Reviewerly/exHarmony huggingface dataset:
A System for Collecting, Managing, Analyzing and Sharing Diverse, Multi-Faceted Cultural Heritage and Tourism Data

Today, social media platforms and other online sources, like forums and review sites, offer an abundance of cultural and touristic information that is voluntarily offered by travelers; this information, although helpful for other travelers, is typically fragmented and thus cannot be easily leveraged to exploitable knowledge by scientists and other tourism stakeholders. In this work, we present a novel, integrated system for collecting, managing, analyzing and sharing diverse, multi-faceted cultural heritage/tourism-related data that aims to assist scientists in the cultural heritage domain and tourism stakeholders to gather and synthesize scattered information to exploitable knowledge. The proposed system is tailored to the tourism domain needs, and allows users with minimum effort and zero IT expertise to (i) gather data from both structured and unstructured/semi-structured online sources, (ii) leverage the data to knowledge via appropriate analysis and visualization tools, and (iii) share the collected data and gathered knowledge with other stakeholders via appropriate publish-subscribe mechanisms. The proposed system is entirely open-source, designed upon big data tools and principles for the data store, the analytics production, and the knowledge sharing, and targets both performance and usability.
Spatial Non-Maximum Suppression for Object Detection using Correlation and Dynamic Thresholds

This paper presents a spatial-non-maximal suppression algorithm (SNMS) that is hardware friendly and improves efficiency. Unlike the greedy NMS, which merely focuses on the scores, the SNMS considers the correlation among the overlapped boxes and applies dynamic thresholds which are determined by the boxes density. Thus, the SNMS integrates spatial and contextual information with the scores. Three techniques are used to shorten the latency. First of all, the SNMS starts the suppression as soon as the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> candidate box is ready. Secondly, the candidate boxes on different layers are analyzed in parallel. Thirdly, the representatives of boxes clusters (BC) which consist of overlapped boxes at multiply scales, are selected on the fly based on the forecastable position relationships. Experimental results demonstrate that the SNMS can compress the number of candidate boxes 30 times at maximum and maintains the accuracy at the same time.
A Hardware-Efficient HOG-SVM Algorithm and its FPGA Implementation

Recently, pedestrian detection has been an important issue in the field of computer vision. To solve the problem of large computation and poor real-time performance in pedestrian detection scene of original histogram of oriented gradients (HOG) algorithm, this paper presents a simplified HOG feature extraction algorithm and an efficient architecture in field programmable gate array (FPGA). This simplified algorithm and Support vector machine (SVM) classifier are successfully implemented on Xilinx Zynq FPGA by using parallelism and pipeline technology. In the feature extraction step, the dimension of HOG feature is reduced by changing the strides of the sliding block, and the complexity of this algorithm and the utilization of hardware resources are reduced. The result shows that this proposed algorithm can achieve 86% true positive rate and 88% precision rate in training stage on INRIA and MIT datasets. The FPGA implementation with pipeline technical and parallel circuit architecture can achieve real-time detect and the simplified algorithm can greatly reduce the utilization of FPGA resources.
Forecasting approach for solar power based on weather parameters (Case study: East Kalimantan)

Abstract Solar Energy is the most popular among several clean energies. As a tropical country, Indonesia has big opportunity to develop solar power, particularly in East Kalimantan which spans around the equator. Solar energy generation, however, is influenced by weather parameters which give uncertain values of the amount of the captured energy. Therefore, this research is conducted to overcome the effect of weather towards solar energy. The aim of this research is to examine the model for sun power forecasting based on the data. The Artificial Neural Network (ANN) and Multiple Linear Regression have taken as the approach models to determine energy forecasting. This study used five input variables; temperature, precipitation level, humidity, wind speed, and surface pressure, while the solar radiation was taken as the output variable. Moreover, the daily solar power and weather data from East Kalimantan has been taken along the period of 27 th July 2018 – 28 th July 2021. The result of this study showed that the RMSE of ANN was slightly similar with the multiple linear regression methods which were calculated by 160.26 and 160.46 respectively. However, the ANN is preferable to use in the solar energy forecasting since the tendency of nonlinearity of the climate data.
Intelligent English automatic translation system based on artificial intelligence support vector machine

research-article Intelligent English automatic translation system based on artificial intelligence support vector machine Share on Author: Jinlian Chai Xinjiang University, China Xinjiang University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 572–576https://doi.org/10.1145/3482632.3482753Online:22 November 2021Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on the Application of Computer Network Technology in the Training of Talents in Vocational Education

Abstract With the continuous deepening of education reform, the processing and analysis of teaching information in higher vocational colleges is increasing. Traditional teaching management methods can no longer meet the needs of higher vocational colleges. Computer technology is more suitable for the needs of education reform, and the modern development of teaching management with computer technology as the core promotes the comprehensive level of teaching management in higher vocational colleges. This article analyzes the impact of computer technology on the training of talents in higher vocational education; the current situation of talent training in higher vocational education at this stage, and discusses how to innovate and reform the talent training model in higher vocational colleges under the background of computer technology.
ESBMC-Solidity: An SMT-Based Model Checker for Solidity Smart Contracts

Smart contracts written in Solidity are programs used in blockchain networks, such as Etherium, for performing transactions. However, as with any piece of software, they are prone to errors and may present vulnerabilities, which malicious attackers could then use. This paper proposes a solidity frontend for the efficient SMT-based context-bounded model checker (ESBMC), named ESBMC-Solidity, which provides a way of verifying such contracts with its framework. A benchmark suite with vulnerable smart contracts was also developed for evaluation and comparison with other verification tools. The experiments performed here showed that ESBMC-Solidity detected all vulnerabilities, was the fastest tool and provided a counterexample for each benchmark. A demonstration is available at https://youtu.be/3UH8_1QAVN0.
MADMAX: a DSL for explanatory decision making

MADMAX is a Haskell-embedded DSL for multi-attribute, multi-layered decision making. An important feature of this DSL is the ability to generate explanations of why a computed optimal solution is better than its alternatives.
A Redesigned Reconstruction Kit for Rapid Collaborative Debugging and Designing of E-Textiles

In this paper, we present an iteration on a "reconstruction kit" for e-textiles, a flexible-state construction kit that allows for rapid deconstruction and reconstruction of sewn, programmable circuits. The reconstruction kit was redesigned to be more modular and was tested in more computationally and spatially challenging debugging and design situations by four pairs of students, familiar with e-textiles, in an introductory computer science course in a U.S. high school. Analyzing think-aloud protocols of the four sessions, we examined affordances and limitations of how students debugged and designed with the reconstruction kit and in which ways collaborative interactions were supported.
Real-Time Motion Control of a Humanoid Robot Using Deep Learning

Abstract This paper discusses the research work done for controlling the humanoid robot manually using deep learning. For teaching, personal assistance, search and rescue humanoid robot are used. Controlling manually makes it to do any task without any explicitly programming. Existing technique for manually controlling the humanoid are heavily dependent on hardware and they are not cost efficient. This paper proposes a novel method for controlling the humanoid using a 2D camera. The image from the 2D camera is processed and skeleton of the human body is captured using deep learning. Then the skeleton is used to control the actuators present in the humanoid robot using image classifier and ROS. As a proof of concept the upper body of the humanoid robot is controlled in real time using this method.
On quantization dimensions of idempotent probability measures

The concept of quantization dimensions (upper and lower) of idempotent probability measures on a metric compactum is introduced. These dimensions, which are defined using a general functorial scheme, are analogous to the quantization dimensions of classical probability distributions. It is proved that the quantization dimensions of an idempotent measure do not exceed the corresponding box dimensions of its support. At the same time, for any non-negative number b not exceeding the upper box dimension of a metric compactum X, there exists an idempotent probability measure μb with support equal to X and the upper quantization dimension equal to b. For the lower quantization dimension, a similar intermediate value theorem is valid under some additional restrictions on the compactum X.
A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder

Student action recognition plays an important role in detecting students learning behaivor in online courses. In order to improve the accuracy of student action recognition in classroom, an algorithm based on Adjusted Variational Auto Encoder (AVAE) is proposed. By adjusting the traditional Variational Auto Encoder (VAE) method, the proposed algorithm can effectively extract the characteristic parameters of students from classroom video images, and then recognize the students' action. Experiments show that the proposed algorithm for student action recognition preforms better than traditional VAE algorithms with higher accuracy and convergence speed, and improves the recognition accuracy by 5.13% compared with traditional Convolutional Neural Network (CNN) method.
A Top-N recommendation algorithm based on graph convolutional network that integrates basic user information

In order to solve the problem of data sparseness and cold start of the collaborative filtering model, many methods have been proposed, but most of them ignore the user attribute similarity and the user preference. The accuracy of recommendation needs to be improved. Most of researches stay in simple linear modeling of the relationship between users and items, and does not consider the influence of auxiliary information on the recommendation algorithm. In our real life, users preferences are affected by age, gender, and personality. Environment, social circle, etc.In this work, we design a Top-N recommendation algorithm LNGCF-B (light neural graph collaborative filtering with user basic information). Firstly, different from traditional graph convolutional collaborative filtering algorithm, the simplified version is more explanatory, the training time is shortened. Secondly, this algorithm considers the attributes of the user, experiments show that LNGCF-B is better than the baseline algorithm. In our social life, there are many different types of networks, under different network models, the performance of the recommendation algorithm is also different. However, there are few researches on the performance of recommendation algorithms in different scenarios. We use LNGCF-B on two data sets belonging to different network models. The results show that the list recommended by the algorithm on the Movielens 100K data set belonging to the scale-free network has a higher degree of relevance, and the Facebook friend relationship data set belonging to the small world network has a higher recall rate.
Possibility of Using Alternative Electric Power Industry for Power Supply of Autonomous Infocommunication Complexes

Abstract This article analyzes the possibility of using non-traditional – solar and wind power engineering to ensure the autonomous functioning of infocommunication facilities operating without the constant presence of personnel. The technical characteristics of the main functional elements and the conditions for their interaction in the power supply system of the communication object are given. The main results obtained during the operation of the experimental scheme of autonomous power supply are shown.
Dynamics of Characteristic and One-Point Correlation Functions of Multi-Mode Bosonic Systems: Exactly Solvable Model

In this communication we study dynamics of the open quantum bosonic system governed by the generalized Lindblad equation with both dynamical and environment induced intermode couplings taken into account. By using the method of characteristics we deduce the analytical expression for the normally ordered characteristic function. Analytical results for one-point correlation functions describing temporal evolution of the covariance matrix are obtained.
Offline Signature Verification System Using SVM Classifier with Image Pre-processing Steps and SURF Algorithm

Abstract A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.
Analysis of Geometric Distortion for Stereo Vision System by VDI/VDE 2634 Guideline

3D structured light scanners have been widely applied in the industry for high-speed and automatic quality control. A lot of scientific research reported that the obtainable accuracy and precision of the scanner vary considerably, and imperfect camera calibration is the main error source in the scanners. Actually, the VDI/VDE 2634 guideline, proposed by Association of German Engineers and Association for Electrical Engineering, Electronics and Information Technologies, has been used for evaluating the accuracy of stereo vision systems. However, the sensitivities of the camera parameters and disparity map for the test results of VDI/VDE 2634 guideline have not been well evaluated. This paper proposes a method for sensitivity analysis based on the principle of stereo vision, Monte Carlo method and VDI/VDE 2634 Part 2 guideline. We simulate the point cloud result of the ball bar artefact with 7 different arrangements and calculate the dimension of sphere diameter, sphere form and sphere-spacing. The simulation results show that the farther ball would have greater systematic error, and the deviation of the focal length and the disparity map would considerably affect the 3D point cloud reconstruction. Moreover, measurement of the ball bar artefact at the different depths of view would efficiently derive the maximum permissible error (MPE) of 3D structured light scanners.
Research on Path Planning of Robot Arm Based on RRT-connect Algorithm

To address the shortcomings of the RRT-connect algorithm in terms of low efficiency of path planning in sampling space and high randomness of node sampling, this paper proposes a three-source fast extended random tree algorithm (GT-RRT) combined with gravitational field. The algorithm adds a third node as a new extension node between the start point and the target point, so that the algorithm can extend the random tree from the start point, the target point and the third node. At the same time, a gravitational field is superimposed on each of the three nodes to guide the generation of nodes, reducing the search range of the null space. The GT-RRT and RRT-connect algorithms are compared in 30 simulation experiments, and the results show that the improved algorithm has better path planning efficiency than the original algorithm in complex environments. Finally, this paper verifies the effectiveness of the improved algorithm in both the moveit simulation and the real environment by using the robot arm of Baxter robot as an example.
A Pose Measurement Method of Non-cooperative Target Based on Monocular Vision

A method based on monocular vision is presented in this paper to estimate the pose of the non-cooperative target spacecraft. It directly takes the image features of the docking ring and solar panel as the recognized object. The circular feature is used to restore two solutions of the relative position and orientation, the vanishing points formed by the two parallel lines of the solar panel are applied to solve the duality problem and restore the roll angle. This algorithm provides a closed-form solution using simple mathematics, therefore, it is suitable to space applications where the computation capability of the on-board processor is very limited. Numerical simulations show that the proposed algorithm can effectively estimate the pose of the non-cooperative target spacecraft.
Archive access robot for smart archive repositories

Purpose Managing archives using robots rather than people can considerably enhance efficiency, while need to modify the structure of archive shelves or installation tracks. This paper aims to develop a fully automated archive access robot without modification. Design/methodology/approach First, a mobile navigation chassis and a motion algorithm based on laser ranging and map matching are created for autonomous movement to any of the archives’ locations. Second, because the existing archives are stacked vertically, the bionic manipulator is made to mimic the movement of manual access to the archives, and it is attached to the robot arm’s end to access different layers of archives. In addition, an industrial camera is used to complete barcode identification of the archives and acquire data on their location and thickness. Finally, the archive bin is created to store archives. Findings The robot can move, identify and access multiple archival copies placed on floors 1–6 and 2–5 cm thick autonomously without modifying the archival repository or using auxiliary devices. Research limitations/implications The robot is currently able to navigate, identify and access files placed on different levels. In the future, the speed of the robot’s navigation and the movement of the robot arm could be even faster, while the level of visualization of the robot could be further improved and made more intelligent. Practical implications The archive access robot developed by the authors makes it possible for robots to manage archives instead of human, while being cheaper and easier to deploy than existing robots, and has already been tested in the archive storage room of the State Grid maintenance branch in Jiangsu, China, with better results. Social implications The all-in-one archive access robot can replace existing robotic access solutions, promote intelligent management of the archive industry and the construction of unmanned archive repositories and provide ideas for the development of robots for accessing book-like materials. Originality/value This study explores the use of robots to identify and access archives without changing archive shelves or installing auxiliary devices. In this way, the robot can be quickly applied to the storage room to improve the efficiency of archive management.
Fex: Assisted Identification of Domain Features from C Programs

Modern software typically performs more than one functionality. These functionalities or features are not always organized in a way for modules representing these features to be used individually. Many software engineering approaches like programming language constructs, or product line visualization techniques have been proposed to organize projects as modules. Unfortunately, much legacy software suffer from years or decades of improper coding practices that leave the modules in the code almost undetectable. In such scenarios, a desirable requirement is to identify modules representing different features to be extracted. In this paper, we propose a novel approach that combines information retrieval and program analysis approaches to allow domain experts to identify slices of the program that represent modules using natural language search terms. We evaluate our approach by building a proof of concept tool in C, and extract modules from open source projects.
Ensemble Multilayer Perceptron Model for Day-ahead Photovoltaic Forecasting

World-wide deployment of photovoltaic system requires accurate forecasting concerning of the uncertainty and imprecision of solar radiation. Multilayer perceptron (MLP) is commonly used in day-ahead photovoltaic forecasting, which has excellent performances in convergence speed and a disadvantage of easily causing overfitting. An ensemble model of MLP is proposed in this paper to counteract the overfitting and reduce the variance of a single MLP model. The input of the ensemble model for day-ahead photovoltaic forecasting comprises feature vectors and the 24-hour power generation of the nearest day. The connection coefficients between MLP are defined by the discounting of feature distance, which measures the dissimilarity between input feature vectors. The forecasting results of a PV system in Macau verifies the effectiveness of the proposed ensemble model of MLP for solving day-ahead photovoltaic forecasting problems.