Reviewerly/exHarmony huggingface dataset:
Maximum Information Coverage and Monitoring Path Planning with Unmanned Surface Vehicles Using Deep Reinforcement Learning

Manual monitoring large water reservoirs is a complex and high-cost task that requires many human resources. By using Autonomous Surface Vehicles, informative missions for modeling and supervising can be performed efficiently. Given a model of the uncertainty of the measurements, the minimization of entropy is proven to be a suitable criterion to find a surrogate model of the contamination map, also with complete coverage pathplanning. This work uses Proximal Policy Optimization, a Deep Reinforcement Learning algorithm, to find a suitable policy that solves this maximum information coverage path planning, whereas the obstacles are avoided. The results show that the proposed framework outperforms other methods in the literature by 32% in entropy minimization and by 63% in model accuracy.
Learning to Herd Amongst Obstacles from an Optimized Surrogate

This paper investigates how a shepherd robot can efficiently steer a coherent group by intelligently moving behind the group in obstacle-filled environments. It was been shown that a model trained by deep reinforcement learning can guide a small number (2–4) of agents among obstacles. However, herding a larger group becomes significantly more challenging because it exhibits the characteristics similar to manipulating a deformable object, i.e., the system is dynamic and the problem is highly underactuated. To overcome these challenges, we show that a model can be trained more effectively via an optimized surrogate, such as a potential field that optimizes the control quality of the group without explicitly considering the placement of the shepherd. Our experiments demonstrate that the trained model is robust to noise for group behaviors and environments. Compared to the rule-based method, the proposed approach maintains a higher probability of guiding the sheep and better control quality.
USING GAME IN IMPROVING STUDENTS’ VOCABULARY MASTERY

This study was aimed at improving vocabulary mastery through games. It is to see whether game can improve students’ vocabulary mastery and to know the effectiveness of using game in teaching class. The participants of this study were 10 students and one English lecturer at University of Muhammadiyah Aceh. The data were obtained from questionnaires that distributed to students and interview one lecturer to add more information. In analyzing the data, this study applied the qualitative descriptive method. The result of this study reveals that the implementation of game in teaching learning process is effective in which it encourages students’ motivation and involvement during the learning process. Indeed, it becomes the relevant way for lecturer to improve students’ vocabulary mastery.
The Feasibility of Deep Counterfactual Regret Minimisation for Trading Card Games

Counterfactual Regret Minimisation (CFR) is the leading technique for approximating Nash Equilibria in imperfect information games. It was an integral part of Libratus, the first AI to beat professionals at Heads-up No-limit Texas-holdem Poker. However, current implementations of CFR rely on a tabular game representation and hand-crafted abstractions to reduce the state space, limiting their ability to scale to larger and more complex games. More recently, techniques such as Deep CFR (DCFR), Variance-Reduction Monte-carlo CFR (VR-MCCFR) and Double Neural CFR (DN-CFR) have been proposed to alleviate CFR's shortcomings by both learning the game state and reducing the overall computation through aggressive sampling. To properly test potential performance improvements, a class of game harder than Poker is required, especially considering current agents are already at superhuman levels. The trading card game Yu-Gi-Oh was selected as its game interactions are highly sophisticated, the overall state space is many orders of magnitude higher than Poker and there are existing simulator implementations. It also introduces the concept of a meta-strategy, where a player strategically chooses a specific set of cards from a large pool to play. Overall, this work seeks to evaluate whether newer CFR methods scale to harder games by comparing the relative performance of existing techniques such as regular CFR and Heuristic agents to the newer DCFR whilst also seeing if these agents can provide automated evaluation of meta-strategies.
Exploiting Explainable Metrics for Augmented SGD

Explaining the generalization characteristics of deep learning is an emerging topic in advanced machine learning. There are several unanswered questions about how learning under stochastic optimization really works and why certain strategies are better than others. In this paper, we address the following question: can we probe intermediate layers of a deep neural network to identify and quantify the learning quality of each layer? With this question in mind, we propose new explainability metrics that measure the redundant information in a network's layers using a low-rank factorization framework and quantify a complexity measure that is highly correlated with the generalization performance of a given optimizer, network, and dataset. We subsequently exploit these metrics to augment the Stochastic Gradient Descent (SGD) optimizer by adaptively adjusting the learning rate in each layer to improve in generalization performance. Our augmented SGD - dubbed RMSGD - introduces minimal computational overhead compared to SOTA methods and outperforms them by exhibiting strong generalization characteristics across application, architecture, and dataset.
Identifying Categories of Domain Names by Using Deep Learning Methods

In recent years, the dramatic increase in the number of domain names has made it more difficult to accurately index and search web pages. An emerging method is to classify webpages according to various features of webpages, the server can accurately locate the search items according to the classification results during retrieval, and filter irrelevant or malicious domain name recommendations. Identifying the category of a domain name by just the URL is not accurate enough because of confusing domain names used by the malicious or irrelevant web pages. In this paper, we implement the task of domain names classification based on a group of features of domain names, including URL, title, description, content, and their combinations. Different from previous work, we not only pay attention to the scene of domain names but also focus on the categories according to their industries. We take four categories of banking, government, education, and transportation as examples to eximine effects of the selected domain name features. We constructed a dataset of five categories including the rejection class. We implement and fine-tune four deep learning models(BERT, RoBERTa, BERT-CNN and BERT-RNN) for the classification tasks on the dataset, and then we experiment with each model using different feature combinations as input, and compare the accuracy of the classification results. According to the results, we observe that with the combined features of URL and content as input, each model can achieve higher classification accuracy, with the highest classification accuracy reaching 94.78%
Cross-Level Semantic Segmentation Guided Feature Space Decoupling And Augmentation for Fine-Grained Ship Detection

Fine-grained ship detection in optical remote sensing images is a challenging problem due to its long-tailed distributed dataset, which is often coupled with the multi-scale of ship and complex environment. In this paper, a novel average instance area imbalance ratio (AIAIR) is firstly used for quantitatively evaluating long-tailed distribution and multi-scale coupled problem. Based on which, we propose the idea of feature space decoupling and augmentation guided by cross-Level semantic segmentation, where features on different classwise-balance level are scheduled. On this basis, a Siamese Semantic Segmentation Guided Ship Detection Network (SGSDet) is proposed to effectively facilitate fine-grained ship detection performance. Our proposed method can be easily plugged into existing object detection models. Numerical experiments show that the proposed method outperforms the baseline by 2.32% mAP on the ShipRSImageNet dataset without extra annotations.
MultiShadow: Shadow Synthesis for Multiple Virtual Objects

Image synthesis adds synthetic objects to existing images and makes them visually hard to distinguish. In order to make the synthetic objects more realistic, shadows are also required to be generated in the synthesis process. However, most existing shadow synthetic methods work poorly when there is no fixed lighting source or there are multiple objects that already existed in the original image. Hence in this paper, we propose a novel method called MultiShadow, which generates realistic shadows for virtual objects with better recognition of illumination information based on the existing objects in the original image. The experiments on benchmark datasets show that MultiShadow outperforms the state-of-the-art.
Introduction

Cloud computing is a widely popular paradigm nowadays for providing computing, data storage, and digital content services to end-users and enterprises via remote third-party computational resources and infrastructure. Unlike traditional computing, cloud computing users are charged according to their actual usage, without worrying about the sunk costs (e.g., equipment, maintenance and upgrade costs). Cloud computing service providers offer their cloud computing infrastructure, platforms and applications hosted in specialized facilities, as known as datacenters. Because of its benefits and convenience, cloud computing is projected to have a substantial growth of demand in the near future. In addition to traditional data retrieval and query tasks, there are a variety of novel applications that can be hosted in cloud computing, including machine learning, virtual reality, on-demand video streaming, and cloud-based gaming. Along with the accelerating demand for cloud computing, there comes a significant challenge—the energy consumption of datacenters has been skyrocketing. In 2018, datacenters world-wide consumed over 200 terawatt hours of electricity (that is more than 1% of global electricity demand). Such energy consumption is almost equivalent to the total energy consumption of several countries. For example, the energy consumption of datacenters is more than the national energy consumption of Spain [1].
An Unsupervised Network for Stereo Matching of Very High Resolution Satellite Imagery

Convolutional Neural Networks (CNN) were recently applied to stereo matching, which is one of the important steps in 3D reconstruction. In remote sensing using Very High Resolution (VHR) satellite imagery, CNN-based stereo matching is employed in Digital Surface Models (DSM) generation, achieving higher accuracy than conventional methods through supervised learning. Since ground-truth disparity maps calculated from reliable elevation data are hard to collect, unsupervised stereo matching methods are as significant as supervised ones. However, there are few studies on stereo matching of VHR satellite imagery with unsupervised learning. In this work, we propose an unsupervised stereo matching network for DSM generation. We also introduce a criterion to select the best epoch without using ground-truth data for validation in a training strategy of gradually increasing the weight of a smoothness loss. Experimental results show that our network performs better in the average endpoint error and the fraction of erroneous pixels than the baseline method of the used dataset without ground-truth data.
Analysis of the development of the creative potential of bachelors of technical universities

This paper presents approaches to the interpretation of the definition of "creative potential", analyzes the criteria for assessing the creative potential of students and the levels of its formation. The definition of "creativity" is fundamental to various fields of knowledge. We built our interpretation of our own definition of creative potential from the standpoint of the multivariance of approaches to the concept. In the course of the selection of criteria for assessing the creative potential, the qualities of the student’s personality to be studied were determined. The analyzed components of creative potential and the criteria for their assessment were the methodological basis for the development of the test questionnaire. The levels of formation of creative potential made it possible to single out the most significant reasons that hinder the successful development of the creative potential of student.
Asynchronous Byzantine-Resilient Distributed Optimization with Momentum

In this paper, we propose a novel asynchronous Byzantine-resilient distributed optimization algorithm. In our algorithm, we adopt multiple buffers in the server to regulate the asynchronous update of workers. In addition, to cope with the Byzantine workers, we combine robust aggregate operator with momentum-based update, which utilize the historical information to improve the resilience of distributed algorithm. It is shown that momentum is beneficial for resisting time-coupled attacks. We theoretically prove the convergence of our algorithm at a speed of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(1/\sqrt{T})$</tex> in the nonconvex setting. Finally, simulation results are provided to show the effectiveness of our algorithm.
Supply Chain Financial System Based on Blockchain Technology

To overcome the pain points of the traditional supply chain, the application of the combination of blockchain and supply chain finance was analyzed, and the feasibility of applying blockchain technology to the supply chain was expounded. Therefore, a supply chain financial system based on blockchain technology was designed and implemented, commodity transactions and cash flows between supply chain partnerships were realized. Then a variety of smart contracts were designed, and their specific functions were explained. The system uses Ethereum as the development platform and includes multi-level suppliers, banks, and other supply chain members. The interface for the trading of the products and financing is provided to the suppliers, finally, the performance of the system is tested. The results show that the proposed system has many advantages.
J-RR: Joint Monocular Depth Estimation and Semantic Edge Detection Exploiting Reciprocal Relations

Depth estimation and semantic edge detection are two key tasks in computer vision, which have made great progress. To date, how to associatively predict the depth and the semantic edge is rarely explored. In this work, we first propose a flexible two-branch framework that can make the two tasks take advantage of each other, achieving a win-win situation. Specifically, for the semantic edge detection branch, an Enhanced Edge Weighting strategy (EEW) is designed, which learns weight information from the by-product of depth branch, depth edge, to enhance edge perception in features. Meanwhile, we make depth estimation benefit from semantic edge detection through introducing Depth Edge Semantic Classification module (DESC). Furthermore, a double reconstruction (D-reconstruction) approach is presented, together with semantic edge-guided disparity smoothing loss to mitigate the ambiguities of the self-supervised manner for depth estimation. Experiments on the Cityscapes dataset demonstrate that our framework outperforms the state-of-the-art method in depth estimation along with a significant improvement in semantic edge detection.
Old Dog, New Tricks, New Energy Mix

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Model identification method of intervention AUV system based on numerical simulation

Traditional identification for the AUV dynamic models is based on the least square method. But it’s time-consuming and expensive. Especially, the intervention AUV is complex to identify the dynamic models. The paper identifies dynamic models for the intervention AUV based on numerical simulation. The paper uses the software “Star CCM+” as a tool. To further improve the identification precision for hydrodynamic damping coefficients, the paper modifies the virtual tank boundary function and AUV surface function in the simulation. Experiments on heading and longitudinal motion trajectory are conducted to verify the identification effectiveness of the improved computational fluid dynamics (CFD) method compared with that of the traditional least square method.
The Use Of Data Envelopment Analysis To Improve The Efficiency Of The Scientific Departments In The Technical Institute Of Al-Suwaira

This research deals with measuring the technical and scale efficiency of the departments of the Suwaira Technical Institute in Iraq utilizing the data envelope analysis method, which is a linear method, not a teacher.The technical and scale efficiency have been computed based on the fixed returns to volume (CCR) model and the variable returns to volume (BCC) model, according to the input and output directions.Furthermore, the mathematical model for evaluation and obtaining results has been designed using the software DEAP.V2.1.This work included all 7 departments of the Suwaira Technical Institute in Iraq for one academic year, 2020-2021.Moreover, this research compared the performance efficiency of the departments in the institute during the academic year 2020-2021 to analyze the extent of progress or delay in efficiency.One of the most important results obtained from this research is that the percentage of departments that obtained full technical competence (100%) for the year 2020-2021, according to the CCR model, is 57% of the total departments, and the direct input orientations, while the percentage of departments that obtained full efficiency according to the BCC model, with both output and input approaches, it is 71% of the total sections.Also, this research has shown that the percentage of sections that obtained full-Scale efficiency (100%) is 57%, and the reasons for scale inefficiency for sections that are scale inefficient and that did not obtain full efficiency, whether due to output conditions or input processes, or both.Furthermore, the research deals with the necessary measures and reforms to improve the performance of the inefficient departments to reach the level of the fully qualified departments by increasing their outputs or reducing their inputs according to certain scientific ratios, as well as the reference departments, which inefficient departments should imitate and simulate as they have the same conditions they are going through.The inefficient departments, however, were able to achieve full efficiency.In other words, these reference departments could produce greater outputs with the same or fewer inputs than the inefficient departments.
Adventures in TwineSpace: An Augmented Reality Story Format for Twine

Augmented reality games have moved into the mainstream thanks to headline-catching titles such as Pokémon GO (2016) and Harry Potter Wizards Unite (2019). However, these games represent only a small portion of what is possible in this space, as demonstrated by emerging independent and serious game efforts in this area. Such development is hindered by the challenges facing individual and casual designers in finding AR tools and accessible engines. By exploring the legacy of casual, "low-friction" AR development and text-game design, we argue previous attempts to integrate narrative engines with augmented reality (e.g. Argon.js + Twine, ARIS) and image recognition (Vuforia, AR.js) have yet to meaningfully center casual development. With this in mind, we present a working prototype called TwineSpace combining popular hypertext authoring tool Twine 2 with open-source augmented reality tools A-Frame and AR.js that opens the possibilities for play within the world of spatial narrative. By combining open-source solutions, we hope this new authoring experience can help improve spatial storytelling for indie game designers, teachers, storytellers, and casual creators alike.
A Hierarchical Deterministic Wallet Using Ed25519 Digital Signature Scheme

On the Internet, most financial applications apply public key cryptography to verify users’ identities, manage their digital assets, authenticate and authorize them, etc. In a blockchain system, this model is crucial to keep the system working, since there are no intermediaries or a central database for management. Commonly, users can manage their key pair using the blockchain wallet, also known as ‘crypto wallets.’ In practice, the user has to keep track of many key pairs for each asset, which is inefficient. We use a hierarchical deterministic wallet (HD wallet) to solve this problem, which can derive hundreds of child wallets from a single master key-pair. Currently, there are already multiple protocols for an HD wallet for Secp256k1 while those for Ed25519 are neither well-known nor well developed. In this thesis, we will try to analyze and build an HD wallet for Ed25519 signature schema.
Highway Surveillance System Using Deep Learning Artificial Neural Networks

This paper proposes a novel moving vehicle detection model for highway surveillance. The models are developed based on machine learning-based models by leveraging the powerful deep learning neural network. The purpose of the research is to study and analyze drivers' behaviours on the road. Methods are proposed to detect moving vehicles using the Darknet and TensorFlow frameworks. The results show that Tensorflow framework could barely correctly identify three vehicles out of nine vehicles on the video frame with one car being incorrectly classified as a truck with confident levels varying widely from 19.10% to 92.52% inconsistently having wide spreading in percentages. The darknet framework shows that all eight out of nine vehicles are correctly classified as a car with only one car being labelled incorrectly. The darknet framework has shown with improved confident levels ranging from 46.38% to 77.59% with lower spreading in percentages.
How Far is It? Distance Estimation and Reproduction Through a Double 3 Telepresence Robot

Telepresence offers to explore an environment without being physically there, but spatial orientation including distance estimation is impaired by limited sensory input. In the present study, we investigated how well 39 participants performed two types of distance estimation tasks while operating a Double 3 telepresence robot via a 2D interface in a web-browser. One task required to visually reproduce a distance that was experienced in virtual self-motion by observing a target that moved away and stopping it at the presumed distance. The second task required to reproduce a distance by virtual self-motion that was visually encoded. The results confirm common underestimation of distances in VR and demonstrate benefits of time-based estimation, which was possible in addition to vision-based estimation in the first task.