Reviewerly/exHarmony huggingface dataset:
POD: Practical Object Detection With Scale-Sensitive Network

Scale-sensitive object detection remains a challenging task, where most of the existing methods not learn it explicitly and not robust to scale variance. In addition, the most existing methods are less efficient during training or slow during inference, which are not friendly to real-time application. In this paper, we propose a practical object detection with scale-sensitive network.Our method first predicts a global continuous scale ,which shared by all position, for each convolution filter of each network stage. To effectively learn the scale, we average the spatial features and distill the scale from channels. For fast-deployment, we propose a scale decomposition method that transfers the robust fractional scale into combinations of fixed integral scales for each convolution filter, which exploit the dilated convolution. We demonstrate it on one-stage and two-stage algorithm under almost different configure. For practical application, training of our method is of efficiency and simplicity which gets rid of complex data sampling or optimize strategy. During testing, the proposed method requires no extra operation and is very friendly to hardware acceleration like TensorRT and TVM.On the COCO test-dev, our model could achieve a 41.5mAP on one-stage detector and 42.1 mAP on two-stage detectors based on ResNet-101, outperforming baselines by 2.4 and 2.1 respectively without extra FLOPS.
Real-time purchase behavior recognition system based on deep learning-based object detection and tracking for an unmanned product cabinet

We propose a system to recognize purchasing behavior by detecting and tracking products in real time using only camera sensors in an unmanned product cabinet. To detect and track products in real time, we focused on the simultaneous pre-processing of videos from multiple cameras for robust product detection. After synchronizing multiple videos, unnecessary frames with relatively little information are removed based on change detection. An object score is measured on a frame-by-frame basis to select the most significant frames. Next, the target products are detected and tracked in the selected frames. Finally, the purchasing behavior of the detected product is recognized based on the tracking information. These processes were used to design an end-to-end recognition framework. The contribution of this paper is significant in that by redesigning the existing deep neural networks a real-time integrated system for a practical application was successfully realized without any bottleneck from multi-camera inputs to final object recognition process. Furthermore, the proposed object detection network shows comparable performance with the state-of-the-art methods. We performed intensive experiments to evaluate pure object detection performance as well as to evaluate various purchase/return scenarios. For example, for a basic purchase/return scenario, the proposed system achieved about 92% or more accuracy, which can be the actual level of commercialization.
Verifiable hybrid secret sharing with few qubits

We consider the task of sharing a secret quantum state in a quantum network in a verifiable way. We propose a protocol that achieves this task, while reducing the number of required qubits, as compared to the existing protocols. To achieve this, we combine classical encryption of the quantum secret with an existing verifiable quantum secret sharing scheme based on Calderbank-Shor-Steane quantum error correcting codes. In this way we obtain a verifiable hybrid secret sharing scheme for sharing qubits, which combines the benefits of quantum and classical schemes. Our scheme does not reveal any information to any group of less than half of the $n$ nodes participating in the protocol. Moreover, for sharing a one-qubit state each node needs a quantum memory to store $n$ single-qubit shares, and requires a workspace of at most $3n$ qubits in total to verify the quantum secret. Importantly, in our scheme an individual share is encoded in a single qubit, as opposed to previous schemes requiring $\mathrm{\ensuremath{\Omega}}(logn)$ qubits per share. Furthermore, we define a ramp verifiable hybrid scheme. We give explicit examples of various verifiable hybrid schemes based on existing quantum error correcting codes.
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions

Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. In this paper, we first detect adversarial examples or otherwise corrupted images based on a class-conditional reconstruction of the input. To specifically attack our detection mechanism, we propose the Reconstructive Attack which seeks both to cause a misclassification and a low reconstruction error. This reconstructive attack produces undetected adversarial examples but with much smaller success rate. Among all these attacks, we find that CapsNets always perform better than convolutional networks. Then, we diagnose the adversarial examples for CapsNets and find that the success of the reconstructive attack was proportional to the visual similarity between the source and target class. Additionally, the resulting perturbations can cause the input image to appear visually more like the target class and hence become non-adversarial. These suggest that CapsNets use features that are more aligned with human perception and address the central issue raised by adversarial examples.
Syntactic dependencies correspond to word pairs with high mutual information

How is syntactic dependency structure reflected in the statistical distribution of words in corpora?Here we give empirical evidence and theoretical arguments for what we call the Head-Dependent Mutual Information (HDMI) Hypothesis: that syntactic heads and their dependents correspond to word pairs with especially high mutual information, an information-theoretic measure of strength of association.In support of this idea, we estimate mutual information between word pairs in dependencies based on an automatically-parsed corpus of 320 million tokens of English web text, finding that the mutual information between words in dependencies is robustly higher than a controlled baseline consisting of non-dependent word pairs.Next, we give a formal argument which derives the HDMI Hypothesis from a probabilistic interpretation of the postulates of dependency grammar.Our study also provides some useful empirical results about mutual information in corpora: we find that maximum-likelihood estimates of mutual information between raw wordforms are biased even at our large sample size, and we find that there is a general decay of mutual information between part-of-speech tags with distance.
Reporting cybercrime victimization: determinants, motives, and previous experiences

Purpose Cybercrime rates have increased rapidly during the last couple of decades, resulting in cybercrimes becoming common crimes. However, most victims do not report cybercrimes to the police. Therefore, this study examines reporting cybercrime victimization and provides insights into the role of the police in this process. Design/methodology/approach A sample of 595 individuals was used. All respondents were shown three vignettes about hypothetical cybercrime victimization and were asked to imagine that this situation happened to them. Four crime and reporting characteristics were manipulated across vignettes. Respondents' intentions to report to the police and to other organizations were used as the dependent variables in regression analyses. Four random factors in the vignettes (i.e. type of crime, seriousness of crime, victim–perpetrator relationship, and reporting modality), as well as several characteristics of the respondents were included in the regression models as independent variables. Findings The type of cybercrime is the most important predictor for reporting behaviors. Other determinants are: more serious offenses were more often reported and offenses are less often reported in situations where the victim personally knows the perpetrator. Furthermore, there is large discrepancy between intended and actual cybercrime reporting. These findings provide valuable insights into the factors that influence reporting behavior in the real world. Only a fifth of respondents indicated that they would not report cybercrime victimization to the police. This implies that attempts at improving reporting rates should not solely be focused on improving people's attitudes, but also on removing obstacles to turn these attitudes into actions. Originality/value In the current study, the authors contribute to the existing literature by asking a large sample from the general population in the Netherlands about both their intended reporting behavior (i.e. a vignette study) and their actual reporting behavior (i.e. self-reports) of victimization of a wide variety of different types of cybercrime. Determinants of both reporting to the police as well as to other organizations are examined. Moreover, respondents are asked about motivations behind their decision to (not) report a cybercrime to the police. Last, people were asked about their past experiences with reporting cybercrime victimization to the police.
Influence-Based Multi-Agent Exploration

Intrinsically motivated reinforcement learning aims to address the exploration challenge for sparse-reward tasks. However, the study of exploration methods in transition-dependent multi-agent settings is largely absent from the literature. We aim to take a step towards solving this problem. We present two exploration methods: exploration via information-theoretic influence (EITI) and exploration via decision-theoretic influence (EDTI), by exploiting the role of interaction in coordinated behaviors of agents. EITI uses mutual information to capture influence transition dynamics. EDTI uses a novel intrinsic reward, called Value of Interaction (VoI), to characterize and quantify the influence of one agent's behavior on expected returns of other agents. By optimizing EITI or EDTI objective as a regularizer, agents are encouraged to coordinate their exploration and learn policies to optimize team performance. We show how to optimize these regularizers so that they can be easily integrated with policy gradient reinforcement learning. The resulting update rule draws a connection between coordinated exploration and intrinsic reward distribution. Finally, we empirically demonstrate the significant strength of our method in a variety of multi-agent scenarios.
Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning

Author name ambiguity causes inadequacy and inconvenience in academic information retrieval, which raises the necessity of author name disambiguation (AND). Existing AND methods can be divided into two categories: the models focusing on content information to distinguish whether two papers are written by the same author, the models focusing on relation information to represent information as edges on the network and to quantify the similarity among papers. However, the former requires adequate labeled samples and informative negative samples, and are also ineffective in measuring the high-order connections among papers, while the latter needs complicated feature engineering or supervision to construct the network. We propose a novel generative adversarial framework to grow the two categories of models together: (i) the discriminative module distinguishes whether two papers are from the same author, and (ii) the generative module selects possibly homogeneous papers directly from the heterogeneous information network, which eliminates the complicated feature engineering. In such a way, the discriminative module guides the generative module to select homogeneous papers, and the generative module generates high-quality negative samples to train the discriminative module to make it aware of high-order connections among papers. Furthermore, a self-training strategy for the discriminative module and a random walk based generating algorithm are designed to make the training stable and efficient. Extensive experiments on two real-world AND benchmarks demonstrate that our model provides significant performance improvement over the state-of-the-art methods.
Synergetic reconstruction from 2D pose and 3D motion for wide-space multi-person video motion capture in the wild

Although many studies have investigated markerless motion capture, the technology has not been applied to real sports or concerts. In this paper, we propose a markerless motion capture method with spatiotemporal accuracy and smoothness from multiple cameras in wide-space and multi-person environments. The proposed method predicts each person's 3D pose and determines the bounding box of multi-camera images small enough. This prediction and spatiotemporal filtering based on human skeletal model enables 3D reconstruction of the person and demonstrates high-accuracy. The accurate 3D reconstruction is then used to predict the bounding box of each camera image in the next frame. This is feedback from the 3D motion to 2D pose, and provides a synergetic effect on the overall performance of video motion capture. We evaluated the proposed method using various datasets and a real sports field. The experimental results demonstrate that the mean per joint position error (MPJPE) is 31.5 mm and the percentage of correct parts (PCP) is 99.5% for five people dynamically moving while satisfying the range of motion (RoM). Video demonstration, datasets, and additional materials are posted on our project page1.
Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers' primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems.These challenges occur at three different levels of pedestrian detection, viz.video acquisition, human detection, and its tracking.The challenges in acquiring video are, viz.illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc.These results in lower recognition rate.A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter.The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed.
Semi-Autoregressive Training Improves Mask-Predict Decoding

The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach. We introduce a new training method for conditional masked language models, SMART, which mimics the semi-autoregressive behavior of mask-predict, producing training examples that contain model predictions as part of their inputs. Models trained with SMART produce higher-quality translations when using mask-predict decoding, effectively closing the remaining performance gap with fully autoregressive models.
Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Knowledge about the interior and exterior camera orientation parameters is required to establish the relationship between 2D image content and 3D object data. Camera calibration is used to determine the interior orientation parameters, which are valid as long as the camera remains stable. However, information about the temporal stability of low-cost cameras due to the physical impact of temperature changes, such as those in smartphones, is still missing. This study investigates on the one hand the influence of heat dissipating smartphone components at the geometric integrity of implemented cameras and on the other hand the impact of ambient temperature changes at the geometry of uncoupled low-cost cameras considering a Raspberry Pi camera module that is exposed to controlled thermal radiation changes. If these impacts are neglected, transferring image measurements into object space will lead to wrong measurements due to high correlations between temperature and camera’s geometric stability. Monte-Carlo simulation is used to simulate temperature-related variations of the interior orientation parameters to assess the extent of potential errors in the 3D data ranging from a few millimetres up to five centimetres on a target in X- and Y-direction. The target is positioned at a distance of 10 m to the camera and the Z-axis is aligned with camera’s depth direction.
Planning Maximum-Manipulability Cutting Paths

This paper presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point-cloud camera. We consider the multi-objective optimisation problem of finding robot trajectories which maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of \textit{rough} robotic cutting, \textit{e.g.} demolition of legacy nuclear plant, where the cut path need not be precise as long as it achieves dismantling. We show how detours in the cut path can be leveraged, to increase the manipulability of the robot at all points along the path. This helps avoid singularities, while maximising the robot's capability to make small deviations during task execution, \textit{e.g.} compliantly responding to cutting forces via impedance control. We show how a sampling-based planner can be projected onto the Riemannian manifold of a curved surface, and extended to include a term which maximises manipulability. We present the results of empirical experiments, with both simulated and real robots, which are tasked with moving over a variety of different surface shapes. Our planner enables successful task completion, while avoiding singularities and ensuring significantly greater manipulability when compared against a conventional RRT* planner.
Reducing human effort in engineering drawing validation

Oil & Gas facilities are extremely huge and have complex industrial structures that are documented using thousands of printed sheets. During the last years, it has been a tendency to migrate these paper sheets towards a digital environment, with the final end of regenerating the original computer-aided design (CAD) projects which are useful to visualise and analyse these facilities through diverse computer applications. Usually, this was done manually by re-sketching each page using CAD applications. Nevertheless, some applications have appeared which generate the CAD document automatically given the paper sheets. In this last case, the final document is always verified by an engineer due to the need of being a zero-error process. Since the need of an engineer is absolutely accepted, we present a new method to reduce the required engineer working time. This is done by highlighting the digitised components in the CAD document that the automatic method could have incorrectly identified. Thus, the engineer is required only to look at these components. The experimental section shows our method achieves a reduction of approximately 40% of the human effort keeping a zero-error process.
Sign Language Semantic Translation System using Ontology and Deep Learning

Translation and understanding sign language may be difficult for some.Therefore, this paper proposes a solution to this problem by providing an Arabic sign language translation system using ontology and deep learning techniques.That is to interpret user's signs to different meanings.This paper implemented ontology on the sign language domain to solve some sign language challenges.In this first version, simple static signs composed of Arabic alphabets and some Arabic words started to translate.Deep Convolution Neural Network (CNN) architecture was trained and tested on a pre-made Arabic sign language dataset and on a dataset collected in this paper to obtain better accuracy in recognition.Experimental results show that according to the pre-made Arabic sign language dataset the classification accuracy of the training set (80% of the dataset) was 98.06% and recognition accuracy of the testing set (20% of the dataset) was 88.87%.According to the collected dataset, the classification accuracy of the training set was 98.6% and Semantic recognition accuracy of the testing set was 94.31%.
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness

Randomized smoothing, using just a simple isotropic Gaussian distribution, has been shown to produce good robustness guarantees against $\ell_2$-norm bounded adversaries. In this work, we show that extending the smoothing technique to defend against other attack models can be challenging, especially in the high-dimensional regime. In particular, for a vast class of i.i.d.~smoothing distributions, we prove that the largest $\ell_p$-radius that can be certified decreases as $O(1/d^{\frac{1}{2} - \frac{1}{p}})$ with dimension $d$ for $p &gt; 2$. Notably, for $p \geq 2$, this dependence on $d$ is no better than that of the $\ell_p$-radius that can be certified using isotropic Gaussian smoothing, essentially putting a matching lower bound on the robustness radius. When restricted to {\it generalized} Gaussian smoothing, these two bounds can be shown to be within a constant factor of each other in an asymptotic sense, establishing that Gaussian smoothing provides the best possible results, up to a constant factor, when $p \geq 2$. We present experimental results on CIFAR to validate our theory. For other smoothing distributions, such as, a uniform distribution within an $\ell_1$ or an $\ell_\infty$-norm ball, we show upper bounds of the form $O(1 / d)$ and $O(1 / d^{1 - \frac{1}{p}})$ respectively, which have an even worse dependence on $d$.
Imaging Spatiotemporal Hong-Ou-Mandel Interference of Biphoton States of Extremely High Schmidt Number

For the first time, experiments show 2D spatial as well as temporal interference of pairs of entangled photons with enormous dimensionality, setting the stage for complex quantum information protocols that depend on space and time.