Reviewerly/exHarmony huggingface dataset:
Handling effective communication to support awareness in rescue operations

Abstract Efficient rescue operations require a high level of situation awareness amongst decision‐makers and first responders for the purpose of achieving operations successfully and reducing losses. Moreover, a common operational picture between involved actors is required in order to support decision‐making. Therefore, different organisations and agencies have to collaborate, cooperate, and coordinate their actions with each other. Hence, effective interactions and communications between participants are vital to fulfil these essential needs. However, emergency actors still lack backing to exchange information effectively and ensure a common operational picture in order to reach shared situational awareness. For this reason, we aim to develop and implement Rescue MODES, a communication system oriented to support situation awareness amongst French emergency actors in rescue operations. In this paper, we examine and analyse actors’ activities and interactions, so that the system will be based on the real needs of actors. We start by studying and modelling the communications, interactions, and information flow. This modelling is based on an application ontology. Then, we define requirements for good communication in these operations and present some existing systems in France and how each system responds to these requirements.
Non-Negative Transfer Learning With Consistent Inter-Domain Distribution

In this letter, we propose a novel transfer learning approach, which simultaneously exploits the intra-domain differentiation and inter-domain correlation to comprehensively solve the drawbacks many existing transfer learning methods suffer from, i.e., they either are unable to handle the negative samples or have strict assumptions on the distribution. Specifically, the sample selection strategy is introduced to handle negative samples by using the local geometry structure and the label information of source samples. Furthermore, the pseudo target label is imposed to slack the assumption on the inter-domain distribution for considering the inter-domain correlation. Then, an efficient alternating iterative algorithm is proposed to solve the formulated optimization problem with multiple constraints. The extensive experiments conducted on eleven real-world datasets show the superiority of our method over state-of-the-art approaches, i.e., our method achieves 11.23% improvement on the MNIST dataset.
Research on robot arm control based on Unity3D machine learning

Abstract Based on the Unity3D engine, the article uses deep reinforcement learning strategies to train the robotic arm through the reward function, and realizes machine learning and intelligent control of the robotic arm. After training and learning, the robotic arm can quickly and accurately find movement point in the environment and has high environmental adaptability. The application of powerful deep reinforcement learning strategies and virtual reality technology in engineering technology teaching has improved the teaching effect and teaching efficiency of mechanical structure cognition, curriculum design and other teaching links.
A New Family of Quantum Synchronizable Codes

Quantum synchronizable codes (QSCs) are special quantum error-correcting codes that correct the effects of both quantum noise on qubits and misalignment in block synchronization. In this letter, we construct a family of QSCs from cyclic codes. The best attainable misalignment tolerance can also be achieved under certain constraints. And we give two classes of specific constructions of QSCs, which achieve the best attainable misalignment tolerance. Moreover, the second class of QSCs possess good error-correcting capability towards bit errors and phase errors.
Gleason Grading of Histology Prostate Images Through Semantic Segmentation via Residual U-Net

Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems allow to delineate and classify the cancerous patterns in the tissue via computer-vision algorithms in order to support the physicians' task. The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue according to the full Gleason system. This model outperforms other well-known architectures, and reaches a pixel-level Cohen's quadratic Kappa of 0.52, at the level of previous image-level works in the literature, but providing also a detailed localisation of the patterns.
LSTM-Based Viewpoint Prediction for Multi-Quality Tiled Video Coding in Virtual Reality Streaming

Virtual reality (VR) streaming is impaired by the large amount of data required to deliver 360-degree video resulting in low-quality end user experience when network bandwidth is limited, or latency is high. To address these challenges, proposed in this paper is a novel method for viewpoint prediction for long-term horizons in VR streaming. This method uses a long short-term memory (LSTM) encoder-decoder network to carry out a sequence-to-sequence prediction. To enhance the results obtained by this network, experiments are performed using viewpoint information from users on low-latency networks. By applying an effective tile-based quality assignment after viewpoint prediction, a 61% average bandwidth reduction, with respect to the transmission of the whole ERP frame, is achieved along with a high-quality viewport rendered to the end user.
Factors affecting Cryptocurrency Prices: Evidence from Ethereum

Cryptocurrency market has a potential growth since Bitcoin emerged as one of the commodity investment in Indonesia. Besides Bitcoin, there is another altcoin which rapidly developing and dominating the cryptocurrency market, Ethereum. Therefore, this study aims to analyze the factors that affect Ethereum prices through macroeconomic aspects, such as EUR/USD exchange rate and the price of gold, as well as Bitcoin and other altcoins prices in the cryptocurrency market. The time series data consists of weekly data of all variables will be utilized during the 2016-2018 period. This study will conduct an empirical analysis by using the Autoregressive Distributed Lag (ARDL) test model. The result found that only in the short term, EUR/USD affects the Ethereum prices, while the price of gold do not show any effect on Ethereum prices. Moreover, Bitcoin and 2 alt-coins (Litecoin, and Monero) affect significantly the Ethereum prices. Nevertheless, Ripple and Stellar do not show significant effect on Ethereum prices.
Randomness quantification for quantum random number generation based on detection of amplified spontaneous emission noise

Abstract The amplified spontaneous emission (ASE) noise has been extensively studied and employed to build quantum random number generators (QRNGs). While the previous relative works mainly focus on the realization and verification of the QRNG system, the comprehensive physical model and randomness quantification for the general detection of the ASE noise are still incomplete, which is essential for the quantitative security analysis. In this paper, a systematical physical model for the detection and acquisition of the ASE noise with added electronic noise is developed and verified, based on which the numerical simulations are performed under various setups and the simulation results all significantly fit well with the corresponding experimental data. Then, a randomness quantification method and the corresponding experimentally verifiable approach are proposed and validated, which quantifies the randomness purely resulted from the quantum process and improves the security analysis for the QRNG based on the detection of the ASE noise. The physical model and the randomness quantification method proposed in this paper are of significant feasibility and applicable for the QRNG system with randomness originating from the detection of the photon number with arbitrary distributions.
Agile Legged-Wheeled Reconfigurable Navigation Planner Applied on the CENTAURO Robot

Hybrid legged-wheeled robots such as the CEN-TAURO, are capable of varying their footprint polygon to carry out various agile motions. This property can be advantageous for wheeled-only planning in cluttered spaces, which is our focus. In this paper, we present an improved algorithm that builds upon our previously introduced preliminary footprint varying A* planner, which was based on the rectangular symmetry of the foot support polygon. In particular, we introduce a Theta* based planner with trapezium-like search, which aims to further reduce the limitations imposed upon the wheeled-only navigation of the CENTAURO robot by the low-dimensional search space, maintaining the real-time computational efficiency. The method is tested on the simulated and real full-size CENTAURO robot in cluttered environments.
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations

Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user. In this work, we present a convolutional neural network for estimating pixelwise object placement probabilities for a set of spatial relations from a single input image. During training, our network receives the learning signal by classifying hallucinated high-level scene representations as an auxiliary task. Unlike previous approaches, our method does not require ground truth data for the pixelwise relational probabilities or 3D models of the objects, which significantly expands the applicability in practical applications. Our results obtained using real-world data and human-robot experiments demonstrate the effectiveness of our method in reasoning about the best way to place objects to reproduce a spatial relation. Videos of our experiments can be found at https://youtu.be/zaZkHTWFMKM.
Strategies for Speeding Up Manipulator Path Planning to Find High Quality Paths in Cluttered Environments

Abstract In many manufacturing applications, robotic manipulators need to operate in cluttered environments. Quickly finding high-quality paths is very important in applications that require high part fix and frequent setup changes. This paper presents a point-to-point path planning framework for manipulators operating in cluttered environments. It uses a bi-directional tree-search to find path and facilitates finding a balance between path quality and planning time. The framework dynamically switches between search strategies based on the search progress to produce high-quality paths quickly. This paper three main contributions. First, we extend a previously developed sampling-based modular tree-search. Specifically, we present a strategy that can sample effectively in challenging regions of the search-space by using local approximations of the configuration space. Second, we add new strategies and scheduling logic that decreases the failure rate as well as the planning time compared to the prior work. We also present an inter-tree connection strategy that adapts to collision information gathered over time. We introduce a scheduling rule that regulates the exploitation of focusing hints derived from the workspace obstacles. Third, we present theoretical reasoning behind strategy switching and how it can help decrease planning times and increase path quality. Together, these new features the reduce average failure rate by a factor of 4 and improve the average planning time by 22% over the previous work.
Drone Image Stitching Using Local Mesh-Based Bundle Adjustment and Shape-Preserving Transform

This article proposes a strategy for drone image stitching using local mesh-based bundle adjustment and shape-preserving transform, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using a simple homography cannot handle the situation that the input drone images have parallax effect, and the image mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following steps. Starting from initial feature sets obtained by off-the-shelf feature extraction methods, we incorporate the parallax errors into an energy minimum framework and construct a robust alignment energy. This energy can be minimized efficiently based on local bundle adjustment and robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> principle, which could eliminate parallax effects and achieve accurate alignment. Then the seamless panoramic image is obtained by warping the target image and the source images onto the mesh plane directly. An image patch can be transformed by projective transformation (e.g., homography), which provides good alignment but may cause distortions. Consequently, combined with mesh-based shape-preserving transform, our proposed strategy can improve the naturalness of the results flexibly. Experiments show that our stitching strategy can eliminate parallax effects more effectively and achieve natural-looking results compared to other state-of-the-art methods.
Semi-supervised GANs for Fraud Detection

Over the years the online gambling industry has evolved into one of the most profitable industries on the Internet. At the same time, new stringent regulations have required the online industry to become a lot more vigilant. Although standards have improved, the methods used to process finance from illicit activities also evolved and became more sophisticated. Detecting these fraudulent activities in real life with high accuracy requires a learning system to be trained with balanced data sets of fraudulent and normal transactions. However, in the real-world, the number of fraudulent cases is significantly lower than normal cases. In this paper, to deal with data imbalance, we propose a novel generative adversarial framework based on semi-supervised learning of sparse auto-encoders for the detection of fraud in online gambling. Experimental results show that the proposed framework outperforms mainstream discriminative techniques such as logistic regression, random forest and multi-layer perceptron. We validate further the approach by applying it to other domains that suffer from the problem of class imbalance obtaining promising results.
Legal Feature Enhanced Semantic Matching Network for Similar Case Matching

Similar case matching (SCM) aims to determine whether legal case documents are similar or not. In fact, SCM is an extension of the semantic text matching. Various deep learning models are proposed to solve the semantic text matching problems. However, the main difference between the case documents may be subtle, and the length of documents can be quite long. Moreover, the case documents are written in structural format and contain plenty of legal terms. To address these challenges, we propose a novel model in this paper. Accordingly, the legal feature vector is introduced into the semantic text matching model, and BERT is adopted as the encoding layer to capture long-range dependencies in the case documents. We conduct several experiments to evaluate the performance of our proposed model. The results show that our model outperforms other existing methods on the public dataset CAIL2019-SCM.
Contact-Aware Controller Design for Complementarity Systems

While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatoric structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. In this work, by exploiting the complementarity structure of contact dynamics, we propose a control framework which can close the loop on rich, tactile sensors. Critically, this framework is non-combinatoric, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on three different underactuated, multi-contact robotics problems.
Gender bias and the impact on managerial evaluation of insider security threats

With regard to computer abuse, the term "malicious insider" tends to be associated with male employees, likely because men commit more crimes relative to women. We draw on the chivalry hypothesis to inform our study and explore whether managers demonstrate gender bias in decision-making regarding insider threats posed by subordinate employees. We recruited managers as participants in our study and randomly assigned them to an "employee gender" condition, wherein half the participants read a scenario with a female offender and half the participants read a scenario with a male offender. The scenarios were identical with the exception of the gender pronouns, and participants were asked to evaluate the intent and harm posed by the employee. Our results suggest that managers’ perceptions of malicious intent are systematically different according to the gender of the subordinate employee; specifically, managers characterize security misbehavior by males as significantly more malicious relative to that by females. These findings suggest that gender biases play a role in how managers perceive employee security behavior. This study extends current research on the chivalry hypothesis by linking gendered criminal and computer stereotypes to bias in evaluating information systems security threats.
AVOT: Audio-Visual Object Tracking of Multiple Objects for Robotics

Existing state-of-the-art object tracking can run into challenges when objects collide, occlude, or come close to one another. These visually based trackers may also fail to differentiate between objects with the same appearance but different materials. Existing methods may stop tracking or incorrectly start tracking another object. These failures are uneasy for trackers to recover from since they often use results from previous frames. By using audio of the impact sounds from object collisions, rolling, etc., our audio-visual object tracking (AVOT) neural network can reduce tracking error and drift. We train AVOT end to end and use audio-visual inputs over all frames. Our audio-based technique may be used in conjunction with other neural networks to augment visually based object detection and tracking methods. We evaluate its runtime frames-per-second (FPS) performance and intersection over union (IoU) performance against OpenCV object tracking implementations and a deep learning method. Our experiments, using the synthetic Sound-20K audio-visual dataset, demonstrate that AVOT outperforms single-modality deep learning methods, when there is audio from object collisions. A proposed scheduler network to switch between AVOT and other methods based on audio onset maximizes accuracy and performance over all frames in multimodal object tracking.
A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes

Object detection and semantic segmentation are two fundamental techniques of various applications in the fields of Intelligent Vehicles (IV) and Advanced Driving Assistance System (ADAS). Early studies separately handle these two problems. In this paper, inspired by some recent works, we propose a deep neural network model for joint object detection and semantic segmentation. Given an image, an encoder-decoder convolution network extracts a set of feature maps, these feature maps are shared by the detection branch and the segmentation branch to jointly carry out the object detection and semantic segmentation. In the detection branch, we design a PriorBox initialization mechanism to propose more object candidates. In the segmentation branch, we use the multi-scale atrous convolution to explore the global and local semantic information in traffic scenes. Benefiting from the PriorBox Initialization Mechanism (PBIM) and Multi-Scale Atrous Convolution (MSAC), our model presents the competitive performance. In the experiments, we widely compare with several recently-proposed methods on the public Cityscapes dataset, achieving the highest accuracy. In addition, to verify the robustness and generalization of our model, the extension experiments are also conducted on the well-known VOC2012 dataset.
Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation

Chinese poetry is an important part of worldwide culture, and classical and modern sub-branches are quite different. The former is a unique genre and has strict constraints, while the latter is very flexible in length, optional to have rhymes, and similar to modern poetry in other languages. Thus, it requires more to control the coherence and improve the novelty. In this paper, we propose a generate-retrieve-then-refine paradigm to jointly improve the coherence and novelty. In the first stage, a draft is generated given keywords (i.e., topics) only. The second stage produces a "refining vector" from retrieval lines. At last, we take into consideration both the draft and the "refining vector" to generate a new poem. The draft provides future sentence-level information for a line to be generated. Meanwhile, the "refining vector" points out the direction of refinement based on impressive words detection mechanism which can learn good patterns from references and then create new ones via insertion operation. Experimental results on a collected large-scale modern Chinese poetry dataset show that our proposed approach can not only generate more coherent poems, but also improve the diversity and novelty.
Bi-Directional Recurrent Attentional Topic Model

In a document, the topic distribution of a sentence depends on both the topics of its neighbored sentences and its own content, and it is usually affected by the topics of the neighbored sentences with different weights. The neighbored sentences of a sentence include the preceding sentences and the subsequent sentences. Meanwhile, it is natural that a document can be treated as a sequence of sentences. Most existing works for Bayesian document modeling do not take these points into consideration. To fill this gap, we propose a bi-Directional Recurrent Attentional Topic Model (bi-RATM) for document embedding. The bi-RATM not only takes advantage of the sequential orders among sentences but also uses the attention mechanism to model the relations among successive sentences. To support to the bi-RATM, we propose a bi-Directional Recurrent Attentional Bayesian Process (bi-RABP) to handle the sequences. Based on the bi-RABP, bi-RATM fully utilizes the bi-directional sequential information of the sentences in a document. Online bi-RATM is proposed to handle large-scale corpus. Experiments on two corpora show that the proposed model outperforms state-of-the-art methods on document modeling and classification.