Reviewerly/exHarmony huggingface dataset:
Key Technologies of Energy Saving and Emission Reduction for Light Electric RTG

The main handling equipment of container yard is general rubber tired gantry crane. However, it is not suitable for small and middle container yard because of high cost, maintain complex, high energy consumption. Based on general RTG, four-drum combined control technology with container hoisting and trolley moving is used in light electric RTG. Hoisting and trolley moving mechanism drive devices are combined to one device which is arranged in the bottom beam of crane. In the premise of ensuring the overall performance, the height of center of gravity is greatly reduced, stability of crane is improved. Accordingly, the parameters of crane are optimized, the weight of the structure is reduced. Moreover, frequency control and energy recovery system is developed for the light electric RTG. So, light electric RTG has the characteristics of light weight, low investment, low energy consumption, zero emissions, cost-effective, energy saving and environmental protection. It is suitable for small and middle container terminal.
Web Education Development Based on Social Network Analysis: An Initial Proposal

E-learners are confronted with a large amount of learning content during E-learning. Recommendation systems can help the learner to find suitable content to achieve the learning objective in an efficient and systematical way. Relationships between users of social networks are often determined by their interests. Analyzing social networks and finding similar users who have similar interests through their social networks helps to understand a user’s interests and recommend adequate content to these needs. In this paper we propose a personalized recommendation system for learners in online courses based on the relationships between users of social networks, using social network analysis and graph theory.
Research on evaluation System of blockchain consensus algorithm

With the rapid development of Bitcoin, blockchain has also received more and more attention, and consensus algorithm is the key to the development of blockchain technology, which directly affects the security, scalability, and transaction processing speed of blockchain. This paper analyzes the popular consensus algorithm and its evolution. The representative consensus algorithm is analyzed in detail. The evaluation system of consensus algorithm is given from four aspects of security, decentralization, consistency, and scalability, hoping to provide a reference for the development and innovation of consensus algorithm.
An Embedded Implementation of Improved SSD with RTMaps and NXP Bluebox2.0 for Autonomous Platforms

Multi target object detection is predominantly employed in field of computer vision. Computer vision and machine vision goes hand in hand to develop efficient applications such as autonomous driving systems and intelligent robotic systems in industries. One of the widely used neural network architectures for object detection is Single Shot MultiBox Detector (SSD). Numerous approaches have been carried out in this field by introducing complex convolutional operations to enhance performance. Still it remains an onerous challenge to develop an efficient neural network model can detect small objects accurately. To overcome this challenge, this research proposes an improved version of SSD enhancing the performance of auxiliary convolutional layers for small object detection. Dice coefficient and cross entropy loss approach was adapted to calculate Intersection Over Union (IoU) threshold. The model was trained on PASCAL VOC 2007 and 2012 datasets and testing was carried on PASCAL VOC 2007. Mean average precision (mAP) of 80.7%, 3.2% greater than original SSD at an input size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$300 \times 300$</tex> . The architecture model was deployed in Intempora RTMaps embedded with NXP Bluebox 2.0 to observe the model performance in real-time.
Robotics Project Using ROS

The previous chapter discussed programming using ROS client libraries such as rospy and roscpp. In this chapter, you see how to apply those things to a real robot. You see how to make a low-cost, differential drive robot that is compatible with ROS. You also see how to perform dead reckoning in the robot using ROS. By doing this project, you get a clearer understanding of ROS concepts and where to apply them.
Situation Awareness Data Fusion Method Based on Library Events

Microelectronic technology and communication technology are developed in deep manner; the computing mode has been transferred from traditional computer-centered to human centered pervasive.So, the concept of Internet of things (IoT) is gradually put forward, which allows people to access information about their surroundings on demand through different terminals.The library is the major public space for human to read and learn.How to provide a more comfortable library environment to better meet people's learning requirements is a place where the Internet of things plays its role.The purpose of this paper is to solve the difference between the data fusion of library environment and the data fusion of other environments by the method of data fusion oriented to library.This paper presents a general technical framework of situational awareness for smart library system which includes a data fusion middleware.It can process data and inform the upper module of the changed library environment after deploying the smart library system in a library, including data collection and processing, how to judge whether events are triggered, how the system reacts, and the acquisition and update of user preferences.This paper presents a situational awareness recommendation method based on an effective data fusion model and algorithm for library after conducting experimental in service of library, which give more accurate of book recommendation than traditional method and good learning service environment of library for readers.
Global solar radiation time series forecasting using different architectures of the multilayer perceptron model

Abstract In this work, the multilayer perceptron model was used to forecast the time series of global solar radiation for a near future about a week. Different architectures of this model were built through varying its different hyperparameters such as optimizers, activation functions, number of neurons and neuron dropout in which their performance was evaluated using error metrics. It was found that the architectures (60, SGD, Sigmoid), (10, Adam, Relu) and (60, SGD, Sigmoid) presented an R 2 around 0.877, 0.873 and 0.872, respectively. The architecture with neuron dropout (150, SGD, Sigmoid, 0.2) presented a higher performance among all the architectures evaluated and its R 2 value was 0.884. Architectures with higher performance are used to predict future values of solar radiation.
Improving Real-Time Energy-Efficient Trajectory Planning Via Machine Learning

This research demonstrates benefits from leveraging simple machine learning algorithms to generate a computationally light, suitably tailored heuristic function to enable mobile robots to make faster, more accurate decisions in navigation tasks. This can be achieved by collecting data from simulations and training top-performing algorithms like Light GBM, MLP, Ridge Regressor, etc., into a heuristic learning model. Our approach is anticipated to replace existing naive heuristic functions by reducing computation time to solution and targeting exploration.
Real-time dynamics in diluted quantum networks

We introduce an approach to characterize the dynamics of disordered quantum networks. Each quantum element (i.e., each node) of the network experiences the other nodes as an effective environment that can be self-consistently represented by a Feynman-Vernon influence functional. For networks having the topology of locally treelike graphs, these Feynman-Vernon (FV) functionals can be determined by a new version of the cavity or belief propagation (BP) method. Here, we find the fixed point solution of this version of BP for a network of uniform quantum harmonic oscillators. Then, we estimate the effects of the disorder in these networks within the replica symmetry ansatz. We show that over a large time interval, at small disorder, the real part of the FV functional induces decoherence and classicality while at sufficiently large disorder the Feynman-Vernon functional tends to zero and the coherence survives, signaling in a time setting, the onset of an Anderson's transition.
Research on Trusted Digital Capability Architecture of Industrial Supply Chain Based on Blockchain Infrastructure

The application of blockchain will promote the data value mining, the automatic processes realization and the construction of cooperative relationship in supply chain. To construct a dynamic supply network, centralized infrastructure, distributed data asset autonomy and flexible application scenario empowerment are integrated and trusted digital capability architecture based on blockchain infrastructure are designed. This structure consists of a cross-organizational shared blockchain node resource network, a common capability platform in line with data governance process and an application solution covering end-to-end supply chain business. The trusted data governance model is proposed to show how to break the information island between heterogeneous systems and blockchain applications. Then the information flow forms a closed-loop with link of data catalog chain and establishes a process coupling with traditional information system. The application will further provide an agile development technology architecture and flexible solution in information sharing, business connection, logistics collaboration and financial services innovation for entire supply chain management and operation.
Research on Component Retrieval and Matching Methods

Aiming at how to quickly retrieve the target component from the huge component library, this paper proposes a component retrieval method based on facet classification, which uses component keyword set to facet description of the component and constructs the component vectorized model. The idea of layering is adopted to divide the components in detail. The K-means clustering algorithm based on the maximum and minimum distances is used to cluster the components, so that the problem of component matching is transformed into the calculation of the cosine similarity of component vectors. This method can narrow down the search scope and effectively improve the search efficiency.
Text Spotting on Metal Part Identifier

In an industrial scene, the text had a more disturbing background and unique font style with depression, hump and other phenomena. And this type of text accounts for a very low proportion of the public datasets which makes the existing models unable to perform well in the detection and recognition of industrial scene text. In view of the small industrial scene text dataset, the dataset was expanded through data augmentation operations to enhance the generalization ability of the model. Based on the Point Gathering Network, the trained model was transferred to the augmented metal part word data set through the Fine-tune method for retraining to improve the model performance. The experimental results show that the F-score of detection is 81.3% and the accuracy of recognition is 86.5%, which basically realizes the detection and recognition of metal parts Identifier.
Learning More Complex Actions with Deep Reinforcement Learning

How to learn more complex actions based on the predefined atomic actions is a significant problem for improving the intelligence of machines. In this paper, we present a novel approach called action space expanding Q-learning (ASE-Q) to the learning of more complex actions. We first introduce two types of formation of the complex actions; then, we design an action combination network to dynamically learn the complex actions a Q-network to evaluate action-value of the complex actions. Learning more complex actions can speed up the process that an agent accomplishes a task and improve its capacity in learning new actions.
Ontology of Card Sleights

We present a machine-readable movement writing for sleightof-hand moves with cards - a "Labanotation of card magic." This scheme of movement writing contains 440 categories of motion, and appears to taxonomize all card sleights that have appeared in over 1500 publications. The movement writing is axiomatized in SROIQ(D) Description Logic, and collected formally as an Ontology of Card Sleights, a computational ontology that extends the Basic Formal Ontology and the Information Artifact Ontology. The Ontology of Card Sleights is implemented in OWL DL, a Description Logic fragment of the Web Ontology Language. While ontologies have historically been used to classify at a less granular level, the algorithmic nature of card tricks allows us to transcribe a performer's actions step by step. We conclude by discussing design criteria we have used to ensure the ontology can be accessed and modified with a simple click-and-drag interface. This may allow database searches and performance transcriptions by users with card magic knowledge, but no ontology background.
Review of VQA : Datasets and Approaches

Visual Question Answering (VQA) is a fairly recent problem that has amassed much attention from two highly significant research fields by integrating natural language processing and computer vision techniques. It is a multimodal task in which an algorithm must answer questions about images. To begin, the paper will evaluate and analyze the various existing datasets for VQA tasks and explore their limitations and specialties. The paper then reviews various papers for novel approaches to the VQA problem. Finally, it explores the field's future scope and prospective possibilities.
Challenges of Robot Assisted Teaching in Education Domain

Nowadays, robot toys for kids are used for educational purposes as well as just having fun. We all know that kids like remote control toys more than the simple toys. It can be useful if we will try to make a mixture of their interest and their needs. Here need refers to their education. If we will do something that make education more interesting to them, then they will enjoy education and it will no more be a burden for them. So, for that we can use educational robots. Educational robots can help make the monotonous digital learning process tangible for kids and can significantly increase their interest and productivity. In addition, playing with educational robots can be a great way for kids to hone their talents and skills. It will be easy for students as well as for parents and teachers if the child will learn the things by his/her own interest. This paper examines how educational robots are gaining attraction in the education industry, as well as the benefits and drawbacks that using this intelligent technology will bring.
A Hybrid Design of Linkable Ring Signature Scheme with Stealth Addresses

Blockchain is a transformational technology which affects finance, Internet, and politics. However, many privacy protection problems for blockchain are waiting to be solved. In this study, we propose a novel linkable ring signature scheme with stealth addresses, which enables the payer and payee of the transaction to be anonymous and unlinkable in the cryptocurrency. The scheme is combined with an elliptic curve discrete logarithm (ECD logarithm)-based key encapsulation mechanism (KEM) stage and a lattice-based signature stage. The master public key and master secret key are much smaller compared with the previous scheme. Complete secure proof of the scheme is also presented in this study.
Integration of Components of the Ontological Knowledge Space to Assess the Impact of Energy on Quality of Life of the Population

This paper examines an ontological approach to integrating knowledge to support interdisciplinary studies in energy and ecology in terms of quality of life assessment. These studies involve the integration of environmental and social components. The environmental component is determined by natural and climatic conditions and the state of elements of the natural environment of a particular territory. The social component implies meeting the electricity and heat demand of the population, which is necessary for comfortable living. Quality-of-life metrics are considered as a way to assess the positive and negative impacts of the energy facilities and population on the natural environment to compare these impacts. We present ontologies detailing basic concepts of the subject area of research into the impact of energy facilities and quality of life and reflecting their integration into a single ontological space of knowledge. The ontological approach provides a visual representation and integration of knowledge from different subject areas.
Application Analysis of Face Recognition Technology Based on Computer Vision

With the development of society, all parties are facing increasingly urgent requirements for fast and effective automatic identity verification. Since biological characteristics are intrinsic properties of humans, they have strong stability and individual differences, so they are an ideal basis for identity verification. Compared with biometrics such as fingerprints and iris, face recognition is more direct, friendly, and convenient to be accepted by users. Therefore, face recognition has become a current research hotspot in the field of pattern recognition and artificial intelligence. As an important part of human-computer interaction, the research of face detection and recognition technology has great theoretical significance and application value. In this paper, the face detection method based on skin color information and the face recognition algorithm based on the embedded hidden Markov model are deeply studied, and the development of the development of the face detection and recognition system, performance analysis and specific applications are done. Introduced. In this paper, the detection and positioning of the face and the extraction of facial features are carried out on the frontal color image: the feature vector extraction of the detected face region using the two-dimensional discrete cosine transform and the method based on the embedded hidden Markov model are proposed. Face recognition method. Experiments show that the method of using the embedded hidden Markov model based on two-dimensional discrete cosine transform for face recognition can make better use of the two-dimensional statistical characteristics of face images, and has high recognition efficiency and very high recognition efficiency. Good robustness; researched on the recognition of hand-drawn portraits of human faces, and proposed a novel algorithm for converting photo images into portraits; applied the face detection and recognition system to specific practices, and established An intelligent search system for the source of unknown corpses. This system provides strong technical support and guarantee for the public security department to detect cases, and has important research and development significance.
Base Field Extension of AG Codes for Decoding

Previous algorithms for decoding AG codes up to the designed distance all assumed existence of an extra rational place on the base algebraic curve. The place is used to solve the decoding problem by linear algebra over the base field of the curve. The rationality of the place is essential, and therefore AG codes supported by all rational places on the curve are excluded from the domain of applicability of the decoding algorithms. This paper presents a decoding algorithm for those AG codes using an extra place of higher degree. Hence finally all AG codes, as Goppa defined 40 years ago, are equipped with a fast decoding algorithm.
A Dimension Hybrid Framework for Multimodal Medical Image Segmentation

Lesion detection or segmentation on 3D medical image (e.g., MRI, CT-PET or hybrid modal) is a challenging task which aims to automatically mark the abnormal region on given input image. The methods to tackle the problem often construct a U-shape autoencoders to map the input to a compressed vector in latent space and predict the final probability map. However, approaches for 3D multimodal image segmentation are still not fully explored and localization accuracy can be improved by designing proper structure based on prior. In this paper, we propose a supervised contrastive approach for 3D multi-modality medical image segmentation. A hybrid 3D-2D multi-modality fusion model is designed based on the input image prior. The 3D segmentation problem is converted to a sequence prediction task by 2D decoder with contrastive modeling. The combined framework (HDC, Hybrid Dimension Contrastive) achieves comparable or even greater performance to 3D counterpart while memory efficient. We conduct extensive experiments on two multimodal datasets of brain image BraTS and our epilepsy MRI-PET. The validation results show the effectiveness of our framework.
Research Progress of Fine-grained Visual Classification: Basic Framework, Challenges, and Future Development

With the progress of multimedia technology, fine-grained visual classification (FGVC) has gradually attracted extensive attention in the field of computer vision. Thanks to the success of deep neural networks, both the accuracy and speed have achieved an unprecedented breakthrough. Hundreds of fine-grained classification techniques have been developed from the early, fully supervised methods to the current weakly ones. To this end, in this article, we provide a comprehensive survey of the recent achievements of FGVC. Specifically, we describe the formulation and setting of the FGVC, including the background, basic framework, and challenges. Then, we introduce the widely used datasets and compare the performance of different models. Lastly, we discuss the potential future directions of FGVC.
Récit Music: Musical community in the information age

This article delineates a metaphysical concept, musical community , and considers its implications for material practices in the art world. It describes ways in which two theoretically distant categories of art music, namely, electroacoustic music and the concert-hall tradition, may converge given the emergent societal and technological shifts in the information age. The concepts of ‘networks’ and ‘reciprocity’, as developed by sociologists Manuel Castells and Marcel Mauss, respectively, constitute the theoretical lens for this analysis, which sees a number of musicological notions, including ‘musical society’ and ‘acousmatics’ reconsidered philosophically. Emphasis is placed, in lieu of the insular methodologies associated with the akousmatikoi , on broader Pythagorean society, its mythical links with present-day musical society, which are explored in relation to practical as well as structural proclivities permeating contemporary art music. These philosophical considerations are then used, so as to provide a general theory of récit music , a musical form designed by the author enabling creative employment of the impasses introduced in the information age. Finally, different reciprocities of the form with contemporary and traditional compositional philosophies are discussed, serving as an overview of the various theoretical and formal facets of récit music as methodology, approached in terms of three propositions.
APPLICATIONS OF CIRCULANT MATRICES TO DETERMINANTS INVOLVING TH POWER RESIDUES

Abstract We use circulant matrices and hyperelliptic curves over finite fields to study some arithmetic properties of certain determinants involving Legendre symbols and k th power residues.