Reviewerly/exHarmony huggingface dataset:
Adaptive electronic differential control of vehicle by torque balance

Abstract Current adaptive torque balancing control of electric wheel-driven vehicle has shortcomings in electronic differential control of drive motor by using rotation speed mode. In order to solve this problem, an adaptive electronic differential control method of electric wheel-driven vehicle is proposed in this paper by torque balance. Firstly, by starting from the kinematics and dynamics of vehicle steering, the speed and force of each drived wheel in the steering are analyzed to explain the auxiliary role of electronic differential control to adaptive torque balancing, as well as influence of steering radius in vehicle. Then, electronic differential distribution by torque of wheel is used to control the abnormal jump interference and calculate the optimal combination of parameters in electronic differential control system. Finally, based on the optimal combination of these parameters, an adaptive electronic differential control of electric wheel-driven vehicle by torque balance is realized with fuzzy control in active and the reactive power. Experimental results show that the proposed method suppresses the abnormal jump interference factors of electronic differential control, as well as realizes the differential functions in control system. It has far-reaching significance by provideing a basic guarantee to realize adaptive electronic differential control system in electronic wheel-driven vehicle.
BLOCKCHAIN: PHILOSOPHY, TECHNOLOGY, APPLICATIONS AND RISKS

The main ideas and properties of blockchain have been examined: architecture, models of blockchain use, in particular, decentralized autonomous organizations and applications, private blockchain – systems and blockchain – services. Blockchain infrastructure in implementation has been analyzed: reality and myths. An attention has been paid to current trends of blockchain usage in business and society in terms of opportunities, threats and risks. The areas of blockchain implementation have been presented, such as government, business, financial organizations, cryptocurrency and ICO, smart contracts and current trends of using blockchain technology.
Flexible and Accurate Calibration Method for Non-Overlapping Vision Sensors Based on Distance and Reprojection Constraints

This paper addresses the problem of flexible and accurate global calibration for multiple non-overlapping vision sensors in a confined workspace. Instead of using an auxiliary calibration pattern, the proposed method uses one laser tracker and only its accessory target sphere to obtain all the 3D calibration points and then accomplish the initial estimation of pose between the vision sensors. Then, the 3D calibration points and the extrinsic parameters between vision sensors are further optimized via the bundle adjustment algorithm based on the distance and reprojection constraints. Experiments were conducted to validate the performance and the experimental results demonstrate that the distance error can be decreased from 3.5 mm to 0.8 mm after introducing the distance and reprojection constraints.
Improving Control of SST using Type-2 Neuro-Fuzzy Controller with Elliptic Membership Function

As it is known, the most basic and important components in power systems are classical transformers which are passive interface between high voltage and low voltage systems. Classical transformers have undesirable characteristics such as poor voltage regulation, large size/weight and sensitivity to harmonics etc. In parallel with technological developments, solid state transformers (SSTs) which are considered to be one of the most indispensable components for future electrical energy systems, have attracted intense interest. In this paper, SST structure consisting of three stages is modeled in Matlab/Simulink environment. Type-2 Neuro-Fuzzy Controller (T2NFC) which has robust and adaptive structure is proposed for controlling of all stage of SST. In addition, the Elliptic Membership Function (MF) is used for T2NFC. The proposed controller is applied to SST structure and simulation studies are performed to verify the performance of the controller.
Simultaneous multi-descent regression and feature learning for facial landmarking in depth images

Abstract Face alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations.
KL Divergence-Based Pheromone Fusion for Heterogeneous Multi-Colony Ant Optimization

In this paper we present a heterogeneous multi-colony ant optimization with a novel interaction strategy named pheromone fusion to balance the search ability and the convergence speed of the conventional ant colony optimization. The pheromone fusion performs interaction directly and effectively by the interchange of the pheromone matrices. It could exploit the benefits of pheromone distribution and take full use of the advantages of heterogeneous sub-colonies. There are also two states defined in this study to control the interaction. The global state based on KL divergence determines which sub-colonies should interact with each other, while the local state based on information entropy decides when a sub-colony starts interaction. These two states greatly improve the adaptability and ensure the effectiveness of the interaction. In addition, a reward and punishment strategy is introduced to adjust the pheromone distribution and facilitate the interaction. The experimental results on the Traveling Salesman Problem demonstrate that the proposed algorithm outperforms the multi-colony algorithms presented in some recent works. The studies also indicate that the proposed algorithm could improve the solution quality and accelerate the convergence compared with single-colony algorithms.
A Scalable Sampling-Based Optimal Path Planning Approach via Search Space Reduction

Many sampling strategies in Sampling-Based Planning (SBP) often consider goal and obstacle population and may however become less efficient in large and cluttered 3D environments with a goal distanced away. This paper presents a search-space-Reduced optimal SBP approach (RSBP) for a rigid body. This reduced space is found by a sparse search tree, which is enabled by a Metric Function (MF) built on a neural network. The offline-learnt MF estimates the minimum traveling cost between any two nodes in a fixed small workspace with various obstacles. It allows connections of two sparse nodes without path planning, where the connections represent the traveling costs (not paths). It is proven that the asymptotic optimality is preserved in the RSBP (assuming a zero-error MF) and the optimality degeneration is bounded (assuming a bounded-error MF). The computational complexity during planning is shown linear to the Lebesgue measure of the entire search space (assuming the same sampling density across environments). Numerical simulations have shown that in tested large and cluttered environments the RSBP is at least as fast as the bidirectional fast marching tree* and informed rapidly exploring random tree*, with planned paths of similar optimality. The results also have shown the RSBP's improved scalability to large environments and enhanced efficiency in dealing with narrow passages.
Management Students’ "Soft Skills" in Foreign Language Training at Technical University

The article discusses the need for the bachelor students’ soft skills development in technical universities. The main objective of our research is the identification of the conceptual model "soft skills" opportunities in technical students training. Moreover, the demand of these skills in modern labor market is accented. The term "soft skills" is one of new tracks of the dynamically changing world. The article includes the content - analysis of the word «soft skills» providing the integrity of the term perception and its contents, applicability level in communication, and also some ways of the students' soft skills development in their foreign language practical training.
Quantum coherence of spatial photonic qudits: experimental measurement and path-marker analysis

Discussions about quantum interference, indistinguishability and superpostion between quantum states goes back to the beginning of quantum mechanics, but the theoretical problem concerning quantitative measures for quantum coherence was only recently solved by Baumgratz et al. [Phys. Rev. Lett. 113 140401 (2014)]. Since then many works have explored one of the possible coherence measures, the l1 norm, which has not yet been experimentally obtained for spatial photonic states in high dimensions. In this article we study states prepared with photons crossing multiple slits and implement the theoretical proposal of T. Paul and T. Qureshi [ Phys. Rev. A 95 042110 (2017)] to determine experimentally the l1 norm of qutrit states. We analyze the method validity and present an alternative for states in which one of their assumptions does not apply. We also discuss the requirements necessary for using an interference pattern for measuring quantum coherence of qudits by treating the measure-operator that describe the detection optical configuration as path-markers, relating them with the quantum coherence determination.
Blockchain-Based Threshold Electronic Voting System

In this paper, we propose a secure blockchain-based electronic voting (e-voting) system with distributed authorities. Specifically, we employ threshold blind signature to distribute trust for registration; and threshold ElGamal decryption to distribute trust in ballots tallying. Combining these techniques with decentralized blockchain technology, our system can achieve verifiability, eligibility, fairness and anonymity with reduced trust. We also analyze the efficiency of our system by implementing the proposed e-voting system on a typical laptop for performance evaluation. Our system is efficient enough to be employed in practice based on the experiment results.
Nonlinear autoregressive sieve bootstrap based on extreme learning machines

The aim of the paper is to propose and discuss a sieve bootstrap scheme based on Extreme Learning Machines for non linear time series. The procedure is fully nonparametric in its spirit and retains the conceptual simplicity of the residual bootstrap. Using Extreme Learning Machines in the resampling scheme can dramatically reduce the computational burden of the bootstrap procedure, with performances comparable to the NN-Sieve bootstrap and computing time similar to the ARSieve bootstrap. A Monte Carlo simulation experiment has been implemented, in order to evaluate the performance of the proposed procedure and to compare it with the NN-Sieve bootstrap. The distributions of the bootstrap variance estimators appear to be consistent, delivering good results both in terms of accuracy and bias, for either linear and nonlinear statistics (such as the mean and the median) and smooth functions of means (such as the variance and the covariance).
Super Trouper: The Playful Potential of Interactive Circus Training

Training can be challenging at times, and even more so to users with movement mastery and body awareness issues,. like children with sensory-based motor disorder (SBMD). They often experience less enjoyment when engaging with physical activities. In this project, our goal is to support physical training of children with SBMD through the design of playful training activities and technology. Drawing from our observations during a technology-supported circus training course with circus instructors, we identify play potentials that can inspire future technology and activity design. We surface key elements that supported the emergence of play and playfulness, including strategies used by the instructors, and technology features. We discuss how these can be built on in future design iterations.
Electronic Field Trips for Science Engagement: The Streaming Science Model

While institutions of higher education work to engage PK-12 youth in STEM (science, technology, engineering, and mathematics) concepts and careers via in-person programming, PK-12 teachers and students face many logistical and access constraints for physically traveling to sites off of school grounds during the school day.Throughout the years, electronic field trips (EFTs) have offered a digital way for schools to engage in meaningful ways with museums, parks, laboratories, and field research sites.In order for EFTs to be effective, they should be cost effective and created collaboratively with teachers, students, subject matter experts, and instructional design and communication professionals.Streaming Science is an online science communication platform that aimed to develop and implement an effective EFT model.Three Streaming Science live interactive EFTs webcasted online were piloted from various locations during 2017-2019 to test wireless internet connections, mobile technologies, STEM content, and impacts on students' interest, attitudes, and learning.The model proved iPads and mobile applications in the field for web streaming were effective for connecting scientists with school audiences.In this professional development article, authors describe the Streaming Science EFT model, including recommendations for instructional design, the pre-production process, content development, teacher collaboration, student engagement, mobile hardware and software, and assessment.
emmy – Trust-Enhancing Authentication Library

People, organizations, devices need to make many kinds of claims as part of their everyday activities. Digital credentials can enable to transmit instantly verifiable claims about their name, date of birth, gender, location, accomplishments. Some privacy-enhancing digital credentials enable revealing only part of your identity and thus hiding all information that is not necessarily needed for the online service. In the past two decades, several privacy- and trust-enhancing authentication techniques and approaches have been proposed to implement such verifiable digital credentials, mostly on the theoretical level. Some implementations exist, but either lack functionalities, rely on heavy computational machinery or are not available in open source. This paper presents emmy, a fully-fledged open source cryptographic library for secure, privacy-aware, and trust-enhancing authentication towards online services.
Digital Forensic InnoDB Database Engine for Employee Performance Appraisal Application

Data is something that can be manipulated by irresponsible people and causing fraud. The use of log files, data theft, unauthorized person, and infiltration are things that should be prevented before the problem occurs. The authenticity of application data that evaluates the performance of company employees is very crucial. This paper will explain how to maintain the company’s big data as a valid standard service to assess employee performance database, based on employee performance of MariaDB or MySQL 5.0.11 with InnoDB storage engine. The authenticity of data is required for decent digital evidence when sabotage occurs. Digital forensic analysis carried out serves to reveal past activities, record time and be able to recover deleted data in the InnoDB storage engine table. A comprehensive examination is carried out by looking at the internal and external aspects of the Relational Database Management System (RDBMS). The result of this research is in the form of forensic tables in the InnoDB engine before and after sabotage occurs.
Multilingual ComplexWord Identification: Convolutional Neural Networks with Morphological and Linguistic Features

Complex Word Identification (CWI) is an essential task in helping Lexical Simplification (LS) identify the difficult words that should be simplified.In this paper, we present an approach to CWI based on Convolutional Neural Networks (CNN) trained on pre-trained word embeddings with morphological and linguistic features.Generally, the majority of works on CWI are either feature-engineered or neural network with word embeddings.Both approaches have advantages and limitations, so here we combine both approaches in order to achieve higher performance and still support multilingualism.Our evaluation has shown that our system achieves quite similar performance as the state-ofthe-art system for English, and it outperforms the state-of-the-art systems for both Spanish and German.
Survey on ARIMA Model Workloads in a DataCenter with respect to Cloud Architecture

Resource administration in a data center is a dire function that affects the operational cost (OPEX) of data center providers and the applications' Service Level Agreement (SLA). Efficient resource management can maximize the resource utilization and guarantee each application's SLA. Accurately predicting each application's workload is a key requirement to provision an efficient resource management. Overestimating or underestimating the application workload results in the resource overprovisioning or under provisioning. In this paper, we apply ARIMA model specific workload application in the data center. ARIMA can automatically analyze the characteristics of the application workload time series and then applies different mathematical models to fit the corresponding application workload time series. The performance of the ARIMA based prediction model is tested through extensive simulations. By selecting the appropriate parameters of the ARIMA model by using the existing methods, the average application workload prediction errors during the day and the month are calculated (i.e., 7.01% and 6.73%, respectively) to provide the accuracy of ARIMA prediction model.
Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification

The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem. Majority of the existing approaches focus on learning a common representation by leveraging both source and target data during training. In this paper, we introduce a two-stage training procedure that leverages weakly supervised datasets for developing simple lift-and-shift-based predictive models without being exposed to the target domain during the training phase. Experimental results show that transfer with weak supervision from a source domain to various target domains provides performance very close to that obtained via supervised training on the target domain itself.
Learning Rich Image Region Representation for Visual Question Answering

We propose to boost VQA by leveraging more powerful feature extractors by improving the representation ability of both visual and text features and the ensemble of models. For visual feature, some detection techniques are used to improve the detector. For text feature, we adopt BERT as the language model and find that it can significantly improve VQA performance. Our solution won the second place in the VQA Challenge 2019.
Investigation of filtering of rain streaks affected video sequences under various quantisation parameter in HEVC encoder using an enhanced V‐BM4D algorithm

This study proposes an enhanced video block matching four-dimensional (V-BM4D) denoising algorithm for highly correlated rain streaks pattern video sequence in high-efficiency video coding (HEVC) encoder. Removal of structural rain streaks from high definition (HD) video sequences are challenging issues in real-time video application. Temporal information of original video sequences is distorted among the successive video frames due to the presence of natural degradation parameters such as fog, smog and rain streaks. Rain streaks are highly correlated structural noise pattern, which affects the nature of video frame compared to other natural degradations. Existing HEVC encoder is employed with in-loop filtering to protect the temporal information of original video sequences from rain streaks pattern. However, in-loop filtering is unable to remove the highly directional oriented rain pattern from video frames. To retain temporal information among the successive video frames, enhanced block matching denoising block is adopted in HEVC coder. The proposed enhanced VBM4D- based in-loop filtering eliminates the various level of rain streaks from the HD video sequences by temporal information prediction in 4D platform. Experimental results demonstrate that the proposed denoising algorithm yields better peak signal to noise ratio value and provides better bit rate saving for various HEVC configuration.
A Cloud-Based Platform for Marine Oil Spill Emergency Response Capability Visualization

With the booming of marine industry, occurrence of maritime oil spills will not only loss of life and property, but also has brought about a long-term and bad influence to ocean environment and ecosystem system. This paper introduces a platform architecture for marine oil spill response capacity visualization, and elaborates the visualization of oil spill emergency elements, emergency resources dynamic interconnection and regional oil spill response capability with marine oil spill accidents. The platform is based on the B/S (browser/server) framework along with the tools of Baidu map API and MapV visualization system to realize the oil spill emergency resources visualization, oil spill emergency capability visualization and oil spill emergency response. The successful running of the platform provides scientific support for the decision-making in handling the oil spill emergency.