Reviewerly/exHarmony huggingface dataset:
Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors Using Deep Reinforcement Learning

Robotic shepherding problem considers the control and navigation of a group of coherent agents (e.g., a flock of bird or a fleet of drones) through the motion of an external robot, called shepherd. Machine learning based methods have successfully solved this problem in an environment with no obstacles. Rule-based methods, on the other hand, can handle more complex scenarios in which environments are cluttered with obstacles and allow multiple shepherds to work collaboratively. However, these rule-based methods are fragile due to the difficulty in defining a comprehensive set of behavioral rules. To overcome these limitations, we propose the first known learning-based method that can herd agents amongst obstacles. By using deep reinforcement learning techniques combined with the probabilistic roadmaps, we train a shepherding model using noisy but controlled environmental and behavioral parameters. Our experimental results show that the trained shepherding controller is robust, namely, it is insensitive to the uncertainties originated from either the group behavioral models or from environments with a small of path homotopy classes. Consequently, the proposed method has a higher success rate, shorter completion time and path length than the rule-based behavioral methods have. These advantages are particularly prominent in more challenging scenarios involving more difficult groups and strenuous passages.
Large-scale vehicle trajectory reconstruction with camera sensing network

Vehicle trajectories provide essential information to understand the urban mobility and benefit a wide range of urban applications. State-of-the-art solutions for vehicle sensing may not build accurate and complete knowledge of all vehicle trajectories. In order to fill the gap, this paper proposes VeTrac, a comprehensive system that employs widely deployed traffic cameras as a sensing network to trace vehicle movements and reconstruct their trajectories in a large scale. VeTrac fuses mobility correlation and vision-based analysis to reduce uncertainties in identifying vehicles. A graph convolution process is employed to maintain the identity consistency across different camera observations, and a self-training process is invoked when aligning with the urban road network to reconstruct vehicle trajectories with confidence. Extensive experiments with real-world data input of over 7 million vehicle snapshots from over one thousand traffic cameras demonstrate that VeTrac achieves 98% accuracy for simple expressway scenario and 89% accuracy for complex urban environment. The achieved accuracy outperforms alternative solutions by 32% for expressway scenario and by 59% for complex urban environment.
Object Detection for Autonomous Driving using YOLO [You Only Look Once] algorithm

The field of autonomous driving is going to be the face of the automobile industry very soon. The number of accidents that take place because of human error currently is very high and it can be slashed to a huge extent with the advent of autonomous driving. One of the primary prerequisites and a huge part of autonomous driving is dependent on object detection through computer vision, this paper aims at aiding towards the field of autonomous driving by helping detect objects with the use of deep learning algorithms. Research work used state-of-the-art algorithm YOLO (you only look once) to detect different objects that appear on the road and classified into the category that they belong to with the help of bounding boxes. The weights of the YOLO v4 is utilized to custom train our model to detect the objects and the data will be collected from the open images dataset using its OIDv4 toolkit.
Carcinoma Type Classification From High-Resolution Breast Microscopy Images Using a Hybrid Ensemble of Deep Convolutional Features and Gradient Boosting Trees Classifiers

Breast cancer is one of the main causes behind cancer deaths in women worldwide. Yet, owing to the complexity of the histopathological images and the arduousness of manual analysis task, the entire diagnosis process becomes time-consuming and the results are often contingent on the pathologist's subjectivity. Thus developing an automated, precise histopathological image classification system is crucial. This paper presents a novel hybrid ensemble framework consisting of multiple fine-tuned convolutional neural network (CNN) architectures as supervised feature extractors and eXtreme gradient boosting trees (XGBoost) as a top-level classifier, for patch wise classification of high-resolution breast histopathology images. Due to the semantic complexity of the patch images, a single CNN architecture may not always extract high quality features, and the traditional Softmax classifier might not provide ideal results for classifying the CNN extracted features. Thus we aim to improve patch wise classification by proposing a hybrid ensemble model that incorporates different discriminating feature representations of the patches, coupled with XGBoost for robust classification. Experimental results show that our proposed method outperforms state-of-the-art methods to the best of our knowledge.
Attractiveness of game elements, presence, and enjoyment of mobile augmented reality games: The case of Pokémon Go

• Enjoyment of Pokémon Go is positively associated with autonomy and competence need satisfaction. • Autonomy-supportive game elements are associated with autonomy need satisfaction. • Relatedness-supportive game elements are associated with relatedness need satisfaction. • Game enjoyment is associated with gameplay length positively. • The satisfaction of autonomy need is associated with both presence and enjoyment. Building on the enjoyment-as-need-satisfaction model, this study examines the attractiveness of various game elements of Pokémon Go to its players and the associations among the attractiveness of game elements, players’ need satisfaction, sense of presence, enjoyment, and length of gameplay. Participants ( N = 719) who had played Pokémon Go filled out an online survey. A research model with the aforementioned constructs was tested using structural equation modeling. This study found that autonomy- and relatedness-supportive elements associated with autonomy and relatedness need satisfaction of the player, which related to presence significantly. The satisfaction of autonomy and competence needs related to game enjoyment, which influenced gameplay length. Theoretical and practical implications were discussed.
A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm

The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering.
Identification of minerals in subtropical soils with different textural classes by VIS–NIR–SWIR reflectance spectroscopy

The physical and chemical attributes of soils are strongly influenced by the nature of the minerals they contain and their concentration. Thus, soil texture is directly dependent on the content in clayey minerals, which influences a number of characteristics such as water dynamics. Although the mineralogical composition of soil is usually determined by X-ray diffraction spectroscopy, this technique is expensive and time-consuming, and uses toxic materials, all of which makes it impractical for obtaining large data sets. Also, available methods for acquiring, interpreting and examining visible–near infrared–shortwave infrared (VIS–NIR–SWIR) spectra are largely ineffective with tropical soils. The aim of this work was to ascertain whether VIS–NIR–SWIR reflectance spectroscopy (350–2500 nm) is useful for identifying minerals in subtropical soils as classified by textural class. For this purpose, soil samples were collected at 66 points at three different soil depths (0–20, 20–40 and 40–60 cm) over a study area located in the State of Rio Grande do Sul (Brazil). The soil texture were determined with the pipette method, and soil spectra were recorded on a FieldSpec Pro VIS-NIR-SWIR laboratory spectrophotometer. Soil minerals were identified, and their proportions determined, from the second-derivative of the Kubelka–Munk (KM) function for the spectra. Five main minerals were thus identified from their spectral signatures, namely: hematite, goethite, kaolinite, chlorite and illite. Identification of the minerals was facilitated by classifying the samples according to texture. The higher the clay content was, the higher was the spectral amplitude of the minerals identified. Those textural classes with the highest clay contents exhibited the greatest proportions of iron oxides and of clay minerals such as kaolinite. These relationships allowed more comprehensive analysis of the soils and expeditious characterization of the study area in terms of texture and mineralogy with a view to facilitating decision-making agricultural support policies.
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player

This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile robots that map raw sensor data to linear and angular velocities and navigate in an unknown environment without a map. An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data. A game format simulation framework is designed to allow the human player to tele-operate the mobile robot to a goal and human action is also scored using the reward function. Both human player data and self-playing data are sampled using prioritized experience replay algorithm. The proposed algorithm and training strategy have been evaluated in two different experimental configurations: Environment 1, a simulated cluttered environment, and Environment 2, a simulated corridor environment, to investigate the performance. It was demonstrated that the proposed method achieved the same level of reward using only 16&#x0025; of the training steps required by the standard Deep Deterministic Policy Gradient (DDPG) method in Environment 1 and 20&#x0025; of that in Environment 2. In the evaluation of 20 random missions, the proposed method achieved no collision in less than 2 h and 2.5 h of training time in the two Gazebo environments respectively. The method also generated smoother trajectories than DDPG. The proposed method has also been implemented on a real robot in the real-world environment for performance evaluation. We can confirm that the trained model with the simulation software can be directly applied into the real-world scenario without further fine-tuning, further demonstrating its higher robustness than DDPG. The video and code are available: https://youtu.be/BmwxevgsdGc https://github.com/hanlinniu/turtlebot3_ddpg_collision_avoidance
Exploration-RRT: A multi-objective Path Planning and Exploration Framework for Unknown and Unstructured Environments

This article establishes the Exploration-RRT algorithm: A novel general-purpose combined exploration and path planning algorithm, based on a multi-goal Rapidly-Exploring Random Trees (RRT) framework. Exploration-RRT (ERRT) has been specifically designed for utilization in 3D exploration missions, with partially or completely unknown and unstructured environments. The novel proposed ERRT is based on a multi-objective optimization framework and it is able to take under consideration the potential information gain, the distance travelled, and the actuation costs, along trajectories to pseudorandom goals, generated from considering the on-board sensor model and the non-linear model of the utilized platform. In this article, the algorithmic pipeline of the ERRT will be established and the overall applicability and efficiency of the proposed scheme will be presented on an application with an Unmanned Aerial Vehicle (UAV) model, equipped with a 3D lidar, in a simulated operating environment, with the goal of exploring a completely unknown area as efficiently and quickly as possible.
Student Thoughts on Virtual Reality in Higher Education—A Survey Questionnaire

This paper explores the benefits of using Virtual Reality (VR) technologies in higher education. The theoretical part investigates the classical education system and its features in order to compare advantages of using VR systems in education. VR technologies and its current state in industry and in education were explored in addition to which branches of higher education use these systems. A survey was conducted through an online questionnaire where respondents (N = 55) gave their opinion on VR and the implementation of VR technologies in education. Three hypotheses related to the use of VR technology, student interest, and learning outcomes as well as the effectiveness, immersiveness and the effect of VR systems on the users were tested through 27 questions.
Aggregatable Distributed Key Generation

In this paper, we introduce a distributed key generation (DKG) protocol with aggregatable and publicly-verifiable transcripts. Compared with prior publicly-verifiable approaches, our DKG reduces the size of the final transcript and the time to verify it from $$\mathcal {O}(n^2)$$ to $$\mathcal {O}(n \log {n})$$ , where n denotes the number of parties. As compared with prior non-publicly-verifiable approaches, our DKG leverages gossip rather than all-to-all communication to reduce verification and communication complexity. We also revisit existing DKG security definitions, which are quite strong, and propose new and natural relaxations. As a result, we can prove the security of our aggregatable DKG as well as that of several existing DKGs, including the popular Pedersen variant. We show that, under these new definitions, these existing DKGs can be used to yield secure threshold variants of popular cryptosystems such as El-Gamal encryption and BLS signatures. We also prove that our DKG can be securely combined with a new efficient verifiable unpredictable function (VUF), whose security we prove in the random oracle model. Finally, we experimentally evaluate our DKG and show that the per-party overheads scale linearly and are practical. For 64 parties, it takes 71 ms to share and 359 ms to verify the overall transcript, while for 8192 parties, it takes 8 s and 42.2 s respectively.
Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.
Structural Analysis of System Dynamics Models

System dynamics (SD) is an established discipline to model and simulate complex dynamic systems. The primary goal of SD is to evaluate and design new policies that can impact the system under study in a desired way. Policy design, that is, identifying effective model levers, however, is a challenge and in many cases trial-and-error driven. In this article, we introduce a new and coherent framework for model analysis, called structural analysis methods (SAM), to facilitate the policy design process in complex SD models. SAM provides a resource-efficient and effective means for the detection of candidate policy parameters. It enables to identify intended and unintended effects of activating these policy parameters, and to discover candidate structural changes such as introducing new variables and links in SD models. The main innovation of SAM is that it translates the structure of SD models into weighted digraphs allowing algorithmic tools from the realms of graph theory and network science to be applied to SD. SAM is validated on the basis of two well-known simulation models of increasing complexity: the third-order Phosphorus Loops in Soil and Sediment (PLUM) model and the fifth-order World2 model. The validation shows that SAM seems to be most valuable for the analysis of more complex simulation models (World2) and is less suited for the analysis of low complexity models (PLUM).
Self-Supervised Euphemism Detection and Identification for Content Moderation

Fringe groups and organizations have a long history of using euphemisms—ordinary-sounding words with a secret meaning—to conceal what they are discussing. Nowadays, one common use of euphemisms is to evade content moderation policies enforced by social media platforms. Existing tools for enforcing policy automatically rely on keyword searches for words on a "ban list", but these are notoriously imprecise: even when limited to swearwords, they can still cause embarrassing false positives [1]. When a commonly used ordinary word acquires a euphemistic meaning, adding it to a keyword-based ban list is hopeless: consider "pot" (storage container or marijuana?) or "heater" (household appliance or firearm?) The current generation of social media companies instead hire staff to check posts manually, but this is expensive, inhumane, and not much more effective. It is usually apparent to a human moderator that a word is being used euphemistically, but they may not know what the secret meaning is, and therefore whether the message violates policy. Also, when a euphemism is banned, the group that used it need only invent another one, leaving moderators one step behind.This paper will demonstrate unsupervised algorithms that, by analyzing words in their sentence-level context, can both detect words being used euphemistically, and identify the secret meaning of each word. Compared to the existing state of the art, which uses context-free word embeddings, our algorithm for detecting euphemisms achieves 30–400% higher detection accuracies of unlabeled euphemisms in a text corpus. Our algorithm for revealing euphemistic meanings of words is the first of its kind, as far as we are aware. In the arms race between content moderators and policy evaders, our algorithms may help shift the balance in the direction of the moderators.
Adaptive Data Structure Regularized Multiclass Discriminative Feature Selection

Feature selection (FS), which aims to identify the most informative subset of input features, is an important approach to dimensionality reduction. In this article, a novel FS framework is proposed for both unsupervised and semisupervised scenarios. To make efficient use of data distribution to evaluate features, the framework combines data structure learning (as referred to as data distribution modeling) and FS in a unified formulation such that the data structure learning improves the results of FS and vice versa. Moreover, two types of data structures, namely the soft and hard data structures, are learned and used in the proposed FS framework. The soft data structure refers to the pairwise weights among data samples, and the hard data structure refers to the estimated labels obtained from clustering or semisupervised classification. Both of these data structures are naturally formulated as regularization terms in the proposed framework. In the optimization process, the soft and hard data structures are learned from data represented by the selected features, and then, the most informative features are reselected by referring to the data structures. In this way, the framework uses the interactions between data structure learning and FS to select the most discriminative and informative features. Following the proposed framework, a new semisupervised FS (SSFS) method is derived and studied in depth. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.
Identification of an Autonomous Underwater Vehicle hydrodynamic model using three Kalman filters

The controllability and maneuverability of an Autonomous Underwater Vehicle (AUV) in practical applications need to be properly validated and assessed before the prototype is finalized for manufacturing. With regard to mathematical system model of the AUV, hydrodynamic coefficients have a dominant effect on the quality of vehicle pre-testing and evaluation, which is crucial to be estimated with adequate accuracy to curb the uncertainty from modeling simplifications. The standard time domain discrete Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms are two promising numerical approximation approaches for hydrodynamic identification technique with favourable computational complexity and acceptable estimation precision. In this paper, a study with respect to time domain discrete optimized UKF (OUKF) algorithm based on recursive tuning rule and update gradient descent lemma for a typical prototype known as NPS AUV II will be proposed to enhance adaptability and prediction performance of the identification approach with appropriate verification. In addition, Auto Regressive Moving Average (ARMA) noisy model needs to be included into three Kalman Filter (KF) algorithms to further improve the estimation precision and perturbations inhibiting performance. Pre-processing numerical model validation and non-dimensional viscous linear damping coefficient identification based on three KF algorithms will be implemented respectively to provide an assistant pre-assessment for the vehicle. In accordance with comparable outputs and estimation-experiment errors, the OUKF identification algorithm is certified to be more precise and superior compared with EKF and UKF approaches in the presence of ARMA noisy model.