Reviewerly/exHarmony huggingface dataset:
A Novel Collision-Free Navigation Approach for Multiple Nonholonomic Robots Based on ORCA and Linear MPC

In the study of collision-free navigation methods of multirobots, much attention has been paid to the constraints of external environment. However, most of the wheeled mobile robots are subjected to nonholonomic constraints. A collision between robots may occur if the nonholonomic constraints are neglected. This paper presents an improved approach to collision-free navigation for multi-nonholonomic robots. This approach combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and Model Predictive Control (MPC) strategy. ORCA used a simple robot model, in which kinematics and dynamics are ignored. To cope with this problem, the MPC controller is introduced. In each ORCA step, the reference trajectory, reference control inputs, and “safe zones” are generated based on the new velocity. Consequently, the derived safe zone is transformed into the constraints of decision variables for a MPC controller. Finally, quadratic programming is used to solve the MPC problem by successive linearization of an error model of the mobile robot. Simulation results illustrate the effectiveness of the proposed method.
Learning to Optimize Non-Rigid Tracking

One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear system in the inner loop. In this paper, we employ learnable optimizations to improve tracking robustness and speed up solver convergence. First, we upgrade the tracking objective by integrating an alignment data term on deep features which are learned end-to-end through CNN. The new tracking objective can capture the global deformation which helps Gauss-Newton to jump over local minimum, leading to robust tracking on large non-rigid motions. Second, we bridge the gap between the preconditioning technique and learning method by introducing a ConditionNet which is trained to generate a preconditioner such that PCG can converge within a small number of steps. Experimental results indicate that the proposed learning method converges faster than the original PCG by a large margin.
Deep k-NN for Noisy Labels

Modern machine learning models are often trained on examples with noisy labels that hurt performance and are hard to identify. In this paper, we provide an empirical study showing that a simple $k$-nearest neighbor-based filtering approach on the logit layer of a preliminary model can remove mislabeled training data and produce more accurate models than many recently proposed methods. We also provide new statistical guarantees into its efficacy.
A Wavelet Based Deep Learning Method for Underwater Image Super Resolution Reconstruction

In order to solve the scattering degradation by turbulence and suspended particles in underwater imaging, traditional processing methods including image enhancement, restoration and reconstruction have been continuously researched.But most of them rely on degradation models, and there exist problems of illposed.Image super resolution reconstruction based on deep learning has become a hot topic in recent years.In order to further improve the effectiveness and efficiency of deep learning based methods, an improved image super-resolution reconstruction algorithm based on deep convolutional neural network is proposed in this paper.The wavelet basis which can effectively simulate the waveform and characteristics of underwater turbulence is selected to replace the neuron fitting function in order to improve the accuracy and efficiency of the algorithm.An improved dense block structure (IDB) is introduced into the network which can effectively solve the gradient disappearance problem of deep convolutional neural network and improve the training speed at the same time.The method proposed in this paper has been verified in laboratory flume, public data set and real water body.The experimental results show that under the same conditions, the proposed algorithm shows improvements on various evaluation parameters compared with DRFN, VDSR and DRCN method.So it can be concluded that the proposed method can effectively improve the quality of deep learning based reconstruction for imaging in natural water.
Open-World Learning for Radically Autonomous Agents

In this paper, I pose a new research challenge – to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. I illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. In addition, I propose a framework for characterizing environments in which goal-directed physical agents operate, along with specifying the ways in which those environments can change over time. In closing, I outline some approaches to the empirical study of such open-world learning.
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning

In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge.The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture.The systems are evaluated on different latency regimes.The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency ≤ 3).The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency ≤ 1000).
Low AND Depth and Efficient Inverses: a Guide on S-boxes for Low-latency Masking

In this work, we perform an extensive investigation and construct a portfolio of S-boxes suitable for secure lightweight implementations, which aligns well with the ongoing NIST Lightweight Cryptography competition. In particular, we target good functional properties on the one hand and efficient implementations in terms of AND depth and AND gate complexity on the other. Moreover, we also consider the implementation of the inverse S-box and the possibility for it to share resources with the forward S-box. We take our exploration beyond the conventional small (and even) S-box sizes. Our investigation is twofold: (1) we note that implementations of existing S-boxes are not optimized for the criteria which define masking complexity (AND depth and AND gate complexity) and improve a tool published at FSE 2016 by Stoffelen in order to fill this gap. (2) We search for new S-box designs which take these implementation properties into account from the start. We perform a systematic search based on the properties of not only the S-box but also its inverse as well as an exploration of larger S-box sizes using length-doubling structures. The result of our investigation is not only a wide selection of very good S-boxes, but we also provide complete descriptions of their circuits, enabling their integration into future work.
Can Neural Networks Automatically Score Essay Traits?

Essay traits are attributes of an essay that can help explain how well written (or badly written) the essay is.Examples of traits include Content, Organization, Language, Sentence Fluency, Word Choice, etc.A lot of research in the last decade has dealt with automatic holistic essay scoring -where a machine rates an essay and gives a score for the essay.However, writers need feedback, especially if they want to improve their writing -which is why traitscoring is important.In this paper, we show how a deep-learning based system can outperform feature-based machine learning systems, as well as a string kernel system in scoring essay traits.
EHR2Vec: Representation Learning of Medical Concepts From Temporal Patterns of Clinical Notes Based on Self-Attention Mechanism

Efficiently learning representations of clinical concepts (i. e., symptoms, lab test, etc.) from unstructured clinical notes of electronic health record (EHR) data remain significant challenges, since each patient may have multiple visits at different times and each visit may contain different sequential concepts. Therefore, learning distributed representations from temporal patterns of clinical notes is an essential step for downstream applications on EHR data. However, existing methods for EHR representation learning can not adequately capture either contextual information per-visit or temporal information at multiple visits. In this study, we developed a new vector embedding method called EHR2Vec that can learn semantically-meaningful representations of clinical concepts. EHR2Vec incorporated the self-attention structure and showed its utility in accurately identifying relevant clinical concept entities considering time sequence information from multiple visits. Using EHR data from systemic lupus erythematosus (SLE) patients as a case study, we showed EHR2Vec outperforms in identifying interpretable representations compared to other well-known methods including Word2Vec and Med2Vec, according to clinical experts' evaluations.
Guided Dropout

Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes random drop of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout. In this research, we propose “guided dropout” for training deep neural network which drop nodes by measuring the strength of each node. We also demonstrate that conventional dropout is a specific case of the proposed guided dropout. Experimental evaluation on multiple datasets including MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of the proposed guided dropout.
Homography-Based Minimal-Case Relative Pose Estimation With Known Gravity Direction

In this paper, we propose a novel approach to two-view minimal-case relative pose problems based on homography with known gravity direction. This case is relevant to smart phones, tablets, and other camera-IMU (Inertial measurement unit) systems which have accelerometers to measure the gravity vector. We explore the rank-1 constraint on the difference between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">euclidean homography matrix</i> and the corresponding rotation, and propose an efficient two-step solution for solving both the calibrated and semi-calibrated (unknown focal length) problems. Based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hidden variable technique</i> , we convert the problems to the polynomial eigenvalue problems, and derive new 3.5-point, 3.5-point, 4-point solvers for two cameras such that the two focal lengths are unknown but equal, one of them is unknown, and both are unknown and possibly different, respectively. We present detailed analyses and comparisons with the existing 6- and 7-point solvers, including results with smart phone images.
The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets

This work investigates empirically the impact of political party control over its candidates or vice versa on winning an election using a natural language processing technique called sentiment analysis (SA). To do this, a set of 7430 tweets bearing or related to #AnambraDecides2017 was streamed during the November 18, 2017, Anambra State gubernatorial election. These are Twitter discussions on the top five political parties and their candidates termed political actors in this paper. We conduct polarity and subjectivity sentiment analyses on all the tweets considering time as a useful dimension of SA. Furthermore, we use the word frequency to find words most associated with the political actors in a given time. We find most talked about topics using a topic modeling algorithm and how the computed sentiments and most frequent words are related to the topics per political actor. Among other things, we deduced from the experimental results that even though a political party serves as a platform that sales the personality of a candidate, the acceptance of the candidate/party adds to the winning of an election. For example, we found the winner of the election Willie Obiano benefiting from the values his party share among the people of the State. Associating his name with his party, All Progressive Grand Alliance (APGA) displays more positive sentiments and the subjective sentiment analysis indicates that Twitter users mentioning APGA are less emotionally subjective in their tweets than the other parties.
Human Language Technology

Human language technology encompasses a wide array of speech and text processing capabilities. The Defense Advanced Research Projects Agency's pioneering research on automatic transcription, translation, and content analysis were major artificial intelligence success stories that changed science fiction into social fact. During a 40‐year period, 10 seminal DARPA programs produced breakthrough capabilities that were further improved and widely deployed in popular consumer products, as well as in many commercial, industrial, and governmental applications. The Defense Advanced Research Projects Agency produced the core enabling technologies by setting crisp, aggressive, and quantitative technical objectives; by providing strong multiyear funding; and by using the Defense Advanced Research Projects Agency's Common Task Method, which was powerful, efficient, and easy to administer. To achieve these breakthroughs, multidisciplinary academic and industrial research teams working in parallel took advantage of increasingly large and diverse sets of linguistic data and rapidly increasing computational power to develop and use increasingly sophisticated forms of machine learning. This article describes the progression of technical advances underlying key successes and the seminal programs that produced them.
A Comparative Study Among Different Mathematical Sequences in 3D Image Steganography

This paper proposes a double-layer message security scheme.The message is first encrypted using AES-128 and is then embedded within a 3D image using least significant bit (LSB) substitution.The LSB substitution is carried out using multiple mathematical sequences and a comparison between the performance of each sequence is investigated.The proposed system is evaluated using a number of performance metrics including the mean square error (MSE), the peak signal to noise ratio (PSNR), the entropy, the mean structural similarity index measurement (MSSIM), the R measurement, the normalized cross correlation (NCC), the image fidelity (IF) and the image distance (ID).Furthermore, a comparison between the proposed schemes and other proposed works from the literature is performed to demonstrate its superiority.
Contact Tracing for Native Americans in Rural Arizona

Contact Tracing for Native Americans in Rural Arizona On a reservation in eastern Arizona, most of the Native Americans with Covid-19 live with others who are at high risk. Identifying patients who...
Efficient Detection of Phishing Websites Using Multilayer Perceptron

Phishing is a type of Internet fraud that aims to acquire the credential of users via scamming websites. In this paper, a novel approach is utilized that uses a Neural Network with a multilayer perceptron to detect the scam URL. The proposed system improves the accuracy of the scam detection system as it achieves a high accuracy percentage of 98.5%.
Hybrid gradient descent spider monkey optimization (HGDSMO) algorithm for efficient resource scheduling for big data processing in heterogenous environment

Abstract Big Data constructed based on the advancement of distributed computing and virtualization is considered as the current emerging trends in Data Analytics. It is used for supporting potential utilization of computing resources focusing on, on-demand services and resource scalability. In particular, resource scheduling is considered as the process of resource distribution through an effective decision making process with the objective of facilitating required tasks over time. The incorporation of heterogeneous computing resources by the Big Data consumers also permits the option of reducing energy usage and enhanced resource efficiency. Further, optimal scheduling of resources is considered as an NP hard problem due to the dynamic characteristics of the resources and fluctuating users’ demand. In this paper, a Hybrid Gradient Descent Spider Monkey Optimization (HGDSMO) algorithm is proposed to efficient resource scheduling by handling the issues and challenges in the Hadoop heterogenous environment. The proposed HGDSMO algorithm uses the Gradient Descentand foraging and social behavior of the spider monkey optimization algorithm involved in the objective of effective resource allocation. It is designed as the efficient task scheduling approach that balances the load of the cloud by allocating them to appropriate VMs depending on their requirements. It is also proposed as a dynamic resource management scheme for efficiently allocating the cloud resources for effective execution of clients’ tasks. The simulation results of the proposed HGDSMO algorithm confirmed to be potent in throughput, load balancing and makespan compared to the baseline hybrid meta-heuristic resource allocation algorithms used for investigation.
Learning Bayesian Networks That Enable Full Propagation of Evidence

This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the problem of learning multiple disjoint subgraphs that do not enable full propagation of evidence. This problem is highly prevalent in cases where the sample size of the input data is low with respect to the dimensionality of the model, which is often the case when working with real data. The paper presents a novel hybrid structure learning algorithm, called SaiyanH, that addresses this issue. The results show that this constraint helps the algorithm to estimate the number of true edges with higher accuracy compared to the state-of-the-art. Out of the 13 algorithms investigated, the results rank SaiyanH 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> in reconstructing the true DAG, with accuracy scores lower by 8.1% (F1), 10.2% (BSF), and 19.5% (SHD) compared to the top ranked algorithm, and higher by 75.5% (F1), 118% (BSF), and 4.3% (SHD) compared to the bottom ranked algorithm. Overall, the results suggest that the proposed algorithm discovers satisfactorily accurate connected DAGs in cases where other algorithms produce multiple disjoint subgraphs that often underfit the true graph.
Say No to Price Discrimination: Decentralized and Automated Incentives for Price Auditing in Ride-Hailing Services

As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers (SPs) usually collect users’ personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different users, which raises price fairness concerns. In this paper, we propose a smart price auditing system named Spas. Spas allows a user to purchase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fair Price Insurance</i> in the form of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Price Auditing Contract</i> , then the price of ride-hailing service (RHS) order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrate a theoretical model for practical payment flows based on real RHS user data and we implement Spas in Hyperledger Fabric to show that decentralizing and automating price auditing for RHS with financial incentives is technically feasible.
A First Global Oceanic Compilation of Observational Dissolved Aluminum Data With Regional Statistical Data Treatment

DATA REPORT article Front. Mar. Sci., 14 July 2020 | https://doi.org/10.3389/fmars.2020.00468
A Hierarchical Motion Planning for Mobile Manipulator

This paper focuses on motion planning for mobile manipulators that can be used to plan both the base and the manipulator. An Optimized Hierarchical Mobile Manipulator Planner (OHMP) is proposed, which consists of two phases: two‐dimensional mobile base path planning and three‐dimensional manipulator trajectory planning. In the two‐dimensional mobile base path planning phase, a hybrid sampling strategy combining bridge test and uniform sampling is adopted to optimize Probabilistic Roadmap‐based basic planning and improve the efficiency of overall planning. In the three‐dimensional manipulator trajectory planning phase, if the current manipulator configuration is in a collision state, then a secure configuration is searched, and the trajectory between them is planned. Given start and goal configurations, the OHMP can efficiently produce a collision‐free path in complex environments. The estimates are backed up by the experiment result. It proves the validity and rationality of the planner, and it is effective for motion planning in complex static environments. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
A Collaborative Compound Neural Network Model for Soil Heavy Metal Content Prediction

The prediction of soil heavy metal content is an important part of the management of soil heavy metal pollution, but it is often ignored. At present, there are few studies on the prediction of soil heavy metal content, and it is an urgent problem to choose an efficient method for soil heavy metal content prediction. In this paper, a collaborative compound neural network model (CCNN) was put forward to predict the soil heavy metal content, this model uses wavelet neural network (WNN) as the basic prediction model, and at the same time proposes a parallel bird swarm algorithm (PBSA) to solve the parameter optimization problem of WNN, based on the bird swarm algorithm (BSA), the PBSA not only increases the gathering behavior of individual, but also adopts sine transformation based on fitness difference ratio to carry out the following behavior of beggars to improve the global optimization ability, besides that, the acceptance criterion is used to compare the fitness of individuals after updating to avoid falling into a local optimum. Soil heavy metal content data from Yinchuan city of Ningxia and six new urban areas in Wuhan, China are used to make prediction experiments respectively, through compare with support vector machine (SVM), radial basis function neural network (RBFNN), WNN and bird swarm algorithm optimizes wavelet neural network (BSA-WNN), the experimental results demonstrate that the predicted value of the CCNN is closer to the actual value and has better prediction performance.