Reviewerly/exHarmony huggingface dataset:
Incentive Mechanism Design for Federated Learning in the Internet of Vehicles

In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.
Change-Patterns Mapping: A Boosting Way for Change Impact Analysis

Change impact analysis (CIA) is a specialized process of program comprehension that investigates the ripple effects of a code change in a software system.In this paper, we present a boosting way for change impact analysis via mapping the historical change-patterns to current CIA task in a cross-project scenario.The change-patterns reflect the coupling dependencies between changed entities in a change set.A traditional CIA tool (such as ImpactMiner) outputs an initial impact set for a starting entity.To boost the traditional CIA tool, our approach retrieves an equivalent entity from various historical change sets for the starting entity.Then, the change-patterns between the equivalent entity and the rest of entities in the change set are mapped to the CIA task at hand.For current CIA task, if an entity in the initial impact set involves the similar change-pattern with the starting entity when comparing with the mapped change-pattern, we will reward the impacted confidence of the entity.Accuracy improvements are observed in the experiments when applying our boosting method to three famous CIA tools, i.e., ImpactMiner, JRipples and ROSE.
Pros and Cons of A VR-based Flight Training Simulator; Empirical Evaluations by Student and Instructor Pilots

A medium-fidelity virtual reality (VR) flight simulator was developed to evaluate how aircraft pilots perceived simulated flights in the VR environment compared with conventional mockup-based simulators. In Experiment 1, student and instructor pilots conducted ten repeating flights in the simulator. Three extreme flight conditions were created, and participants rated perceptions of the extreme flights using multiple criteria. In Experiment 2, pilots manipulated G1000 electronic cockpit systems in the simulator during three repeating simulated flights and were asked to rate their perceptions of the manual controls. Participants perceived that the VR simulator was similar to or better than conventional simulators for all given Experiment 1 criteria and found that repetition made the operations easier. However, manipulating electronic cockpit systems was still not considered better than using conventional simulators, even though it became easier by repetition. Participants liked the 360-degree angle of visibility in the VR environment.
Improving accuracy of Pedestrian Detection using Convolutional Neural Networks

Pedestrian detection has become an interesting and challenging problem lately. The main bottleneck in the pedestrian detection is when pedestrians are far away from the camera. The pedestrians who are far from the camera, retaining much less information than the pedestrians who are close, and they also have different appearance. In this article, first a Region-based convolutional neural networks Faster RCNN with a pre-trained Resnet101 architecture is used to provide rich and distinct hierarchical feature representations as well as initial pedestrian proposals. Then, to improve the accuracy of the pedestrian detection, a saliency map is extracted from the video. To do this, the pretrained Yolo is developed to detect the object in the video and the Flownet is applied to extract the motion features. Finally long short-term memory with two convolutional layers is used to obtain the final saliency map. In this research, the Caltech dataset collected by the University of California has been used. The proposed method has been evaluated with a log average miss rate metric, which achieved a miss rate of 40.35%, and compared with the active detection module (ADM) method, 1.92% improvement is achieved.
BCES: A BlockChain based Credible E-Bidding System

In recent years, blockchain is experiencing a rapid development and has the huge potential of revolutionizing the information platform in the deposit application field. In this paper, a blockchain based credible e-bidding system (BCES) is presented to address operational compliance, multi-party coordination, and cybersecurity problem in the process of distribution, verification and backtracking of bidding data files. BCES is comprised of basic configuration management, contract business deployment, data service processing, system service support, data visualization. Through detailed design of a set of blockchain integration and transformation mechanism for bidding business, different stakeholders are connected together with the help of the alliance blockchain, forming a flat trust management model. The experiments demonstrate that the average TPS of BCES is stable at around 2600-2800, and the response time of the blockchain deposit platform is within the acceptable time range of 3s, and the error rate is very low. Currently, BCES has been deployed and applied in China Telecom Corporation and has achieved remarkable results.
Closing the Loop: Real-Time Perception and Control for Robust Collision Avoidance with Occluded Obstacles

Robots have been successfully used in well-structured and deterministic environments, but they are still unable to function in unstructured environments mainly because of missing reliable real-time systems that integrate perception and control. In this paper, we close the loop between perception and control for real-time obstacle avoidance by introducing a new robust perception algorithm and a new collision avoidance strategy, which combines local artificial potential fields with global elastic planning to maintain the convergence towards the goal. We evaluate our new approach in real-world experiments using a Franka Panda robot and show that it is able to robustly avoid dynamic or even partially occluded obstacles while performing position or path following tasks.
Sharing Is Caring – Design and Demonstration of a Data Privacy Tool for Interorganizational Transfer of Data

This paper presents a Data Privacy Tool (IDPT) that is designed to provide a holistic and secure data management in organizations for handling, securing, sharing and re-sharing of data. The current version of IDPT allows the data controller in the organizations to configure data encryptions based on privacy obligations in a given context. This paper presents an inter-organizational use case scenario and subsequent demonstration of IDPT highlighting its user-friendly interface, security features (e.g. 2-way authentication, deletion of data after processing) and transparency of handling data.
Robust Pedestrian Tracking in Crowd Scenarios Using an Adaptive GMM-based Framework

In this paper, we address the issue of pedestrian tracking in crowd scenarios. People in close social relationships tend to act as a group which is a great challenge to individually discriminate and track pedestrians on a LiDAR system. In this paper, we integrally model groups of people and track them in a recursive framework based on Gaussian Mixture Model (GMM). The model is optimized by an extended Expectation-Maximization (EM) algorithm which can adaptively vary the number of mixture components over scans. Experimental results both qualitatively and quantitatively indicate the reliability and accuracy of our tracker in populated scenarios.
An Advanced Black-Box Adversarial Attack for Deep Driving Maneuver Classification Models

Connected and autonomous vehicles (CAV) have been introduced to increase roadway safety and traffic flow efficiency. In CAV scenarios, an autonomous vehicle shares its current and near-future driving maneuvers in terms of different driving signals (e.g., speed, brake pedal pressure) with its nearby vehicles using wireless communication technologies. Deep neural network (DNN) models are usually used to process the driving maneuver time-series data over other machine learning algorithms due to the high prediction accuracy of DNN models. In this scenario, an attacker can send false driving maneuver signals to fool the DNN model to misclassify an input. The existing black-box adversarial attacks (which are for image datasets) require many queries to the DNN model to check if a generated attack will be successful (hence long time) or high amount of perturbation (low imperceptibility), and thus cannot be applied to the time-sensitive CAV scenarios featured by multi-dimensional time series driving data. In this paper, we present an Advanced black-box Adversarial Attack (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for the deep driving maneuver classification models. We first formulate an optimization problem for the attack generation with continuous search space to reduce the search time. To solve the optimization problem, A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> innovatively combines the binary search and optimization algorithm to improve the time-efficiency of searching the optimal solution. It first uses a binary partition technique to reduce the perturbation search space in solving the problem to improve time-efficiency. It then uses the zeroth-order stochastic gradient descent approach, which is featured by searching a solution faster for high-dimensional datasets, thus further improving time-efficiency. We evaluate the proposed A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> attack in terms of different metrics using two real driving datasets. The experimental results show that the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> attack requires up to 84.12% fewer queries and 57.67% less perturbation with 94.87% higher success rates than the existing black-box adversarial attacks.
DR<sup>2</sup>Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression

Visual tracking has yielded promising applications with unmanned aerial vehicle (UAV). In literature, the advanced discriminative correlation filter (DCF) type trackers generally distinguish the foreground from the background with a learned regressor which regresses the implicit circulated samples into a fixed target label. However, the predefined and unchanged regression target results in low robustness and adaptivity to uncertain aerial tracking scenarios. In this work, we exploit the local maximum points of the response map generated in the detection phase to automatically locate current distractors <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity. Substantial experiments conducted on three challenging UAV benchmarks demonstrate both excellent performance and extraordinary speed (~50fps on a cheap CPU) of our tracker.
Accelerometer-based human activity recognition using 1D convolutional neural network

Abstract Human activity recognition (HAR) is an important research field with a variety of applications in healthcare monitoring, fitness tracking and in user-adaptive systems in smart environments. The performance of the activity recognition system is highly dependent on the features extracted from the sensor data which makes the selection of appropriate features a very important part of HAR. A 1D CNN model trained on accelerometer data is suggested in the paper for automatic feature extraction in a HAR system. A semi-automatic approach is used that effectively and efficiently determines the number of convolutional layers in the network, the number of kernels and the size of the kernels. The experimental results show that the suggested model outperforms several existing recognition approaches that use the same data set.
Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation

Planar pushing remains a challenging research topic, where building the dynamic model of the interaction is the core issue. Even an accurate analytical dynamic model is inherently unstable because physics parameters such as inertia and friction can only be approximated. Data-driven models usually rely on large amounts of training data, but data collection is time consuming when working with real robots.In this paper, we collect all training data in a physics simulator and build an LSTM-based model to fit the pushing dynamics. Domain Randomization is applied to capture the pushing trajectories of a generalized class of objects. When executed on the real robot, the trained recursive model adapts to the tracked object's real dynamics within a few steps. We propose the algorithm Recurrent Model Predictive Path Integral (RMPPI) as a variation of the original MPPI approach, employing state-dependent recurrent models. As a comparison, we also train a Deep Deterministic Policy Gradient (DDPG) network as a model-free baseline, which is also used as the action generator in the data collection phase. During policy training, Hindsight Experience Replay is used to improve exploration efficiency. Pushing experiments on our UR5 platform demonstrate the model's adaptability and the effectiveness of the proposed framework.
A Distributed Range-Only Collision Avoidance Approach for Low-cost Large-scale Multi-Robot Systems

The challenges of developing low-cost, large-scale multi-robot navigation systems include noisy measurements, a large number of robots, and computing efficiency for collision avoidance. This paper presents a distributed motion planning framework for a large number of robots to navigate with robust collision avoidance using low-cost range only measurements. The novelty of this work is threefold. (1) Developing a distributed collision-free navigation system for a large-scale robot group in which each robot performs motion planning based on the noisy range measurements of neighboring robots; (2) Developing a set of algorithms for each robot to accurately estimate the relative positions and orientations based on the range measurements and relative velocities; (3) Developing a velocity obstacle (VO) based motion planning algorithm for each robot which can take into account of the estimation uncertainties in relative positions and orientations. The proposed approach is tested with various numbers of differential-driven robots in the Gazebo simulator and real-world experiments. Both simulation and experiment results validate the superior performance of the proposed approach compared to other state-of-art technologies.
ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODELING TO INTERPRETABILITY

Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and they cannot cope with non-Euclidean spaces efficiently. Second, as other data-driven techniques, deep learning models do not necessarily respect physical or causal relations. Finally, deep learning models are still obscure and resistant to interpretability. Advances are needed to cope with arbitrary signal structures and data relations, physical plausibility and interpretability. This paper discusses about ways forward to develop new DL methods for the Earth sciences in all three directions.
Multimodal strategies for balancing formality and informality

Abstract This paper investigates multimodal strategies for balancing formality and informality online. The analysis of 300 comment-reply interactions on a recipe sharing site in Japan demonstrates that writers tend to avoid being overly formal or informal in their messages. For example, most comments and replies are written in polite forms but many incorporate some plain forms and colloquial expressions. Linguistic features, however, are not the only way through which the writers manage an appropriate level of formality and informality. The study examines the role of kaomoji or Japanese-style emoticons for socio-relational work online. Some kaomoji function locally as cues for interpreting the sentences featuring kaomoji . All kaomoji , including those with local functions, work to enhance the social presence of the writers on the screen via pictographic gaze and gestures, which increases the perception of intimate rapport. The findings underscore the importance of a multimodal perspective in examining how people handle social relationships online.
Seeing the Vibration: Visual-Based Detection of Low Frequency Vibration Environment Pollution

Visual information can be easily obtained nowadays with more access to various photographic facilities. Appropriate logical analysis and processing of massive visual information could apply to flexible environment pollution detection. In this paper, we present a visual-based non-contact sensing method to detect the low frequency vibration pollution in daily life. The edge points of vibrating objects with more vibration information are extracted using bilateral filtering and Sobel operator. The Sobel operator weights the influence of the pixel position, which make the edge feature extraction effect better and the edge feature obtained more obvious. After that, the continuous frames of the vibration objects with edge information are transformed into several one-dimensional pixel level signals as the input of multi-scale network, which also improves the generalization ability. Experiments on daily objects under low frequency vibration are conducted. The detection accuracy is above 99%, which proves the reliability of the proposed method for the detection of low frequency vibration circumstance pollution.
File fragment recognition based on content and statistical features

Nowadays, the speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms are used to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. The issue of identifying the type of file fragment due to its importance in cybercrime issues as well as antivirus has been highly emphasized and has been addressed in many articles. Increasing the accuracy in this field on the types of widely used files due to the sensitivity of the subject of recognizing the type of file under study is the main goal of researchers in this field. Failure to identify the correct type of file will lead to deviations of the results and evidence from the main issue or failure to conclude. In this paper, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 Multiclass classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. The proposed recognition algorithm can recognize 6 types of useful files (PDF, TXT, JPG, DOC, HTML, EXE) and may distinguish a type of file fragments with higher accuracy than the similar works done.
Differential algebra enabled multi-target tracking for too-short arcs

Untracked space debris is the principal threat to operational satellites' functioning whose services have become a fundamental part of our daily life. Though some specialised sensors can detect objects down to sub-cm sizes in geostationary Earth orbit, only objects larger than 30 cm are currently being catalogued. Thus, small debris are only seldom observed, typically for a short amount of time when surveying the sky. Having to deal with short arcs, the data association's problem becomes relevant: one must find more observations of the same resident space object to precisely determine its orbit. This paper develops a new method enabled by differential algebra for track initialisation and catalogue build-up, within the framework of multi-target tracking. This is compared to literature methods that build on the concept of the admissible region and attributable to solve the problem of correlating sparse observations. The comparison is carried out on synthetic measurements and real optical observations obtained by the ZimSMART telescope on consecutive nights. Furthermore, simulated observations are used to assess whether raw data in the tracklets can be exploited to reduce the admissible region's size. Though the gain in computational efficiency is only limited, this paper effectively shows an alternative method to the Mahalanobis distance, where the success of correlation is less affected by the time separation of two observations.
Analysis of Traffic Flow in Urban Area for Satellite Video

With the development of satellite technology, the resolution of remote sensing data has been continuously improved. The launch of remote sensing video satellites has provided new opportunities and challenges for traffic analysis, which are benefit to optimize the traffic conditions suffered by people especially in cities. In this paper, a method is proposed to analyze the traffic flow conditions using remote sensing satellite video. Firstly, vehicle targets are detected by combining with road network data, vehicle brightness characteristics and its contrast information with the surrounding background. Then, the traffic flow, density, and average vehicle speed are calculated. Finally, traffic flow information can be obtained, such as traffic congestion, traffic flow collection, abnormal vehicle behavior, etc. With the satellite video acquired by the International Space Station and Skysat-1, the proposed method is validated to vehicle detection and traffic flow analysis, including average travel speed, vehicle turning, traffic congestion and so on.