OpenRaiser/Intern-Atlas huggingface dataset:
Ultra-Fast, Low-Storage, Highly Effective Coarse-grained Selection in Retrieval-based Chatbot by Using Deep Semantic Hashing
Venue: arXiv cs.CL 2020
Year: 2020

We study the coarse-grained selection module in retrieval-based chatbot. Coarse-grained selection is a basic module in a retrieval-based chatbot, which constructs a rough candidate set from the whole database to speed up the interaction with customers. So far, there are two kinds of approaches for coarse-grained selection module: (1) sparse representation; (2) dense representation. To the best of our knowledge, there is no systematic comparison between these two approaches in retrieval-based chatbots, and which kind of method is better in real scenarios is still an open question. In this paper, we first systematically compare these two methods from four aspects: (1) effectiveness; (2) index stoarge; (3) search time cost; (4) human evaluation. Extensive experiment results demonstrate that dense representation method significantly outperforms the sparse representation, but costs more time and storage occupation. In order to overcome these fatal weaknesses of dense representation method, we propose an ultra-fast, low-storage, and highly effective Deep Semantic Hashing Coarse-grained selection method, called DSHC model. Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two autoencoder models is stacked on a trained dense representation model, and three loss functions are designed to optimize it. The hash codes provided by hashing optimizing module effectively preserve the rich semantic and similarity information in dense vectors. Extensive experiment results prove that, our proposed DSHC model can achieve much faster speed and lower storage than sparse representation, with limited performance loss compared with dense representation. Besides, our source codes have been publicly released for future research.
A fully pipelined FPGA accelerator for scale invariant feature transform keypoint descriptor matching,
Venue: arXiv cs.CV 2020
Year: 2020

The scale invariant feature transform (SIFT) algorithm is considered a classical feature extraction algorithm within the field of computer vision. SIFT keypoint descriptor matching is a computationally intensive process due to the amount of data consumed. In this work, we designed a novel fully pipelined hardware accelerator architecture for SIFT keypoint descriptor matching. The accelerator core was implemented and tested on a field programmable gate array (FPGA). The proposed hardware architecture is able to properly handle the memory bandwidth necessary for a fully-pipelined implementation and hits the roofline performance model, achieving the potential maximum throughput. The fully pipelined matching architecture was designed based on the consine angle distance method. Our architecture was optimized for 16-bit fixed-point operations and implemented on hardware using a Xilinx Zynq-based FPGA development board. Our proposed architecture shows a noticeable reduction of area resources compared with its counterparts in literature, while maintaining high throughput by alleviating memory bandwidth restrictions. The results show a reduction in consumed device resources of up to 91 percent in LUTs and 79 percent of BRAMs. Our hardware implementation is 15.7 times faster than the comparable software approach.
Multi-Modal Depth Estimation Using Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

This paper addresses the problem of dense depth predictions from sparse distance sensor data and a single camera image on challenging weather conditions. This work explores the significance of different sensor modalities such as camera, Radar, and Lidar for estimating depth by applying Deep Learning approaches. Although Lidar has higher depth-sensing abilities than Radar and has been integrated with camera images in lots of previous works, depth estimation using CNN's on the fusion of robust Radar distance data and camera images has not been explored much. In this work, a deep regression network is proposed utilizing a transfer learning approach consisting of an encoder where a high performing pre-trained model has been used to initialize it for extracting dense features and a decoder for upsampling and predicting desired depth. The results are demonstrated on Nuscenes, KITTI, and a Synthetic dataset which was created using the CARLA simulator. Also, top-view zoom-camera images captured from the crane on a construction site are evaluated to estimate the distance of the crane boom carrying heavy loads from the ground to show the usability in safety-critical applications.
DenseHMM: Learning Hidden Markov Models by Learning Dense Representations
Venue: arXiv cs.LG 2020
Year: 2020

We propose DenseHMM - a modification of Hidden Markov Models (HMMs) that allows to learn dense representations of both the hidden states and the observables. Compared to the standard HMM, transition probabilities are not atomic but composed of these representations via kernelization. Our approach enables constraint-free and gradient-based optimization. We propose two optimization schemes that make use of this: a modification of the Baum-Welch algorithm and a direct co-occurrence optimization. The latter one is highly scalable and comes empirically without loss of performance compared to standard HMMs. We show that the non-linearity of the kernelization is crucial for the expressiveness of the representations. The properties of the DenseHMM like learned co-occurrences and log-likelihoods are studied empirically on synthetic and biomedical datasets.
Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment
Venue: arXiv cs.CV 2020
Year: 2020

Evaluating neurological disorders such as Parkinson's disease (PD) is a challenging task that requires the assessment of several motor and non-motor functions. In this paper, we present an end-to-end deep learning framework to measure PD severity in two important components, hand movement and gait, of the Unified Parkinson's Disease Rating Scale (UPDRS). Our method leverages on an Inflated 3D CNN trained by a temporal segment framework to learn spatial and long temporal structure in video data. We also deploy a temporal attention mechanism to boost the performance of our model. Further, motion boundaries are explored as an extra input modality to assist in obfuscating the effects of camera motion for better movement assessment. We ablate the effects of different data modalities on the accuracy of the proposed network and compare with other popular architectures. We evaluate our proposed method on a dataset of 25 PD patients, obtaining 72.3% and 77.1% top-1 accuracy on hand movement and gait tasks respectively.
Reduced Order Modeling using Shallow ReLU Networks with Grassmann Layers
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents a nonlinear model reduction method for systems of equations using a structured neural network. The neural network takes the form of a "three-layer" network with the first layer constrained to lie on the Grassmann manifold and the first activation function set to identity, while the remaining network is a standard two-layer ReLU neural network. The Grassmann layer determines the reduced basis for the input space, while the remaining layers approximate the nonlinear input-output system. The training alternates between learning the reduced basis and the nonlinear approximation, and is shown to be more effective than fixing the reduced basis and training the network only. An additional benefit of this approach is, for data that lie on low-dimensional subspaces, that the number of parameters in the network does not need to be large. We show that our method can be applied to scientific problems in the data-scarce regime, which is typically not well-suited for neural network approximations. Examples include reduced order modeling for nonlinear dynamical systems and several aerospace engineering problems.
Treadmill Assisted Gait Spoofing (TAGS): An Emerging Threat to wearable Sensor-based Gait Authentication
Venue: arXiv cs.CR 2020
Year: 2020

In this work, we examine the impact of Treadmill Assisted Gait Spoofing (TAGS) on Wearable Sensor-based Gait Authentication (WSGait). We consider more realistic implementation and deployment scenarios than the previous study, which focused only on the accelerometer sensor and a fixed set of features. Specifically, we consider the situations in which the implementation of WSGait could be using one or more sensors embedded into modern smartphones. Besides, it could be using different sets of features or different classification algorithms, or both. Despite the use of a variety of sensors, feature sets (ranked by mutual information), and six different classification algorithms, TAGS was able to increase the average False Accept Rate (FAR) from 4% to 26%. Such a considerable increase in the average FAR, especially under the stringent implementation and deployment scenarios considered in this study, calls for a further investigation into the design of evaluations of WSGait before its deployment for public use.
Secrecy of Multi-Antenna Transmission with Full-Duplex User in the Presence of Randomly Located Eavesdroppers
Venue: arXiv cs.IT 2020
Year: 2020

This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user's choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.
DistB-SDoIndustry: Enhancing Security in Industry 4.0 Services based on Distributed Blockchain through Software Defined Networking-IoT Enabled Architecture
Venue: arXiv cs.CR 2020
Year: 2020

The concept of Industry 4.0 is a newly emerging focus of research throughout the world. However, it has lots of challenges to control data, and it can be addressed with various technologies like Internet of Things (IoT), Big Data, Artificial Intelligence (AI), Software Defined Networking (SDN), and Blockchain (BC) for managing data securely. Further, the complexity of sensors, appliances, sensor networks connecting to the internet and the model of Industry 4.0 has created the challenge of designing systems, infrastructure and smart applications capable of continuously analyzing the data produced. Regarding these, the authors present a distributed Blockchain-based security to industry 4.0 applications with SDN-IoT enabled environment. Where the Blockchain can be capable of leading the robust, privacy and confidentiality to our desired system. In addition, the SDN-IoT incorporates the different services of industry 4.0 with more security as well as flexibility. Furthermore, the authors offer an excellent combination among the technologies like IoT, SDN and Blockchain to improve the security and privacy of Industry 4.0 services properly. Finally , the authors evaluate performance and security in a variety of ways in the presented architecture.
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Venue: arXiv cs.LG 2020
Year: 2020

Researchers require timely access to real-world longitudinal electronic health records (EHR) to develop, test, validate, and implement machine learning solutions that improve the quality and efficiency of healthcare. In contrast, health systems value deeply patient privacy and data security. De-identified EHRs do not adequately address the needs of health systems, as de-identified data are susceptible to re-identification and its volume is also limited. Synthetic EHRs offer a potential solution. In this paper, we propose EHR Variational Autoencoder (EVA) for synthesizing sequences of discrete EHR encounters (e.g., clinical visits) and encounter features (e.g., diagnoses, medications, procedures). We illustrate that EVA can produce realistic EHR sequences, account for individual differences among patients, and can be conditioned on specific disease conditions, thus enabling disease-specific studies. We design efficient, accurate inference algorithms by combining stochastic gradient Markov Chain Monte Carlo with amortized variational inference. We assess the utility of the methods on large real-world EHR repositories containing over 250, 000 patients. Our experiments, which include user studies with knowledgeable clinicians, indicate the generated EHR sequences are realistic. We confirmed the performance of predictive models trained on the synthetic data are similar with those trained on real EHRs. Additionally, our findings indicate that augmenting real data with synthetic EHRs results in the best predictive performance - improving the best baseline by as much as 8% in top-20 recall.
PointINet: Point Cloud Frame Interpolation Network
Venue: arXiv cs.CV 2020
Year: 2020

LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors, a novel task named Point Cloud Frame Interpolation is studied in this paper. Given two consecutive point cloud frames, Point Cloud Frame Interpolation aims to generate intermediate frame(s) between them. To achieve that, we propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). Based on the proposed method, the low frame rate point cloud streams can be upsampled to higher frame rates. We start by estimating bi-directional 3D scene flow between the two point clouds and then warp them to the given time step based on the 3D scene flow. To fuse the two warped frames and generate intermediate point cloud(s), we propose a novel learning-based points fusion module, which simultaneously takes two warped point clouds into consideration. We design both quantitative and qualitative experiments to evaluate the performance of the point cloud frame interpolation method and extensive experiments on two large scale outdoor LiDAR datasets demonstrate the effectiveness of the proposed PointINet. Our code is available at https://github.com/ispc-lab/PointINet.git.
Neural Network Embeddings for Test Case Prioritization
Venue: arXiv cs.SE 2020
Year: 2020

In modern software engineering, Continuous Integration (CI) has become an indispensable step towards systematically managing the life cycles of software development. Large companies struggle with keeping the pipeline updated and operational, in useful time, due to the large amount of changes and addition of features, that build on top of each other and have several developers, working on different platforms. Associated with such software changes, there is always a strong component of Testing. As teams and projects grow, exhaustive testing quickly becomes inhibitive, becoming adamant to select the most relevant test cases earlier, without compromising software quality. We have developed a new tool called Neural Network Embeeding for Test Case Prioritization (NNE-TCP) is a novel Machine-Learning (ML) framework that analyses which files were modified when there was a test status transition and learns relationships between these files and tests by mapping them into multidimensional vectors and grouping them by similarity. When new changes are made, tests that are more likely to be linked to the files modified are prioritized, reducing the resources needed to find newly introduced faults. Furthermore, NNE-TCP enables entity visualization in low-dimensional space, allowing for other manners of grouping files and tests by similarity and to reduce redundancies. By applying NNE-TCP, we show for the first time that the connection between modified files and tests is relevant and competitive relative to other traditional methods.
On Finality in Blockchains
Venue: arXiv cs.DC 2020
Year: 2020

There exist many forms of Blockchain finality conditions, from deterministic to probabilistic terminations. To favor availability against consistency in the face of partitions, most blockchains only offer probabilistic eventual finality: blocks may be revoked after being appended to the blockchain, yet with decreasing probability as they sink deeper into the chain. Other blockchains favor consistency by leveraging the immediate finality of Consensus-a block appended is never revoked-at the cost of additional synchronization. In this paper, we focus on necessary and sufficient conditions to implement a blockchain with deterministic eventual finality, which ensures that selected main chains at different processes share a common increasing prefix. This is a much weaker form of finality that allows us to provide a solution in an asynchronous system subject to unlimited number of byzantine failures. We study stronger forms of eventual finality as well and show that it is unfortunately impossible to provide a bounded displacement. By bounded displacement we mean that the (unknown) number of blocks that can be revoked from the current blockchain is bounded. This problem reduces to consensus or eventual consensus depending on whether the bound is known or not. We also show that the classical selection mechanism, such as in Bitcoin, that appends blocks at the longest chain is not compliant with a solution to eventual finality.
LGENet: Local and Global Encoder Network for Semantic Segmentation of Airborne Laser Scanning Point Clouds
Venue: arXiv cs.CV 2020
Year: 2020

Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geo-information products like 3D city models, digital terrain models and land use maps. In this paper, we present a local and global encoder network (LGENet) for semantic segmentation of ALS point clouds. Adapting the KPConv network, we first extract features by both 2D and 3D point convolutions to allow the network to learn more representative local geometry. Then global encoders are used in the network to exploit contextual information at the object and point level. We design a segment-based Edge Conditioned Convolution to encode the global context between segments. We apply a spatial-channel attention module at the end of the network, which not only captures the global interdependencies between points but also models interactions between channels. We evaluate our method on two ALS datasets namely, the ISPRS benchmark dataset and DCF2019 dataset. For the ISPRS benchmark dataset, our model achieves state-of-the-art results with an overall accuracy of 0.845 and an average F1 score of 0.737. With regards to the DFC2019 dataset, our proposed network achieves an overall accuracy of 0.984 and an average F1 score of 0.834.
Should I visit this place? Inclusion and Exclusion Phrase Mining from Reviews
Venue: arXiv cs.IR 2020
Year: 2020

Although several automatic itinerary generation services have made travel planning easy, often times travellers find themselves in unique situations where they cannot make the best out of their trip. Visitors differ in terms of many factors such as suffering from a disability, being of a particular dietary preference, travelling with a toddler, etc. While most tourist spots are universal, others may not be inclusive for all. In this paper, we focus on the problem of mining inclusion and exclusion phrases associated with 11 such factors, from reviews related to a tourist spot. While existing work on tourism data mining mainly focuses on structured extraction of trip related information, personalized sentiment analysis, and automatic itinerary generation, to the best of our knowledge this is the first work on inclusion/exclusion phrase mining from tourism reviews. Using a dataset of 2000 reviews related to 1000 tourist spots, our broad level classifier provides a binary overlap F1 of $\sim$80 and $\sim$82 to classify a phrase as inclusion or exclusion respectively. Further, our inclusion/exclusion classifier provides an F1 of $\sim$98 and $\sim$97 for 11-class inclusion and exclusion classification respectively. We believe that our work can significantly improve the quality of an automatic itinerary generation service.
Universal Approximation in Dropout Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

We prove two universal approximation theorems for a range of dropout neural networks. These are feed-forward neural networks in which each edge is given a random $\{0,1\}$-valued filter, that have two modes of operation: in the first each edge output is multiplied by its random filter, resulting in a random output, while in the second each edge output is multiplied by the expectation of its filter, leading to a deterministic output. It is common to use the random mode during training and the deterministic mode during testing and prediction. Both theorems are of the following form: Given a function to approximate and a threshold $\varepsilon>0$, there exists a dropout network that is $\varepsilon$-close in probability and in $L^q$. The first theorem applies to dropout networks in the random mode. It assumes little on the activation function, applies to a wide class of networks, and can even be applied to approximation schemes other than neural networks. The core is an algebraic property that shows that deterministic networks can be exactly matched in expectation by random networks. The second theorem makes stronger assumptions and gives a stronger result. Given a function to approximate, it provides existence of a network that approximates in both modes simultaneously. Proof components are a recursive replacement of edges by independent copies, and a special first-layer replacement that couples the resulting larger network to the input. The functions to be approximated are assumed to be elements of general normed spaces, and the approximations are measured in the corresponding norms. The networks are constructed explicitly. Because of the different methods of proof, the two results give independent insight into the approximation properties of random dropout networks. With this, we establish that dropout neural networks broadly satisfy a universal-approximation property.
Towards Robust Explanations for Deep Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible to manipulations. With this work, we aim to enhance the resilience of explanations. We develop a unified theoretical framework for deriving bounds on the maximal manipulability of a model. Based on these theoretical insights, we present three different techniques to boost robustness against manipulation: training with weight decay, smoothing activation functions, and minimizing the Hessian of the network. Our experimental results confirm the effectiveness of these approaches.