Reviewerly/exHarmony huggingface dataset:
An Anchor-free Object Detection Network for Arbitrarily-orientated Ships in Large-scale Remote Sensing Images

Reliable and efficient ship detection is still a challenging task for large-scale remote sensing images with complex background. Since ships in remote sensing images are usually densely distributed with different sizes and uncertain directions, adopting the common horizontal bounding box to detect the inclined ships with large aspect ratio will lead to inaccurate detection. In this letter, a multitask learning object detection framework called MSRDNet is proposed to detect ships with arbitrarily orientations. Convolutional features are extracted by the backbone with parallel attention residual block (PARB), which combines spatial and channel attention module. Then, a lightweight classifier is designed as a subtask to filter out a large number of background patches. Moreover, the anchor-free detector with orientation regression is designed as the other subtask for the inclined and densely distributed ships. The proposed algorithm and comparative methods are tested on two public remote sensing image data sets DOTA and HRSC2016. The experimental results demonstrate that the proposed algorithm outperforms all the compared methods. The code is available from https://github.com/Sdy344/paper_code.
Virtual Testing and Policy Deployment Framework for Autonomous Navigation of an Unmanned Ground Vehicle Using Reinforcement Learning

The use of deep reinforcement learning (DRL) as a framework for training a mobile robot to perform optimal navigation in an unfamiliar environment is a suitable choice for implementing AI with real-time robotic systems. In this study, the environment and surrounding obstacles of an Ackermann-steered UGV are reconstructed into a virtual setting for training the UGV to centrally learn the optimal route (guidance actions to be taken at any given state) towards a desired goal position using Multi-Agent Virtual Exploration in Deep Q-Learning (MVEDQL) for various model configurations. The trained model policies are to be transferred to a physical vehicle and compared based on their individual effectiveness for performing autonomous waypoint navigation. Prior to incorporating the learned model with the physical UGV for testing, this paper outlines the development of a GUI application to provide an interface for remotely deploying the vehicle and a virtual reality framework reconstruction of the training environment to assist safely testing the system using the reinforcement learning model.
Employing the Concept of Multilevel Security to Generate Access Protection Configurations for Automotive On-Board Networks

Future automotive on-board networks are expected to integrate various functions on only a few centralized processing platforms. Combined with attack surfaces that originate from the external connectivity of modern vehicles, this turns the design of secure on-board networks into a challenging endeavor. Therefore, we present a formal model to describe both confidentiality and integrity requirements of applications in such a network using security levels. The proposed model is then integrated into an existing design methodology for the automatic configuration of access protection units in MPSoCs. The resulting methodology ensures that the above-mentioned requirements are automatically enforced during runtime and is validated using a safety-critical example scenario from the automotive domain.
Expansive Voronoi Tree: A Motion Planner for Assembly Sequence Planning

One major challenge in Assembly Sequence Planning (ASP) for complex real-world CAD-scenarios is to find an appropriate disassembly path for each assembled part. Complex real-world scenes are characterized by a large installation space. There each part has many different possible disassembly paths that differ in length and clearance. However, due to tight packing in the installation space, these paths can contain narrow passages. Therefore a motion planner is needed that is able to globally search for a reasonable path and to locally overcome narrow passages. Moreover, since motion planning requests are executed in the ASP context over and over again for many parts, both for those that can be disassembled in the next step and for those that cannot be yet, the motion planner has to be reliably fast.We present a new rigid body motion planner, called Expansive Voronoi Tree (EVT), which is optimized for complex ASP scenarios. The EVT estimates a globally reasonable path using a General Voronoi Diagram of the complete scene. With a novel EST-based sampling strategy, which is the contribution of this paper, it then locally explores the environment along the estimated path. The EVT automatically adapts to different clearance situations. It passes wide environments quickly and samples densely at narrow passages.We compare our EVT to state of the art motion planners which use different sampling strategies on a real-world data set consisting of a large subset of a car. The experiments show that the EVT is reliably many times faster and delivers shorter paths.
“Can You Search for Me?”: Understanding and Improving<scp>User‐System</scp>Dialogues for Complex Search Tasks

Abstract Most voice‐based personal assistants are suitable for simple tasks which are not conversational but single‐turn question‐answering. To address this limitation, we investigate the dialogue capabilities of commercial conversational systems and compare them to the standards expected by the users. We designed a set of moderately complex search tasks and used two popular personal assistants to evaluate the user‐system interaction. A laboratory‐based user study was conducted with twenty‐five users and seventy‐five search sessions to collect user‐system conversational dialogues (for three search tasks). Next, we show that using a set of simple rules, which could be implemented in the immediate future, it is possible to improve the users' interaction experience and make the system more anthropomorphic. Using a conceptual prototype where a human (Wizard) played the role of the system (unknowing to the users), we demonstrate the efficacy of the guidelines and provide design recommendations for future conversational search systems.
"Be Gay, Do Crimes": The Co-Production and Activist Potential of Contemporary Fanzines

Online creative communities are increasingly a space for marginalized groups to build solidarity and engage in activist work, encouraging the exploration and articulation of intersectionally-marginalized identities through processes of creative production. One such context for creative production includes community-driven sites such as Archive of Our Own, which by their design are intended to leverage and effectively support voices that are marginalized on other social platforms. In this paper, we build upon work on creative production and fan communities to further describe the work of fanart and fanzine collectives. We share the results of 1) an interview study with fanzine producers and 2) a two month remote co-design study where we further explored fanzine culture and the potential of future archival support. We used a range of qualitative methods to investigate themes of activism co-production in relation to the fanzine work of these producers, seeking to identify characteristic barriers and opportunities for community support as these artists seek to promote their work, encourage the co-production of work with other artists, and navigate the constantly shifting legal landscape associated with fanart. We conclude with implications for creative community support that amplify marginalized voices and facilitate archival work at the intersection of visual art, folksonomy, and legality.
FPN-D-Based Driver Smoking Behavior Detection Method

In view of the fact that a driver's smoking behavior seriously affects the driving safety, a feature pyramid network (FPN)-based smoking behavior identification method has been studied in order to reduce the occurrence of the driver smoking. Most of the existing research has been focused on detection and recognition based on movements while smoking or smog characteristics. Therefore, the probability of misjudgment in such methods is high, thus to address this issue, the present work proposes a method based on FPN to detect the driver's smoking behavior. FPN has been combined with the dilated convolution technique in order to detect a small target object in the driver's image and recognize their smoking behavior. By using the driver behavior images collected from the vehicle platform, a simulation experiment was carried out by employing the behavior identification method proposed in this work. The experimental results show that the accuracy of the proposed method is 94.75%, the recall rate is 96%, the precision rate is 95.05% and the area under the receiver operating characteristic curve is 95.5%.
PaCon: a symbolic analysis approach for tactic-oriented clustering of programming submissions

Enrollment in programming courses increasingly surges. To maintain the quality of education in programming courses, instructors need ways to understand the performance of students and give feedback accordingly at scale. For example, it is important for instructors to identify different problem-solving ways (named as tactics in this paper) used in programming submissions. However, because there exist many abstraction levels of tactics and high implementation diversity of the same tactic, it is challenging and time-consuming for instructors to manually tackle the task of tactic identification. Toward this task, we propose PaCon, a symbolic analysis approach for clustering functionally correct programming submissions to provide a way of identifying tactics. In particular, PaCon clusters submissions according to path conditions, a semantic feature of programs. Because of the focus on program semantics, PaCon does not struggle with the issue of an excessive number of clusters caused by subtle syntactic differences between submissions. Our experimental results on real-world data sets show that PaCon can produce a reasonable number of clusters each of which effectively groups together those submissions with high syntax diversity while sharing equivalent path-condition-based semantics, providing a promising way toward identifying tactics.
Mobile 3D Printing Robot Simulation with Viscoelastic Fluids

The system design and algorithm development of mobile 3D printing robots need a realistic simulation. They require a mobile robot simulation platform to interoperate with a physics-based material simulation for handling interactions between the time-variant deformable 3D printing materials and other simulated rigid bodies in the environment, which is not available for roboticists yet. To bridge this gap and enable the real-time simulation of mobile 3D printing processes, we develop a simulation framework that includes particle-based viscoelastic fluid simulation and particle-to-mesh conversion in the widely adopted Gazebo robotics simulator, avoiding the bottlenecks of traditional additive manufacturing simulation approaches. This framework is the first of its kind that enables the simulation of robot arms or mobile manipulators together with viscoelastic fluids. The method is tested using various material properties and multiple collaborating robots to demonstrate its simulation ability for the robots to plan and control the printhead trajectories and to visually sense at the same time the printed fluid materials as a free-form mesh. The scalability as a function of available material particles in the simulation was also studied. A simulation with an average of 5 FPS was achieved on a regular desktop computer.
Deep deterministic policy gradient-based combustion optimization method for coal-fired boiler

Boiler combustion optimization technology aims to improve boiler efficiency while reducing NOx emissions. However, the combustion optimization methods commonly used lack the self-learning capability and have low utilization of historical data. In this paper, a combustion optimization method based on the deep deterministic policy gradient (DDPG) algorithm is proposed. Firstly, dynamic modeling is realized based on online least squares support vector machine and multi-kernel learning, and the optimal kernel function weight coefficient and related kernel parameters are selected by an improved particle swarm optimization algorithm. Secondly, the DDPG algorithm is introduced to optimize the control variables. The simulation results demonstrate that the multi-kernel least squares support vector machine (MKLSSVM) model has better local fitting performance and global generalization performance. The DDPG optimizer and MKLSSVM predictor can effectively improve the boiler efficiency and reduce pollutant emissions while maintaining the stable reheat steam temperature.
Autonomous Car Based on Teaching-and-Playback Control

Autonomous cars or self-controlled cars are now starting to be used in general. In a limited work area, an autonomous vehicle that operates without the use of sensors and processors with high specifications can be developed. The vehicle will advance by using the trip data that has been taught previously. One of the methods of control that makes it possible to realize autonomous vehicles with a limited work area is the teaching and playback method. In this research, a remote control car is used for hardware simulation. When the car teaching mode is run via an android based remote control, while in playback mode the data stored in memory is read line-to-line to run the car. The test results produce data on the success rate of testing straight steering 80%, 60% right-turning steering, 60% left-turning steering, 70% turning right-turning steering wheel to destination 70%, and steering turning right turning left according to route and up to 60% destination.
Word Order does not Matter for Speech Recognition

In this paper, we study training of automatic speech recognition system in a weakly supervised setting where the order of words in transcript labels of the audio training data is not known. We train a word-level acoustic model which aggregates the distribution of all output frames using LogSumExp operation and uses a cross-entropy loss to match with the ground-truth words distribution. Using the pseudo-labels generated from this model on the training set, we then train a letter-based acoustic model using Connectionist Temporal Classification loss. Our system achieves 2.3%/4.6% on test-clean/test-other subsets of LibriSpeech, which closely matches with the supervised baseline's performance.
Minimum-entanglement protocols for function estimation

We derive a family of optimal protocols, in the sense of saturating the quantum Cram\'er-Rao bound, for measuring a linear combination of $d$ field amplitudes with quantum sensor networks, a key subprotocol of general quantum sensor network applications. We demonstrate how to select different protocols from this family under various constraints. Focusing primarily on entanglement-based constraints, we prove the surprising result that highly entangled states are not necessary to achieve optimality in many cases. Specifically, we prove necessary and sufficient conditions for the existence of optimal protocols using at most $k$-partite entanglement. We prove that the protocols which satisfy these conditions use the minimum amount of entanglement possible, even when given access to arbitrary controls and ancillas. Our protocols require some amount of time-dependent control, and we show that a related class of time-independent protocols fail to achieve optimal scaling for generic functions.
Electric Vehicle Charging Robot Charging Port Identification Method Based on Multi-algorithm Fusion

Aiming at the problem of plugging and positioning identification in the process of automatic charging of electric vehicles, especially the difficult problem of low efficiency and accuracy of identification in complex operation environment, this article proposes a multi-algorithm fusion of electric vehicle charging port identification method, which can effectively obtain the characteristic information of the round hole of charging port and realize the purpose of automatic identification of charging port by robot. Firstly, an electric vehicle charging port in a complex environment is analyzed and simulated, and camera selection and calibration are explained; on this basis, algorithms based on image smoothing filtering, feature detection segmentation of ROI region, improved Canny edge detection and combined mathematical morphology are proposed to correlate the charging port image respectively, and the features of the target charging port jack are extracted. Finally, the experimental verification of the charging port identification method was conducted for different illumination intensities and different shooting distances. The experimental results show that the identification success rate is 93.3% under the weak illumination and 97.8% under the normal illumination intensity of 4000lx. This shows that the method can effectively improve the robustness and accuracy of the charging port identification.
A Safe Zone SMOTE Oversampling Algorithm Used in Earthquake Prediction Based on Extreme Imbalanced Precursor Data

Earthquake prediction based on extreme imbalanced precursor data is a challenging task for standard algorithms. Since even if an area is in an earthquake-prone zone, the proportion of days with earthquakes per year is still a minority. The general method is to generate more artificial data for the minority class that is the earthquake occurrence data. But the most popular oversampling methods generate synthetic samples along line segments that join minority class instances, which is not suitable for earthquake precursor data. In this paper, we propose a Safe Zone Synthetic Minority Oversampling Technique (SZ-SMOTE) oversampling method as an enhancement of the SMOTE data generation mechanism. SZ-SMOTE generates synthetic samples with a concentration mechanism in the hyper-sphere area around each selected minority instances. The performance of SZ-SMOTE is compared against no oversampling, SMOTE and its popular modifications adaptive synthetic sampling (ADASYN) and borderline SMOTE (B-SMOTE) on six different classifiers. The experiment results show that the quality of earthquake prediction using SZ-SMOTE as oversampling algorithm significantly outperforms that of using the other oversampling algorithms.
Modeling Affect-based Intrinsic Rewards for Exploration and Learning

Positive affect has been linked to increased interest, curiosity and satisfaction in human learning. In reinforcement learning, extrinsic rewards are often sparse and difficult to define, intrinsically motivated learning can help address these challenges. We argue that positive affect is an important intrinsic reward that effectively helps drive exploration that is useful in gathering experiences. We present a novel approach leveraging a task-independent reward function trained on spontaneous smile behavior that reflects the intrinsic reward of positive affect. To evaluate our approach we trained several downstream computer vision tasks on data collected with our policy and several baseline methods. We show that the policy based on our affective rewards successfully increases the duration of episodes, the area explored and reduces collisions. The impact is the increased speed of learning for several downstream computer vision tasks.
Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

In the last years, Unmanned Aerial Vehicles (UAVs) are being increasingly used in different kinds of inspections due to their maneuverability, flexibility, and efficiency. When deployed in the inspection of large structures, such as dam and slopes, the UAV must cover the largest possible area in the most efficient way. This task is denominated Coverage Path Planning (CPP). Determining the ideal path planning in CPP applications is complex due to dynamic obstacles that cannot be modeled beforehand or when a structural problem is identified, making the UAV to re-plain its trajectory. Therefore, this research proposes the use of bio-inspired metaheuristic techniques for real-time coverage path planning. The methodology dynamically adapts the UAV trajectory when structure failures and obstacles are found. The obtained results showed that the system is efficient, and it has adequate performance for large structures inspections.
Multi-frame co-saliency spatio-temporal regularization correlation filters for object tracking

The spatial regularization weight of the correlation filter is not related to the object content and the model degradation in the tracking process. To solve this problem, a new multi-frame co-saliency spatio-temporal regularization correlation filters (MCSRCF) is proposed for visual object tracking. To the best our knowledge, this is the first application of co-saliency regularization to CF-based tracking. In MCSRCF, grayscale features, directional gradient histogram (HOG) features and CNN features are extracted to improve the tracking precision of the tracker. Secondly, the three-dimensional spatial saliency and semantic saliency are introduced to obtain the initial weight of the spatial regularization with object content information. Then, the heterogeneous saliency fusion method is exploited to add a co-saliency spatial regularization term to the objective function to make the spatial penalty weight learn the change of the object region. In additional, the temporal saliency regularization is introduced to learn the information between adjacent frames, which reduces the overfitting effect caused by inaccurate samples. A variety of evaluations are conducted on public benchmarks, and the experimental results show that the proposed tracker achieves good robustness against many state-of-the-art trackers in various complex scenarios.
MultiModal Language Modelling on Knowledge Graphs for Deep Video Understanding

The natural language processing community has had a major interest in auto-regressive [4, 13] and span-prediction based language models [7] recently, while knowledge graphs are often referenced for common-sense based reasoning and fact-checking models. In this paper, we present an equivalence representation of span-prediction based language models and knowledge-graphs to better leverage recent developments of language modelling for multi-modal problem statements. Our method performed well, especially with sentiment understanding for multi-modal inputs, and discovered potential bias in naturally occurring videos when compared with movie-data interaction-understanding. We also release a dataset of an auto-generated questionnaire with ground-truths consisting of labels spanning across 120 relationships, 99 sentiments, and 116 interactions, among other labels for finer-grained analysis of model comparisons in the community.
Non-instantaneous Information Transfer in Physical Reservoir Computing

The Echo State Network reservoir computing model is used in many applications. The original equations have a form of ‘instantaneous’ information flow. Here we suggest using a more physically realistic form of the equations, which takes into account the time needed for information to flow and to be processed. We demonstrate the effect of this change on a timeseries task, sunspot prediction, on a dynamical system emulation task, NARMA10, and on formulating ‘reservoir of reservoir’ equations.