Reviewerly/exHarmony huggingface dataset:
The AGV Guidance of Giant Grinding-polishing Coalescence Robot by Machine Vision and Ackermann Steering System Design

For the grinding and polishing process of the blade surface of a huge or super-jumbo wind driven generator with the working area at about 100$\times $5m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , it usually adopts manual grinding operation, followed by manual polishing, with labor-intensity, or employs multi-small and medium-sized robot machine group with single grinding or polishing tool head,leading to two considerable problems as the core technical bottleneck are low accuracy even with unstable quality, and low productivity. Aiming at solving these problems, the current study proposes and designs a grinding- polishing coalesced giant robot to achieve high-precision in efficient grinding and polishing operation. One of the technical difficulties is “to operate in precision meanwhile to move in stability” for the robot. AGV guidance and Ackermann steering system provide an accurate, reliable, and stable position and gesture for the tool head. Based on machine vision and the parameters of the horizontal line of the blade as the basic navigation information, specified reference points are accurately located and possible static objects are literally spotted for auxiliary reference points between the blade and the robot, making the vehicle move from the start, through the intermediate operation, to job completion according to the rational threshold value in the design. In consideration of the high efficiency of the giant robot, the self-guided vehicle is designed with double beams, four bridges and eight wheels as the basic characteristics. Ackermann trapezoidal steering is introduced in the vehicle smartly and in compact. Eight wheels are specially designed with special selection of materials in four groupe. Two groups of wheels are for guiding-positioning-supporting wheels are set at both ends of the vehicle while the middle two groups of wheels are for positioning-supporting. All these tyres are meridian type with reinforced steel wires. On the basis of ensuring strong rigidity, the robot is able to grind and polish continuously and efficiently in motion.
Performance Prediction of Product/Person Using Real Time Twitter Tweets

Over a previous decade people have experienced an exponential boom in the usage of online resources in specific social media and microblogging internet site such as Twitter, Facebook, Instagram and YouTube. Many businesses and agencies has identified these sources as a wealthy mine of marketing information. On such platforms, massive quantities of records are produced (e.g.: 5000 tweets per 2d on twitter), this representing an chance for companies to check their social impact and people opinions towards their products, and even frequent people can additionally discover out what is a performance of a certain product or the overall performance of a particular political personality. In this project, we fetch the given number of tweets from users and classify it as Positive, Negative and Neutral by the usage of supervised machine learning approach. In this method we&#x2019;re analyzing the Polarity and Subjectivity of the tweets and then later we&#x2019;re using NLP to classify the raw records into records body which gets rid of the undesirable words from each of the tweets. Neutral words like &#x2018;as, the, of&#x2019; are eliminated from the tweets. Using NLP, we get better results of the tweets, later we classify the tweets using classifying algorithms like Random Forest Classifier, Decision Tree Classifier, Logistic Regression, and Support Vector Classifier. Later it compares the result of tweets which had been analyzed before processing into NLP. We are also using Data Visualization for phrase frequencies, and for displaying a pie or bar chart of a variety of positive, negative and impartial tweets.
SIMGAT: A Sentiment Analysis Model Based on Graph Attention Mechanism

With the normalization of social media, the popularization of Chinese, how to effectively enable computers to recognize Chinese short-text messages is an important task for network public opinion management and control. Due to the complexity of social media, information between people will have mutual influence, that is, short-text information is interrelated and can be described as a form of graph data. This paper is based on the method of graph neural network for Chinese sentiment analysis, and proposes a method based on improved graph attention mechanism to learn the semantic and structural information between short-text content, and at the same time aggregate short-text information from the field, so as to effectively express the emotional context. The experimental results show that, compared with the existing methods, the graph-based sentiment analysis model is very effective, and the attention mechanism shows a better effect on the sentiment analysis task of short-text.
Research on Cotton Flow Velocity Measurement Based on SIFT Feature Matching

In the process of eliminating foreign fibers by foreign fiber machine, estimating the cotton flow velocity can help to improve the accuracy of foreign fiber machine. Linear CCD cameras at different positions are used to collect cotton flow image synchronously. The overlapping parts of two images at the same time are matched through image processing algorithm. A cotton flow velocity measurement method based on SIFT feature matching algorithm is proposed. The advantage of scale invariant feature transformation (SIFT) is introduced into cotton flow feature extraction, which solves the problem that it is difficult to extract cotton flow features. Finally, the speed is calculated according to the actual distance and pixel difference between the two cameras. An experimental device is designed to verify the effectiveness of the method. The cotton flow velocity measurement method based on SIFT feature matching algorithm measures the dynamic cotton flow velocity, and the average error is 5.29%.
A Graph-Based Knowledge Model for Value-Oriented PLM Implementation

Product Lifecycle Management (PLM) has matured over the last two decades. The software and the market have evolved, resulting in a small number of dominant vendors offering an array of functionality in their software platforms. Moreover, PLM software is becoming available in the cloud as a service (Software-as-a-Service, SaaS). These developments impact the Value Added Resellers (VARs) who deliver software and services to the users of the PLM software. The VARs need to focus more on their added value by providing contextual application knowledge for PLM software in specific business environments. The management of this knowledge is a challenge for VARs because this relies strongly on tacit, empirical knowledge that resides in the heads of their staff. This paper describes a methodology that uses Benefit Dependency Networks to capture, manage, and reuse this knowledge. The methodology is based on the literature and then modified with insights from case studies with a PLM VAR. All relevant contextual elements in an extended Benefit Dependency Network of a PLM implementation are registered in a Graph Model, allowing consultants to find answers within a given industry context and configure a customer-specific implementation plan. The paper also describes a software design built on a Graph Database to use this knowledge in an operational VAR environment and build a foundation for future machine learning in implementation knowledge.
Comparative Analysis of 3D Interactive Modes in Different Object Layouts in Mixed Reality

Mid-air interaction is natural and intuitive in mixed reality environment, which has three main interaction modes, such as point-pinch interaction, head-pinch interaction, and gaze-pinch interaction. Improving interactive experience in mixed reality requires analysis and evaluation on the efficiencies of these interaction modes, especially their performance in different spatial layouts. In this study, the common spatial layout in mixed reality is divided into three basic types: vertical layout, horizontal layout and distributed layout. This study discusses the user experience of the above three interaction modes in the above three layout modes. The results show that gaze and pinch interaction provides users with the most efficient, relaxed and enjoyable choice interaction. Head and pinch interaction has no obvious advantages. The accuracy of point and pinch interaction is the best.
Post-hoc Explainable Reinforcement Learning Using Probabilistic Graphical Models

Reinforcement learning (RL) has recently enjoyed significant success in games, robotics, bioinformatics, etc. Soon, it will not be uncommon to see AI models employing RL agents integrated with various hardware and software solutions. Due to its generality and robustness, RL is applied in several disciplines such as game theory, control theory, multi-agent systems, swarm intelligence, robotics, and NLP. Despite these advances and successes, reinforcement learning faces many challenges for real-world adoption. Some of the major difficulties being, operator’s trust and ability of an agent to explain the actions taken in a human-understandable manner. Traditionally the AI systems are black-box models. With the advent of various legal regulations worldwide, notably the European General Data Protection Regulation (GDPR) [29], it has started becoming mandatory that the AI models be transparent, interpretable, and secure. If an RL agent can effectively and accurately explain the actions carried out by the RL system to the observers/operators, it will be a tangible step towards developing the ART (accountable, reliable and trustworthy) RL agent. This can effectively facilitate the adoption of RL systems in real-world domains. Various explainable AI (XAI) methods have been reported in the literature. However, there is a considerable lacuna in the availability of Explainable RL (XRL) methods. This paper introduces a novel RL algorithm agnostic approach of generating human-understandable explanations using the probabilistic graphical model. This method is based on Probabilistic Graphical Models (PGM) [36]. It is algorithm agnostic in that it is not dependent on any specific RL method and can be integrated with any RL algorithm. We also introduce a PGM model, which is learned along with an agent’s training via classic methods and used for generating explanations at run time. Specific case studies are considered, and results are presented which demonstrate our approach. Our experiments show that the PGM-based approach is highly intuitive and a definitive step towards generating the human understandable explanations. It is a promising approach for discrete as well as continuous real-world systems employing RL.
Design of case statistics analysis based on JavaEE

With the development of information, how to use the key information collected, improve the rate of solving cases, reduce the rate of cases, has become the main problem that local police stations facing now. This paper tries to analyze the time and place of the case, and summarize the law of the time and place of each kind of case, To set up specific patrol routes for police stations, or set up special strike action for certain cases, and provide data support to better protect people&#x2019;s life and property. This article use JavaEE technology to realize statistical analysis of cases , realize the column Chart Display of case data by using JFREECHART technique.
Peak-aware guided filtering for spectrum signal denoising

In the analysis of spectrum signals, it is especially important to identify the peaks of a spectrum signal. Due to various noise, spectrum signals must be smoothed in advance, while preserving peaks information. In this paper, we propose a peak-aware guided filtering (PAGF) for peak-preserving smoothing of a noisy spectrum signal. The content of a guidance signal is taken into account in the filtering process, which can provide smooth structure information and peak position of the input signal. The guidance signal is generated from the input signal by a nonlinear diffusion filtering (NDF) since the output of NDF provides sufficiently smooth structure and accurate peak position for the input signal. The proposed PAGF is validated by experiments on commonly tested signals, in comparison to previous peak-preserving smoothing methods. The results show that the PAGF can better preserve the peak height, width and position of the input spectrum signal while removing the noise.
Critical Datapath Cells for NCL Asynchronous Circuit Area Reduction

Datapath cells are custom designed complimentary metal-oxide-semiconductor (CMOS) gates that can take in both asynchronous and synchronous signals simultaneously while maintaining its Boolean functionality, even if those input signals have different arrival times. They are based on standard CMOS gate techniques with the extra characteristic of having return-to-zero (RTZ) implementation seen in asynchronous NCL gates, which allows the outputs of these datapath gates to not be set before the asynchronous input has arrived and been set. These datapath cells serve as the basis on a new design methodology which will allow designers to convert synchronous netlists into hybrid (synchronous and asynchronous) netlists. This will benefit the netlist with more security from side channel attacks that might expose confidential data otherwise. This conversion methodology will be based on timing critical path data of the design and will preserve all functionality of the original netlist. Our results from simulations of these datapath gates show that they can simultaneously take in both synchronous and asynchronous signals simultaneously and only execute once a value has been set for the asynchronous input. These datapath gates can be used to convert a portion of a synchronous netlist, based on a timing critical path or paths, to a hybrid netlist which gives a design added security benefits from asynchronous components while keeping the area required as minimal as possible.
Blockchain Applications for Improving Track and Trace Process on Pharmaceutical Supply Chain

The pharmaceutical industry has many complex and strict environmental control processes to ensure authentic drug quality control and regulatory compliance throughout the supply chain of medical products. Despite the huge effort made by pharmaceutical firms, the drug counterfeiting is still an ongoing and complicated issue, which has caused important economic losses and impacted patient health. To eradicate this problematic, the pharma companies should lead a deep digital transformation of the pharmaceutical supply chain by including track and trace technology from the first supplier of the Raw & Pack materials to the patients. This paper investigates blockchain-based technology, which is revolutionizing the pharma supply chain activities and leading to a transparent, reliable and authentic ecosystem. To reach this objective, we examined the advantage of introducing blockchain technology on serialization concept and track & trace process. In addition, the paper analyses the integration of the blockchain with the ERP system.
Cytiva Plans $500M Expansion, 1,000 New Hires Worldwide

GEN EdgeVol. 2, No. 1 Drug DiscoveryCytiva Plans $500M Expansion, 1,000 New Hires WorldwideCEO Emmanual Ligner tells GEN: “We'll just have to be ready” as COVID-19 sparks demand for expanded manufacturing capacityAlex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:13 Sep 2020https://doi.org/10.1089/genedge.2.1.42AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 2Issue 1Jan 2020 Information© 2020 by GEN PublishingTo cite this article:Alex Philippidis.Cytiva Plans $500M Expansion, 1,000 New Hires Worldwide.GEN Edge.Jan 2020.238-241.http://doi.org/10.1089/genedge.2.1.42Published in Volume: 2 Issue 1: September 13, 2020PDF download
Book review

It is argued that contraposition is valid for a class of natural language conditionals, if some modifications are allowed to preserve the meaning of the original conditional. In many cases, implicit temporal indices must be considered, making a change in verb tense necessary. A suitable contrapositive for implicative counterfactual conditionals can also usually be found. In some cases, the addition of certain words is necessary to preserve meaning that is present in the original sentence and would be lost or changed in the contrapositive without them. A distinction is made between adding new meaning and adding new words to preserve existing meaning. For concessive conditionals and relevance conditionals, however, no valid contrapositive can be found. They do not belong to the class of contraposable conditionals, which can be independently defined. Difficult cases are also discussed in which the contradictory of the consequent semantically entails the truth of the antecedent. In such cases the content of the antecedent is implicit in the meaning of the consequent. Contraposition becomes possible if what is implicit in the original consequent is made explicit in the contrapositive antecedent.
Model-Driven Run-Time Dependency Graphs for Component-Based Robotic Software

For the observance of a system’s non-functional properties, one must first parameterise them as elemental or compound objects. Dependency objects are defined as entities that encapsulate a distinct system characteristic that is relevant to and is altered by components and connections that compose the system. Software components and network connections form nodes of a graph, edges of which represent a change of one or more system-wide properties. Dependency graphs are networks of dependencies that emerge from the flow of system-level characteristics, controlled by evidence and business logic. Component developers and network experts provide functional units to describe how the value of a particular dependency object is transferred across computational or connection nodes respectively. Run-time dependency graph models for independently developed software components and network connections are integrated, built and explored while building or validating a system. These run-time models are used to observe networks of dependencies that emerge from the flow of system-level features graphically and programmatically. The design and engineering anatomy of such a toolchain is presented in this paper.
Dynamic Speed Estimation of Moving Objects from Camera Data

Recent advances in computer vision techniques have led to increasing interest in the use of vision as the only mechanism to estimate the vehicle speed in certain applications. The challenge of making accurate estimations of distances and speeds arises from the discrete nature of video sensors that project the 3D world into a 2D discrete plane. This work will study the concept of estimating the speed of an object by only using the camera data. Rather than using a separate mechanism dependent on camera specifications, we consider a convolutional neural network fed recurrent neural network to estimate the speed at each time step. The video data can be fed to an object detection algorithm to extract the features and patterns in the videos. The network architecture is designed to make use of pre-trained object detection models to extract features from camera video feed and embed them into a data cluster. This cluster can represent the spatiotemporal information of the objects in the video frames. A recurrent neural network is then used to learn the association of the motion speed values with respect to the changes in the features within the cluster. Based on this structure, both the object detector and the recurrent neural network can be trained end-to-end to detect and estimate the speed of objects in videos. This network architecture would not require additional sensors for calibration, while providing a cost-effective solution for mobile robotic applications such as autonomous ground vehicles or drones. The proposed methodology, network architecture and its components will be discussed in detail. This paper also presents a video data case study and preliminary results to demonstrate the development and use of the speed estimation network. Some future work is planned to improve the algorithm accuracy and computing efficiency of the proposed architecture.
Design and research of non-contact Optical density measuring device

Accurate measurement of optical density plays an important role in the fields of biology, medicine, material, image analysis, printing, painting and photography. It is an important link to measure and guarantee the printing quality. Especially when the color difference is less than 1.0, it is difficult for the human eye to distinguish the difference between the two, only the instrument can measure their color difference and optical density value to distinguish. Aiming at the problem that the reflective gray-scale test card used in the photographic industry for evaluating image quality cannot be used for traditional contact measurement, the optical density measurement theory and the characteristics of non-contact measurement are studied, a simple and feasible test scheme is proposed, and a parallel optical tube and a 45/0 automatic control corner system are designed. A high precision, non-contact optical density measurement system is established, used for measuring the optical density of materials with small size, bending and easy to leave scratches. Based on the measurement experiments and experimental data analysis of the TE-108 reflective gray scale test card produced by German company Image Engineering, it shows that the measuring device has obvious advantages in measuring accuracy, reliability, measuring efficiency and measuring cost.
Co-Drink: Exploring Social Support Water Bottles to Increase the Hydration Status of Individuals with Intimate Relationship

Due to busy work routines in the modern society, it is common that people neglect to drink water. Social influences, such as interactions between friends and lovers, appears to be crucial to foster healthy behaviors. In this study, we investigate leveraging social support mechanisms in the design of persuasive technology to increase the frequency of daily fluid intakes. We firstly carried out an auto-ethnography as well as a probe-based co-design study to explore dedicated social mechanisms for promoting healthy hydration habits. This led to the conceptualization of Co-Drink that focuses on technology-enabled social interactions between individuals with intimate relationships to stimulate their water intake behaviors. Co-Drink was implemented as a pair of smart water bottles with ambient light. It also associated with an app to facilitate a coaching mode and a co-learning mode for persuading health behavior change. The prototypes of Co-Drink were evaluated in a Wizard-of-Oz study with three dyads of college students. Results showed its benefits for hydration health promotion and applicability for daily life use, due to the embedded social mechanisms and the ambient displays integrated with the water bottle. Based on these qualitative findings, we discuss implications for the future development of persuasive technologies for improving water intake behaviors.
An efficient self-certified scheme based on discrete logarithms and factoring for MANETs

The self-certified scheme has some unique merits comparing with certificate-based and identity-based scheme. This scheme proposes a self-certified authentication scheme based on discrete logarithm and factoring which employ Rabin signature system to generate user public keys for mobile ad hoc networks(MANETs), and which realizes one-way authentication between nodes. The analysis shows that the online interactive data of the protocol can effectively prevent the leakage of verification information, and has certain security and effectiveness.