Reviewerly/exHarmony huggingface dataset:
Deep neural networks for human pose estimation from a very low resolution depth image

The work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision. The regression network was expected to reason about relations of body parts based on depth image, and to extract locations of joints, and provide coordinates defining the body pose. The method involved creation of a dataset with 200,000 realistic depth images of a 3D body model, then training and testing numerous architectures including feedforward multilayer perceptron network and deep convolutional neural networks. The results of training and evaluation are included and discussed. The most accurate DNN network was further trained and evaluated on an augmented depth images dataset. The achieved accuracy was similar to a reference Kinect algorithm results, with a great benefit of fast processing speed and significantly lower requirements on sensor resolution, as it used 100 times less pixels than Kinect depth sensor. The method was robust against sensor noise, allowing imprecision of depth measurements. Finally, our results were compared with VGG, MobileNet, and ResNet architectures.
An Efficient Solution for Semantic Segmentation: ShuffleNet V2 with Atrous Separable Convolutions

Assigning a label to each pixel in an image, namely semantic segmentation, has been an important task in computer vision, and has applications in autonomous driving, robotic navigation, localization, and scene understanding. Fully convolutional neural networks have proved to be a successful solution for the task over the years but most of the work being done focuses primarily on accuracy. In this paper, we present a computationally efficient approach to semantic segmentation, while achieving a high mean intersection over union (mIOU), 70.33% on Cityscapes challenge. The network proposed is capable of running real-time on mobile devices. In addition, we make our code and model weights publicly available.
Wearable Embedded Intelligence for Detection of Falls Independently of on-Body Location

Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and pocket, without a calibration step being required. This algorithm is able to be fully executed on a wearable device and no external devices are necessary for data processing. Additionally, a study of the accelerometer sampling rate, that allows the algorithm to achieve a better performance, was performed. The algorithm was validated with a continuous dataset with daily living activities and 272 simulated falls. Considering the trade-off between sensitivity and the number of false alarms the most suitable sampling rate found was 50 Hz. The proposed algorithm was able to achieve a trade-off of no false alarms and 89.5% of fall detection rate when wearing the sensor on the user's waist with a medium sensitivity level of the algorithm. In conclusion, this paper presents a reliable solution for automatic fall detection that can be adapted to different usages and conditions, since it can be used in different body locations and its sensitivity can be adapted to different subjects according to their physical activity level.
Analyzing the Traits and Anomalies of Political Discussions on Reddit

Online communities like Reddit feature intensive discussions on political topics, engaging many users who give and receive votes for their posts and comments. Prior work has focused on detecting controversial discussions and analyzing the role of troll-like users. This paper provides a systematic analysis of the characteristics of user actions, like replies and votes, in discussion threads, identifying different patterns that refine the notion of controversies into disputes, disruptions and discrepancies. Most importantly, our study connects the dimension of user actions with a rich feature space comprising also the sentiments expressed in post contents and the topical variation across the posts and replies. We use this framework to gain insight on the traits of different archetypes of discussions, and to statistically test a variety of hypotheses on expected and abnormal behavior.
Developing an Augmented Reality Based Training Demonstrator for Manufacturing Cherry Pickers

This paper presents an Augmented Reality (AR) demonstrator to test its feasibility with enhancing the training process, improving learning time and error rate. The application environment was a manufacturer of cherry pickers. The demonstrator focused on covering the assembly of hydraulic hoses to the relative valve; the choice was driven by Company needs. Requirements led to the choice of Microsoft HoloLens as hardware, while Unity and Vuforia were used as software. The demonstrator provides sequential instructions through texts, images and animations. Results showed improvements when introducing AR for error rates and for the average assembly times.
A Hierarchical Blockchain Aided Proactive Caching Scheme for Internet of Vehicles

The emerging blockchain technology provides a new paradigm for maintaining data integrity and unforgeability in a distributed manner. However conventional public blockchain systems suffer large consensus latency thus cannot be well applied to Internet of Vehicles (IoV) with the high mobility of vehicles and low latency requirement. In addition, not all messages in IoV should be stored in a global ledger. In this paper, we propose a novel Hierarchical Blockchain (HB) which divides the system into two layers and each layer maintains an exclusive ledger. Sensing information of vehicles are recorded in differnet layer according to its influence scope. Furthermore, based on the transactions recorded in the hierarchical blockchain, we design the proactive file duplicate caching scheme considering not only popularities but influence scopes for the enhancement of overall system performance. Simulation results shows the superiority of the proposed architecture compared with conventional vehicular systems, in terms of failure rate, latency and system utility.
Solar Image Restoration with the CycleGAN Based on Multi-fractal Properties of Texture Features

Abstract Texture is one of the most obvious characteristics in solar images and it is normally described by texture features. Because textures from solar images of the same wavelength are similar, we assume that texture features of solar images are multi-fractals. Based on this assumption, we propose a pure data-based image restoration method: with several high-resolution solar images as references, we use the Cycle-Consistent Adversarial Network to restore blurred images of the same steady physical process, in the same wavelength obtained by the same telescope. We test our method with simulated and real observation data and find that our method can improve the spatial resolution of solar images, without loss of any frames. Because our method does not need a paired training set or additional instruments, it can be used as a post-processing method for solar images obtained by either seeing-limited telescopes or telescopes with ground-layer adaptive optic systems.
Trajectory Generation of a Two-Wheeled Mobile Robot in an Uncertain Environment

This article presents a novel method to generate a trajectory for a two-wheeled mobile robot moving in an uncertain environment where only a few way-points are available to reach a nearby target state. The proposed method interpolates way-points by the combination of a straight line and a fifth-order Bézier curve along which the curvature varies continuously. In addition, a method to generate the associated timing law is proposed such that the robot travels along the curve in near minimum time under the maximum velocity and torque constraints. Since the method aims for an online application, heuristic techniques are applied to minimize the computational cost for finding the optimal solution. The time for computing a complete time-optimal trajectory for a moving robot to transit to a next way-point is only several milliseconds when tested in a personal computer. This result implies that a highly efficient motion control system for a two-wheeled mobile robot can be implemented with a low-cost hardware available today.
Exploring repost features of police-generated microblogs through topic and sentiment analysis

Purpose This study aims to explore the repost features of microblogs acting to promote the information diffusion of government-generated content on social media. Design/methodology/approach This study proposes a topic−sentiment analysis using a mixed social media analytics framework to analyse the microblogs collected from the Sina Weibo accounts of 30 Chinese provincial police departments. On the basis of this analysis, this study presents the distribution of reposted microblogs and reveals the reposting characteristics of police-generated microblogs (PGMs). Findings The experimental results indicate that children’s safety and crime-related PGMs with a positive sentiment can achieve a high level of online information diffusion. Originality/value This study is novel, as it reveals the reposting features of PGMs from both a topic and sentiment perspectives, and provides new findings that can inspire users’ reposting behaviour.
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition

Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Character-Level Convolutional Neural Network for Predicting Severity of Software Vulnerability from Vulnerability Description

System administrators and security officials of an organization need to deal with vulnerable IT assets, especially those with severe vulnerabilities, to minimize the risk of these vulnerabilities being exploited. The Common Vulnerability Scoring System (CVSS) can be used as a means to calculate the severity score of vulnerabilities, but it currently requires human operators to choose input values. A word-level Convolutional Neural Network (CNN) has been proposed to estimate the input parameters of CVSS and derive the severity score of vulnerability notes, but its accuracy needs to be improved further. In this paper, we propose a character-level CNN for estimating the severity scores. Experiments show that the proposed scheme outperforms conventional one in terms of accuracy and how errors occur.
Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks

To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features that can be leveraged for various NLP tasks. In this article, we study some of these discourse features in multimedia text and what communicative function they fulfill in the context. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We conclude that the discourse and text layout features provide information that is complementary to lexical semantic information. Finally, we show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable.
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks

To solve the shared tasks of COIN: COmmonsense INference in Natural Language Processing) Workshop in EMNLP-IJCNLP 2019, we need explore the impact of knowledge representation in modeling commonsense knowledge to boost performance of machine reading comprehension beyond simple text matching.There are two approaches to represent knowledge in the low-dimensional space.The first is to leverage large-scale unsupervised text corpus to train fixed or contextual language representations.The second approach is to explicitly express knowledge into a knowledge graph (KG), and then fit a model to represent the facts in the KG.We have experimented both (a) improving the fine-tuning of pre-trained language models on a task with a small dataset size, by leveraging datasets of similar tasks; and (b) incorporating the distributional representations of a KG onto the representations of pre-trained language models, via simply concatenation or multi-head attention.We find out that: (a) for task 1, first fine-tuning on larger datasets like RACE (Lai et al., 2017) and SWAG (Zellers et al., 2018), and then finetuning on the target task improve the performance significantly; (b) for task 2, we find out the incorporating a KG of commonsense knowledge, WordNet (Miller, 1995) into the Bert model (Devlin et al., 2018) is helpful, however, it will hurts the performace of XL-NET (Yang et al., 2019), a more powerful pretrained model.Our approaches achieve the state-of-the-art results on both shared task's official test data, outperforming all the other submissions.
CPA-to-CCA Transformation for KDM Security

We show that chosen plaintext attacks (CPA) security is equivalent to chosen ciphertext attacks (CCA) security for key-dependent message (KDM) security. Concretely, we show how to construct a public-key encryption (PKE) scheme that is KDM-CCA secure with respect to all functions computable by circuits of a-priori bounded size, based only on a PKE scheme that is KDM-CPA secure with respect to projection functions. Our construction works for KDM security in the single user setting. Our main result is achieved by combining the following two steps. First, we observe that by combining the results and techniques from the recent works by Lombardi et al. (CRYPTO 2019), and by Kitagawa et al. (CRYPTO 2019), we can construct a reusable designated-verifier non-interactive zero-knowledge (DV-NIZK) argument system based on an IND-CPA secure PKE scheme and a secret-key encryption (SKE) scheme satisfying one-time KDM security with respect to projection functions. This observation leads to the first reusable DV-NIZK argument system under the learning-parity-with-noise (LPN) assumption. Then, as the second and main technical step, we show a generic construction of a KDM-CCA secure PKE scheme using an IND-CPA secure PKE scheme, a reusable DV-NIZK argument system, and an SKE scheme satisfying one-time KDM security with respect to projection functions. Since the classical Naor-Yung paradigm (STOC 1990) with a DV-NIZK argument system does not work for proving KDM security, we propose a new construction methodology to achieve this generic construction. Moreover, we show how to extend our generic construction and achieve KDM-CCA security in the multi-user setting, by additionally requiring the underlying SKE scheme in our generic construction to satisfy a weak form of KDM security against related-key attacks (RKA-KDM security) instead of one-time KDM security. From this extension, we obtain the first KDM-CCA secure PKE schemes in the multi-user setting under the CDH or LPN assumption.
Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection

In this work we present a clustering technique called \textit{multi-level conformal clustering (MLCC)}. The technique is hierarchical in nature because it can be performed at multiple significance levels which yields greater insight into the data than performing it at just one level. We describe the theoretical underpinnings of MLCC, compare and contrast it with the hierarchical clustering algorithm, and then apply it to real world datasets to assess its performance. There are several advantages to using MLCC over more classical clustering techniques: Once a significance level has been set, MLCC is able to automatically select the number of clusters. Furthermore, thanks to the conformal prediction framework the resulting clustering model has a clear statistical meaning without any assumptions about the distribution of the data. This statistical robustness also allows us to perform clustering and anomaly detection simultaneously. Moreover, due to the flexibility of the conformal prediction framework, our algorithm can be used on top of many other machine learning algorithms.
A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.
RGB-D Image Processing Algorithm for Target Recognition and Pose Estimation of Visual Servo System

This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen the performance indicators of visual servoing system such as real time and accuracy, etc. Firstly, color images acquired by the RGB-D camera are segmented based on optimized normalized cuts. Next, the gray scale is restored according to the histogram feature of the target image. Then, the obtained 2D graphics depth information and the enhanced gray image information are distort merged to complete the target pose estimation based on the Hausdorff distance, and the current image pose is matched with the target image pose. The end angle and the speed of the robot are calculated to complete a control cycle and the process is iterated until the servo task is completed. Finally, the performance index of this control system based on proposed algorithm is tested about accuracy, real-time under position-based visual servoing system. The results demonstrate and validate that the RGB-D image processing algorithm proposed in this paper has the performance in the above aspects of the visual servoing system.
Accelerating B-spline interpolation on GPUs: Application to medical image registration

Background and Objective. B-spline interpolation (BSI) is a popular technique in the context of medical imaging due to its adaptability and robustness in 3D object modeling. A field that utilizes BSI is Image Guided Surgery (IGS). IGS provides navigation using medical images, which can be segmented and reconstructed into 3D models, often through BSI. Image registration tasks also use BSI to align pre-operative data to intra-operative data. However, such IGS tasks are computationally demanding, especially when applied to 3D medical images, due to the complexity and amount of data involved. Therefore, optimization of IGS algorithms is greatly desirable, for example, to perform image registration tasks intra-operatively and to enable real-time applications. A traditional CPU does not have sufficient computing power to achieve these goals. In this paper, we introduce a novel GPU implementation of BSI to accelerate the calculation of the deformation field in non-rigid image registration algorithms. Methods. Our BSI implementation on GPUs minimizes the data that needs to be moved between memory and processing cores during loading of the input grid, and leverages the large on-chip GPU register file for reuse of input values. Moreover, we re-formulate our method as trilinear interpolations to reduce computational complexity and increase accuracy. To provide pre-clinical validation of our method and demonstrate its benefits in medical applications, we integrate our improved BSI into a registration workflow for compensation of liver deformation (caused by pneumoperitoneum, i.e., inflation of the abdomen) and evaluate its performance. Results. Our approach improves the performance of BSI by an average of 6.5x and interpolation accuracy by 2x compared to three state-of-the-art GPU implementations. We observe up to 34% acceleration of non-rigid image registration.
Managing Security Outsourcing in the Presence of Strategic Hackers

Nowadays, firms tend to outsource security operations to professional managed security service providers (MSSPs) as a result of the sophistication of strategic hackers. Thus, how an MSSP makes security decisions according to a strategic hacker’s action is worth researching. Constructing a contract theory model, this paper examines the interaction between an MSSP and a strategic hacker based on both parties’ characteristics. We find that the hacker will give up less valuable information assets, and thus not all information assets are worth protecting for the MSSP. For both parties, their optimal efforts do not necessarily increase with their respective efficiency, and the firm’s reputation loss has an opposite effect on its respective efforts. Moreover, we distinguish two types of security externalities including MSSP-side externality and hacker-side externality, and we find that the two types of security externalities have different effects on both parties’ optimal efforts and expected payoffs. We also find that as a result of the trade-off between the integration effect of the MSSP and the effect of MSSP-side externality, firms are still willing to outsource their security operations to the MSSP even when an MSSP devotes fewer security efforts than those of firms that manage security in-house. Last, we extend our base model from two aspects to generalize the main results.