions including stimuli like generator supply, weather and load demand using Siemens PSS/E software and this data is trained using deep learning methods and subsequently tested. The results are highly encouraging. As per our knowledge, this is the first paper to propose a working and scalable deep learning model for this problem.
Imperfect Knowledge in Autonomous Urban Traffic Manoeuvres
Venue: arXiv cs.LO 2017
Year: 2017

Urban Multi-lane Spatial Logic (UMLSL) was introduced in [13] for proving safety (collision freedom) in autonomous urban traffic manoeuvres with perfect knowledge. We now consider a concept of imperfect knowledge, where cars have less information about other cars. To this end, we introduce the concept of a multi-view and propose crossing controllers using broadcast communication with data constraints for turning manoeuvres at intersections.
Run-Time Risk Mitigation in Automated Vehicles: A Model for Studying Preparatory Steps
Venue: arXiv cs.SY 2017
Year: 2017

We assume that autonomous or highly automated driving (AD) will be accompanied by tough assurance obligations exceeding the requirements of even recent revisions of ISO 26262 or SOTIF. Hence, automotive control and safety engineers have to (i) comprehensively analyze the driving process and its control loop, (ii) identify relevant hazards stemming from this loop, (iii) establish feasible automated measures for the effective mitigation of these hazards or the alleviation of their consequences. By studying an example, this article investigates some achievements in the modeling for the steps (i), (ii), and (iii), amenable to formal verification of desired properties derived from potential assurance obligations such as the global existence of an effective mitigation strategy. In addition, the proposed approach is meant for step-wise refinement towards the automated synthesis of AD safety controllers implementing such properties.
Calibration of depth cameras using denoised depth images
Venue: arXiv cs.CV 2017
Year: 2017

Depth sensing devices have created various new applications in scientific and commercial research with the advent of Microsoft Kinect and PMD (Photon Mixing Device) cameras. Most of these applications require the depth cameras to be pre-calibrated. However, traditional calibration methods using a checkerboard do not work very well for depth cameras due to the low image resolution. In this paper, we propose a depth calibration scheme which excels in estimating camera calibration parameters when only a handful of corners and calibration images are available. We exploit the noise properties of PMD devices to denoise depth measurements and perform camera calibration using the denoised depth as an additional set of measurements. Our synthetic and real experiments show that our depth denoising and depth based calibration scheme provides significantly better results than traditional calibration methods.
Locating 3D Object Proposals: A Depth-Based Online Approach
Venue: arXiv cs.CV 2017
Year: 2017

2D object proposals, quickly detected regions in an image that likely contain an object of interest, are an effective approach for improving the computational efficiency and accuracy of object detection in color images. In this work, we propose a novel online method that generates 3D object proposals in a RGB-D video sequence. Our main observation is that depth images provide important information about the geometry of the scene. Diverging from the traditional goal of 2D object proposals to provide a high recall (lots of 2D bounding boxes near potential objects), we aim for precise 3D proposals. We leverage on depth information per frame and multi-view scene information to obtain accurate 3D object proposals. Using efficient but robust registration enables us to combine multiple frames of a scene in near real time and generate 3D bounding boxes for potential 3D regions of interest. Using standard metrics, such as Precision-Recall curves and F-measure, we show that the proposed approach is significantly more accurate than the current state-of-the-art techniques. Our online approach can be integrated into SLAM based video processing for quick 3D object localization. Our method takes less than a second in MATLAB on the UW-RGBD scene dataset on a single thread CPU and thus, has potential to be used in low-power chips in Unmanned Aerial Vehicles (UAVs), quadcopters, and drones.
On the correlation between a level of structure order and properties of composites. In Memory of Yu.L. Klimontovich
Venue: arXiv cs.GR 2017
Year: 2017

Proposed the computerized method for calculating the relative level of order composites. Correlation between a level of structure order and properties of solids is shown. Discussed the possibility of clarifying the terminology used in describing the structure.