Reviewerly/exHarmony huggingface dataset:
A Laser-Guided Solution to Manipulate Mobile Robot Arm Terminals Within a Large Workspace

Globally accurate manipulation and localization of the terminal of a mobile robot arm within a large workspace has always been a bottleneck problem. In this article, a planar-laser-guided solution is proposed for this issue. The core of this solution is using three movable planar lasers to designate a unique reference point with orientation information within the workspace, and designing a sensor module mounted on the terminal of the robot arm with photoelectric position sensitive devices (PSDs) to measure the relative pose between the terminal and the reference point. Thus, the terminal of the robot can be guided by this relative pose to the target in a given orientation. According to this concept, a parameterized model of the moveable planar lasers and the poses of the PSDs, as well as an expression of their geometric relationship, were derived. The manipulation strategy of the robot arm was further investigated through the model, and an error compensation method to improve the manipulation accuracy was proposed. Finally, a reduced-scale prototype system, based on this solution, was built, on which tracking and positioning experiments of the robot arm were carried out to verify the feasibility of the solution. It has been shown that this laser-guided solution is not sensitive to the blockage (occlusion) of several laser transmitters, which could be an advantage for its application in future autonomous settings.
RETRACTED: Research on virtual simulation of basketball technology 3D animation based on FPGA and motion capture system

A stable structure and group information for measuring the driving dynamic binding character information processing, network control and operation when ready. Created with Unity, Tensol Stream and PyTorch. This method can be done bipedal and quadruped movement in the past occupied the complex and rational activity. The study will help researchers select the appropriate exercise programs and motion capture framework to test their gadgets. The rose to select and added to the open network arena, improve audit in their investigation. Framework has instructed sub conventional and highly accurate three-dimensional movement. Article screening to determine the motion capture framework research study of distributed source in one hundred academic studies used in the report of human activity. The proposed use of support to Field Programmable Gate array (FPGA) virtual computing innovation replay basketball to move the ball from the foundation and other projects, such as the distinction between players and referees. The two-dimensional position of the ball results are calibrated to identify the real position of the ball. Reality from the position of the ball, shoot the scene from every angle camera, and further ground plane. True development continue to see the ball from the top. Tests showed that the proposed calculation obstacles can be handled quickly and less difficult ball, the ball slightly different size and shape.
Experimental parameters, combined dynamics, and nonlinearity of a magnonic-opto-electronic oscillator (MOEO)

We report the construction and characterization of a comprehensive magnonic-opto-electronic oscillator (MOEO) system based on 1550-nm photonics and yttirum iron garnet (YIG) magnonics. The system exhibits a rich and synergistic parameter space because of the ability to control individual photonic, electronic, and magnonic components. Taking advantage of the spin wave dispersion of YIG, the frequency self-generation as well as the related nonlinear processes become sensitive to the external magnetic field. Besides being known as a narrowband filter and a delay element, the YIG delayline possesses spin wave modes that can be controlled to mix with the optoelectronic modes to generate higher-order harmonic beating modes. With the high sensitivity and external tunability, the MOEO system may find usefulness in sensing applications in magnetism and spintronics beyond optoelectronics and photonics.
SplitServe

Due to their lower startup latencies and finer-grain pricing than virtual machines (VMs), Amazon Lambdas and other cloud functions (CFs) have been identified as ideal candidates for handling unexpected spikes in simple, stateless workloads. However, it is not immediately clear if CFs would be similarly effective in autoscaling complex workloads involving significant state transfer across distributed application components. We have found that, through careful design, currently available CFs can indeed be useful even for complex workloads. To demonstrate this, we design and implement SplitServe, an enhancement of Apache Spark. If not enough executors on existing VMs are available for a newly arriving latency-sensitive job, SplitServe is able to use CFs to quickly bridge this shortfall in VMs, so avoiding the startup latencies of newly requested VMs. If desirable in terms of performance or cost, when newly requested VMs, or executors on existing VMs, do become available, SplitServe is able to move ongoing work from CFs to them. Our experimental evaluation of SplitServe using four different workloads (either on a mixture of VM-based executors and CFs or just CFs) shows that it improves execution time by up to (a) 55% for workloads with small to modest amount of shuffling, and (b) 31% in workloads with large amounts of shuffling, when compared to only VM-based autoscaling.
Interacting T-S fuzzy particle filter algorithm for transfer probability matrix of adaptive online estimation model

For the problem of inaccurate or difficult to obtain statistical characteristics of non-Gaussian noise, an interacting T-S fuzzy modeling algorithm is proposed to incorporate spatial-temporal information into particle filtering. In the proposed method, feature information is characterized by multiple semantic fuzzy sets, and the model transition probabilities are estimated by using the fuzzy set transition probabilities, which can be derived by the closeness degrees between the fuzzy sets. Furthermore, the correntropy can capture the statistical information to solve the non-Gaussian noise, thus a kernel fuzzy C-regression means (FCRM) based on correntropy and spatial-temporal information is proposed to adaptively identify the premise parameters of T-S fuzzy model, and a modified strong tracking method is used to estimate the consequence parameters. By using the proposed interacting T-S fuzzy model, an efficient importance density function is constructed for the particle filtering algorithm. Finally, the simulation results show that the tracking performance of the proposed algorithm is effective in processing non-Gaussian noise.
RETRACTED: Auxiliary research on difficult aerobics exercise training based on fpga and movement recognition technology

Based on case instruction, traditional training systems are based on past rivalries’ case studies and preparing to give strength preparing to Aerobics, uncommon games. The preparation results can't be assessed shrewdly and precisely, and the exhibition of investigation and dynamic examination is poor. To take care of this issue, the reason for unique high impact exercise estimates dependent on human-made consciousness is to understand a preparation system that focuses on Aerobics, the strength and the quality of particular sports. It can be achieved by researching Convolutional Neural Networks (CNN) model systems, such as optimizations and other intelligent features such as an intelligent Convolutional Neural Network (CNN) decision making. The design system architecture framework includes sensors, receivers, databases, and analysis modules. The system's core chip is the human-computer interaction FPGA control module to provide comprehensive training in impressive aerobics intensity quality. The information-gathering module is used for gathering information about gauges, sports and strength training as a language. Trainee information management and training result statistics and query execution through the information management module. Some system software includes a software configuration diagram of the system and booting and landing the system. Through the work cycle of the investigation module, the power of specific vigorous exercise sports is dissected. Test results show that the planned framework can give ongoing, standard strength preparing for Aerobics, explicit activities and improve preparing productivity.
Sagnac-type entangled photon source using only conventional polarization optics

Abstract We designed and implemented a novel combination of a Sagnac-interferometer with a Mach–Zehnder interferometer for a source of polarization-entangled photons. The new versatile configuration does not require multi-wavelength polarization optics, yet it performs with a good polarization quality and phase-stability over a wide wavelength range. We demonstrate the interferometer using only standard commercial optics to experimentally realize the pulsed generation of polarization-entangled photon-pairs at wavelengths of 764 nm and 1221 nm via type-I spontaneous four-wave mixing in a polarization-maintaining fiber. Polarization entanglement was verified by a polarization-correlation measurement with a visibility of 95.5% from raw coincidence counts and the violation of the Clauser–Horne–Shimony–Holt (CHSH) inequality with S = 2.70 ± 0.04. The long-term phase-stability was characterized by an Allan deviation of 8° over an integration time of about 1 h with no active phase-stabilization.
Superpixel Segmentation Based on Spatially Constrained Subspace Clustering

Superpixel segmentation aims at dividing the input image into some representative regions containing pixels with similar and consistent intrinsic properties, without any prior knowledge about the shape and size of each superpixel. In this article, to alleviate the limitation of superpixel segmentation applied in practical industrial tasks that detailed boundaries are difficult to be kept, we regard each representative region with independent semantic information as a subspace, and correspondingly formulate superpixel segmentation as a subspace clustering problem to preserve more detailed content boundaries. We show that a simple integration of superpixel segmentation with the conventional subspace clustering does not effectively work due to the spatial correlation of the pixels within a superpixel, which may lead to boundary confusion and segmentation error when the correlation is ignored. Consequently, we devise a spatial regularization and propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel and generate the content-aware superpixels with more detailed boundaries. Finally, the proposed model is solved by an efficient alternating direction method of multipliers solver. Experiments on different standard datasets demonstrate that the proposed method achieves superior performance both quantitatively and qualitatively compared with some state-of-the-art methods.
Reference Knowledgeable Network for Machine Reading Comprehension

Multi-choice Machine Reading Comprehension (MRC) as a challenge requires models to select the most appropriate answer from a set of candidates with a given passage and question. Most of the existing researches focus on the modeling of specific tasks or complex networks, without explicitly referring to relevant and credible external knowledge sources, which are supposed to greatly make up for the deficiency of the given passage. Thus we propose a novel reference-based knowledge enhancement model called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reference</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knowledgeable</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Network (RekNet)</i> , which simulates human reading strategies to refine critical information from the passage and quote explicit knowledge in necessity. In detail, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RekNet</i> refines fine-grained critical information and defines it as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reference Span</i> , then quotes explicit knowledge quadruples by the co-occurrence information of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reference Span</i> and candidates. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RekNet</i> is evaluated on three multi-choice MRC benchmarks: RACE, DREAM and Cosmos QA, obtaining consistent and remarkable performance improvement with observable statistical significance level over strong baselines. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Yilin1111/RekNet</uri> .
Elective courses for training the mathematics teachers to realise STEM approach

Abstract The article presents the results of research in the field of university training of mathematics teachers aimed at realising STEM education at school and other educational organisations. The authors identified 1) insufficient readiness of school teachers to implement STEM training, and 2) lack of professional mathematics teachers having special qualification in this area. Based on Russian and foreign experience, the authors propose to use the potential of the elective courses for purposeful preparation of future mathematics teachers to implement STEM training. The approach to designing the content and technological components of such a course rests on the principles of practical orientation, multidisciplinarity, professional orientation and the application of a regional context. The course consists of theoretical and practical modules, which make it possible to integrate the potential of all STEM components in the professional training of teachers and to master the basics of STEM implementation.
Simulation of English classroom effectiveness based on human-computer interaction and facial identification

A variety of factors affect English classroom teaching, which prevents teachers from effectively grasping students’ learning status and learning situation. In particular, classroom management is more difficult during online teaching. In order to improve the effectiveness of English classroom teaching, based on the human-computer interaction algorithm and facial identification algorithm, this paper effectively recognizes the human-computer interaction process and classroom learning status of students in online teaching, and eliminates the image background according to the actual teaching needs. Moreover, by extracting and fusing the time sequence information and spatial information of the motion in the video, a spatiotemporal feature image capable of expressing a dynamic typical feature is obtained. In addition, this paper uses system algorithms to judge the status and feed back the identification results to the teacher’s teaching terminal equipment, which is convenient for timely teaching adjustment. Finally, this paper analyzes the effectiveness of the algorithm through simulation experiments. The research results show that the algorithm constructed in this paper has good performance.
Personalized fashion recommender system with image based neural networks

With an increase in the standard of living, peoples' attention gradually moved towards fashion that is concerned to be a popular aesthetic expression. Humans are inevitably drawn towards something that is visually more attractive. This tendency of humans has led to development of fashion industry over the course of time. However, given too many options of garments on the e-commerce websites, has presented new challenges to the customers in identifying their correct outfit. Thus, in this paper, we proposed a personalized Fashion Recommender system that generates recommendations for the user based on an input given. Unlike the conventional systems that rely on user's previous purchases and history, this project aims at using an image of a product given as input by the user to generate recommendations since many-a-time people see something that they are interested in and tend to look for products that are similar to that. We use neural networks to process the images from DeepFashion dataset and a nearest neighbour backed recommender to generate the final recommendations.
ABACUS

DAG-based blockchain systems have been deployed to enable trustworthy peer-to-peer transactions for IoT devices. Unique address checking, as a key part of transaction generation for privacy and security protection in DAG-based blockchain systems, incurs big latency overhead and degrades system throughput.
Solution of chemical dynamic optimization systems using novel differential gradient evolution algorithm

Abstract Optimization for all disciplines is essential and relevant. Optimization has played a vital role in industrial reactors’ design and operation, separation processes, heat exchangers, and complete plants in Chemical Engineering. In this paper, a novel hybrid meta-heuristic optimization algorithm which is based on Differential Evolution <?CDATA $(DE),$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>D</mml:mi> <mml:mi>E</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>,</mml:mo> </mml:math> Gradient Evolution <?CDATA $(GE),$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> </mml:math> and Jumping Technique (+) named Differential Gradient Evolution Plus (DGE+) is presented. The main concept of this hybrid algorithm is to enhance its exploration and exploitation ability. The proposed algorithm hybridizes the above-mentioned algorithms with the help of an improvised dynamic probability distribution, additionally provides a new shake off method to avoid premature convergence towards local minima. The performance of <?CDATA $DGE+$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> <mml:mo>+</mml:mo> </mml:math> is investigated in thirteen benchmark unconstraint functions, and the results are compared to the other state-of-the-art meta-heuristics. The comparison shows that the proposed algorithm can outperform the other state-of-the-art meta-heuristics in almost all benchmark functions. To evaluate the precision and robustness of the <?CDATA $DGE+$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> <mml:mo>+</mml:mo> </mml:math> it has also been applied to complex chemical dynamic optimization systems such as optimization of a multimodal continuous stirred tank reactor, Lee-Ramirez bioreactor, Six-plate gas absorption tower, and optimal operation of alkylation unit, the results of comparison revealed that the proposed algorithm can provide very compact, competitive and promising performance overall complex non-linear chemical design problems.
Accurate calibration of standard plenoptic cameras using corner features from raw images

Light field cameras capture spatial and angular information simultaneously. A scene point in the 3D space appears many times on the raw image, bringing challenges to light field camera calibration. This paper proposes a novel calibration method for standard plenoptic cameras by using corner features from raw images. We select appropriate micro-lens images on raw images and detect corner features on them. During calibration, we first build the relationship of corner features and points in object space by using a few intrinsic parameters and then perform a linear calculation of these parameters, which are further refined via a non-linear optimization. Experiments on Lytro and Lytro Illum cameras demonstrate that the accuracy and efficiency of the proposed method are superior to the state-of-the-art methods based on features of raw images.
The Self-Surveillance Failures of Wearable Communication

By discursively analyzing blogs and popular press articles written by people who discontinued using Fitbit, this article reveals the incongruities between the tracking of our bodily information, the communication of that bodily information via wearables, and the promises of changing of users’ attitudes and behaviors with how wearables are actually used in practice. The discourses of discontinuance in this analysis also reveal how former Fitbit engenders feelings of disconnection and may affect users in detrimental ways. As a result, and despite the predominant framing and conceptualization of wearables as “motivating,” “empowering,” and “useful” (self)surveillance tools, I argue that Fitbit is an example of a failure both of self-surveillance and of wearable communication for helping users achieve their health and fitness goals. Finally, I argue that we need to start thinking about wearable communication like other forms of communication that are inherently inconsistent and contradictory, and that can be accepted, negotiated, or rejected by users. Instead of focusing on the disciplinary or controlling potentials of wearables as a form of self-surveillance, this paper considers the resistance and negotiations inherent in the (dis)use of wearables, and demonstrates the necessity of exploring both wearables and surveillance itself in relation to fundamental understandings of communication.
Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms

Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.
Incorporating RDA Outputs in the Design of a European Research Infrastructure for Natural Science Collections

To support future research based on natural sciences collection data, DiSSCo (Distributed System of Scientific Collections) – the European Research Infrastructure for Natural Science Collections – adopts Digital Object Architecture as the basis for its planned data infrastructure. Using the outputs of one Research Data Alliance (RDA) interest group (IG) and five working groups (WGs) we show how RDA recommendations and supporting documents have been applied to the various stages of the DiSSCo data lifecycle.