Reviewerly/exHarmony huggingface dataset:
The Design of English Independent Learning System in Network Multimedia Environment

The problem of traditional English learning is that students lack the ability of self-management and self-supervision, and the initiative to learn English. To solve this problem, the paper designed an independent learning English system in network multimedia environment. The paper used multimedia technology to analyze the basic model of English independent learning system, and then respectively designed the hardware and software of the system. Finally, the experiment proved that the design of English independent learning system in the network multimedia environment could reflect the difficulty and the focus of the content, and stimulate students’ sensation in a multi-level manner by sound, image and video, so as to activate thinking, improve the interest of learning and stimulate learning initiative.
Impact of Organisational Culture on the Requirement Engineering Activities

Requirement Engineering (RE) is a socio-technical activity and it demands intensive communication with the stake-holders to elicit and model all requirements. As both national and organisational cultures influence the behaviour of the individuals and their work practices, they might also influence RE activities deeply. We propose an extension to a formal framework for identifying and analysing cultural influences that could impact RE activities: the aim is to apply the framework not only on the national culture level, but expand it to the level of organisational culture to provide a more nuanced approach.
Vulnerability Assessment of Pakistan Government Websites

With the growing usage of internet in Pakistan, government of Pakistan is utilizing web for provision of e-services to the people. Most of these websites collect personally identifiable information of Pakistani citizens in one form or another. A minor attack can pose a huge risk to the critical data being handled by these websites. Therefore, it is important to regularly assess the vulnerabilities in government websites in order to timely address and mitigate the threats. In this research, 60 government websites are tested for the presence of vulnerabilities using open-source web vulnerability assessment tools Arachni and Owasp ZAP. Both tools are able to identify different set of high, medium and low severity vulnerabilities which are thoroughly studied and grouped using Owasp top 10 and SANS/CWE top 25. The research will help government IT departments construct detailed policies related to secure software development and maintenance.
Bridge Deck Drainage System Design Based on GIS

The traditional bridge drainage system design to consider the cost, in most cases according to experience for open rapid drainage design. With the increase of people’s traffic demand and the increase of highway network density, it is inevitable and more and more that high-grade roads cross large water areas. Based on the ultimate strength theory of rainwater pipelines, the design method of bridge deck drainage system supported by GIS is put forward, and the determination of surface catchment time, design rainfall duration and design flow are studied. The results show that this method can effectively collect runoff from chemical dangerous goods leakage accidents, thus avoiding environmental risks, ensuring the timely discharge of rainwater runoff from bridge deck and reducing the pollution of rainwater to water environment by rainwater runoff.
College Computer Basic Course Design Based On Stratified Teaching Model for Science and Engineering Discipline

Due to the students of science and engineering with different background, learning ability, learning ways and study style, the traditional teaching methods lead to poor teaching effects. In View of the characteristics that science and engineering discipline have higher requirements on students' practical operation ability, and take the comprehensive consideration of students' intelligence factors and non-intelligence factors into condition, this paper carries out college computer basic course design based stratified teaching model, the teaching objective of the model is to train engineers, the teaching content of the model is based on engineering cases, and the teaching evaluation of the model is based on the measure of students' improvement. Statistics shows that student achievements have improved enough to fit the normal distribution, and our stratified teaching model achieves good results.
Path Planning for Intelligent Vehicle Based on Improved Ant Colony Algorithm

In order to solve the path planning problem of intelligent vehicles in the unknown environment, this paper proposes an algorithm which the improved ant colony algorithm. Firstly, the vehicle sensors were used to obtain the environment information and construct the grid map. The heuristic function of the traditional ant colony algorithm was improved to give the ant colony a preliminary guidance direction, and the pheromone was updated by using the hybrid strategy. On the basis of the path planning, the third-order B-spline curve was used to smooth and optimize, and the simulation was compared with the ant colony algorithm. Secondly, the road was planned in combination with the global planning path, and the dynamic window approach was used to plan the local moving target point in real time, and the preview tracking method was used to track the target point according to the control parameters in the path planning process to realize real-time obstacle avoidance and obtain the local optimal path until reaching the target point. Finally, the fusion algorithm was verified and analyzed by MATLAB simulation platform and ROS intelligent vehicle platform. The results show that the improved ant colony algorithm smoothed the turning angle of the path, and the dynamic window approach which integrates the global path planning algorithm can realize real-time collision avoidance, ensure the tracking of local moving target points, improve the efficiency of path planning, and realize the automatic feedback control of intelligent vehicle.
The Contribution of Sonoelastography to the Diagnosis and Correlation of Strain Index Values with Pathological Subgroups in Fibroepithelial Breast Lesions

Conclusion:We think that US elastography contributes to morphological USG findings in the evaluation of fibroepithelial lesions of the breast and can guide the biopsy decision correctly.
Infrared imagery super-resolution by using a generative adversarial network

The thermal camera often has a limited spatial resolution compared to the RGB camera with typically provides megapixels of resolution. This study presents a super-resolution architecture for infrared (IR) imagery base on a generative adversarial network. The up-sampling in this proposed network’s design generates a new super-resolution image by four times. Moreover, in this paper, generative network and discriminative models for IR images are presented. The small-object features in super-resolution IR images are shown in the simulation section with high quality.
A Poisson Equation-Based Method for 3D Reconstruction of Animated Images

3D reconstruction techniques for animated images and animation techniques for faces are important research in computer graphics-related fields. Traditional 3D reconstruction techniques for animated images mainly rely on expensive 3D scanning equipment and a lot of time-consuming postprocessing manually and require the scanned animated subject to remain in a fixed pose for a considerable period. In recent years, the development of large-scale computing power of computer-related hardware, especially distributed computing, has made it possible to come up with a real-time and efficient solution. In this paper, we propose a 3D reconstruction method for multivisual animated images based on Poisson’s equation theory. The calibration theory is used to calibrate the multivisual animated images, obtain the internal and external parameters of the camera calibration module, extract the feature points from the animated images of each viewpoint by using the corner point detection operator, then match and correct the extracted feature points by using the least square median method, and complete the 3D reconstruction of the multivisual animated images. The experimental results show that the proposed method can obtain the 3D reconstruction results of multivisual animation images quickly and accurately and has certain real-time and reliability.
Face Recognition Using Artificially Intelligent Methodologies on FERET and FEI Datasets

Artificial intelligence and computational science have been developing at a phenomenal rate in the past decade. Adapting to increasing amounts of data in the world, scientists shifted a lot of their focus towards data science in the recent past. One area of AI is facial recognition from raw image data. In modern times it would be a boon to be able to accurately and quickly predict information from image data, especially when it comes to identifying individuals. This work utilizes a double pronged approach to analyze and improve the work done in the field, by testing validity of previously hailed models on large publicly available controlled face image datasets FERET and FEI. The goal of this paper is to fit implementation of the best-suited subspace reduction technique and support vector classifier along with CNN and test their ability to scale to large datasets. This work shows the difference in efficient scalability between CNN and SVM. Additionally, it achieves an improvement upon past results obtained on the FEI dataset using convolutional neural networks and the PCA + SVM technique.
Fishy Action Recognition from Cross-Event Integrated Video Dataset Based on Deep 3D and Mixed CNN models

As two crucial aspects- 1) temporal features and 2) motion characteristics are to be scrutinized apart from spatial content in analyzing video actions, therefore applying the two-dimensional CNNs alone entail fundamental problems: They do not consider the temporal ordering and eventually collapse the temporal correlations. In this paper, an automatic deep fishy action recognition system has been developed. We have implemented two spatiotemporal convolutional algorithms (pre-trained on Kinetics-400) that are capable of handling information regarding temporal, spatial, and motion in video sequences via residual learning techniques. Six categories of suspicious videos where there are only fine-grained atomic actions from five different datasets have been considered and a composite dataset has been created. To reduce over-fitting and making the dataset more robust and generalized, synthetic data have been procured through the offline augmentation processes. To the best of our knowledge this is the first attempt to recognize malicious actions across an integrated dataset comprising of five cross-event and heterogeneous datasets of actions. Therefore, we had to combat with multifarious challenges of domain shifting. We have achieved the higher level of accuracy.
Constructing software countermeasures against instruction manipulation attacks: an approach based on vulnerability evaluation using fault simulator

Abstract Fault injection attacks (FIA), which cause information leakage by injecting intentional faults into the data or operations of devices, are one of the most powerful methods compromising the security of confidential data stored on these devices. Previous studies related to FIA report that attackers can skip instructions running on many devices through many means of fault injection. Most existing anti-FIA countermeasures on software are designed to secure against instruction skip (IS). On the other hand, recent studies report that attackers can use laser fault injection to manipulate instructions running on devices as they want. Although the previous studies have shown that instruction manipulation (IM) could attack the existing countermeasures against IS, no effective countermeasures against IM have been proposed. This paper is the first work tackling this problem, aiming to construct software-based countermeasures against IM faults. Evaluating program vulnerabilities to IM faults is required to consider countermeasures against IM faults. We propose three IM simulation environments for that aim and compare them to reveal their performance difference. GDB (GNU debugger)-based simulator that we newly propose in this paper outperforms the QEMU-based simulator that we presented in AICCSA:1–8, 2020 in advance, in terms of evaluation time at most $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> 400 faster. Evaluating a target program using the proposed IM simulators reveals that the IM faults leading to attack successes are classified into four classes. We propose secure coding techniques as countermeasures against IMs of each four classes and show the effectiveness of the countermeasures using the IM simulators.
Reactive Programming Extensions for Time-Sensitive IoT Applications

The Internet of Things (IoT) can transform any object into a smart thing by means of sensors that measure physical properties, which in turn send messages wirelessly over the Internet. Many IoT applications are time-sensitive, i.e. they react to events based on their time context, ranging from industrial control to mobility automation business cases. When sensor messages fail to arrive in a timely fashion, time-sensitive applications can react to timeouts by replacing missing measurements with predictions to maintain timely responses, albeit less accurate. To produce meaningful results, these applications combine measurements based on their time context; a challenging task since sensor messages can be late or lost due to the unreliability and varying delay of wireless IoT systems. Whereas timely measurements can be combined to obtain new results, late measurements can be used to recompute past results that were partially based on predictions, thus improving their accuracy over time. The reactive programming paradigm is a good fit for IoT application development, because reacting to events from data streams very well embodies the intuition behind sensors in a wireless sensor network. However, existing reacting programming frameworks offer limited support for reacting to events based on their time context, which remains a repetitive process that is tedious and error-prone, forcing developers to perform manual checks that result in cross-cutting code fragments. To fill this gap, this paper contributes a suite of programming language concepts to combine and operate on events based on their time context, with which we extend ReactiveX, a state-of-the-art reactive programming framework. We evaluate our extensions by implementing a swarm robotics use case with and without them, and comparing the resulting programs in terms of code size. The results show that using our extensions decreases the resulting code size by 77.7%, thus simplifying the development of time-sensitive IoT applications using reactive programming.
Design and Practice of Cloud Platform for Online Music Courses Based on the Mobile Internet Era

Abstract Music is a subject of humanities and arts. It uses practice, observation, and exploration as the main learning methods to cultivate students’ music literacy through independent or cooperative learning. In music teaching, the application of the mobile Internet-based personalized learning teaching model can not only optimize classroom teaching, but also increase students’ interest in learning music. The theoretical value and practical significance in classroom teaching in middle schools. This paper uses the cloud algorithm of the learning platform and the in-depth mining algorithm to initially construct a personalized learning model for music courses based on the mobile Internet, sort out the basic process of personalized teaching based on the mobile Internet, and establish a music curriculum based on the mobile Internet. Evaluation methods, including the evaluation of students, the evaluation of problems and the evaluation of teachers, can get the views of all walks of life on the online music course learning platform. The experimental results show that this article conducted interviews and questionnaire surveys on experimental class students, and found that more than 90% of the students recognized and liked personalized learning and teaching methods based on mobile Internet.
User Background Information-Aware Music Recommendation with Reinforcement Learning-Based Knowledge Graph Exploration

We propose a new reinforcement learning-based music recommendation method that takes into account users’ background information. Although conventional methods using a knowledge graph based on collaborative filtering can provide recommendations with high accuracy, they suffer from the cold-start problem. The proposed method solves this problem by introducing users’ background information such as countries and age groups into the knowledge graph and can achieve higher recommendation performance. The advantage of the proposed method is that our method can take users’ cultural backgrounds into account in the task of music recommendation. We confirmed the effectiveness of the proposed method by comparing it with conventional methods including a state-of-the-art method.
Geometric Segmentation of 3D Scanned Surfaces for Multi-Sensor Coordinate Metrology

Abstract In modern industry, multi-sensor metrology methods are increasingly applied for fast and accurate 3D data acquisition. These method typically start with fast initial digitization by an optical digitizer, the obtained 3D data is analyzed to extract information to provide guidance for precise re-digitization and multi-sensor data fusion. The raw output measurement data from optical digitizer is dense unsorted points with defects. Therefore a new method of analysis has to be developed to process the data and prepare it for metrological verification. This article presents a novel algorithm to manage measured data from optical systems. A robust edge-points recognition method is proposed to segment edge-points from a 3D point cloud. The remaining point cloud is then divided into different patches by applying the Euclidean distance clustering. A simple RANSAC-based method is used to identify the feature of each segmented data patch and derive the parameters. Subsequently, a special region growing algorithm is designed to refine segment the under-segmentation regions. The proposed method is experimentally validated on various industrial components. Comparisons with state-of-the-art methods indicate that the proposed method for feature surface extraction is feasible and capable of achieving favorable performance and facilitating automation of industrial components.
Innovation of Mobile-based Pedicab Application to Support National Tourism

Transportation online is now increasingly developing in people’s lives and making it easier for people to fulfill and help carry out their daily work, apart from that online transportation is very easy to access and the price is also relatively cheaper than other transportation that we usually encounter. Pedicab is one of the traditional vehicles in Indonesia. Lately, the existence of pedicabs is decreasing because they are unable to compete with engine-powered public transportation such as microbuses, conventional motorcycle taxis, online motorcycle taxis, and taxis. As a result, many pedicab drivers have started to lose their jobs, and not a few have swerved to become porters. The focus of the research is how to provide jobs for people in Indonesia. This study uses the incremental method with the stages of requirement, specification, architecture design, code, test. The trial was carried out through media testing and field tests with good results and included in the valid category with a value of 85%. The development of mobile apps is expected to be a solution for the community, especially pedicab drivers who have lost their jobs due to lack of income and the nature of this application is to create jobs.
Application of Computer Technology in Track and Field Physical Education

The traditional physical education depends on action demonstration, equipment and field of the professor, which has great limitations. Integrating CT(computer technology, hereinafter referred to as CT) into traditional physical education can not limited to many conditions of traditional mode and help to realize the progress of physical education. The computer industry continues to develop, a variety of CT has entered the classroom, physical education and other subjects, teaching content, methods and methods are constantly changing and developing, especially in the course of physical education, multimedia technology has been used to improve the level of physical education theory teaching. Multimedia courseware provides visual, auditory and other information channels. According to the requirements of teachers, teachers can freely obtain, process and display all kinds of images and images, so as to change the original boring and rigid curriculum into vivid, interesting and vivid. It vividly describes various, complex and abstract sports images and forms with pictures and words. Colorful animation can vividly simulate all kinds of sports technology, and fully demonstrate sports technology. Through the simple analysis of the time and space characteristics of sports presenters, It can strengthen students' understanding of sports knowledge. At the same time, the human-computer interaction provided by multimedia is of special significance to stimulate students' interest in learning, mobilize their enthusiasm and leadership, and develop their personal potential. In track and field sports teaching, there are many items which are difficult for teachers to judge and demonstrate accurately. It is necessary to use mathematical algorithm combined with CT for better teaching demonstration and teaching verification.
An Offline Educational Resources Access System for the Galapagos Islands

The lack of Internet access increases the digital divide in the educational sector of developing countries, specifically in marginal and remote sectors, limiting universal access to Information and Communications Technology (ICT). This research aims to present a local educational resources access system for improving the Internet browsing experience, enhancing the user learning process (i.e., students, teachers, and administrative personnel) in the distance learning education system. Thus, this solution reduces the access time to digital content and broadband data consumption based on a low-cost web technology solution, affording two Internet browsing modes when there is a low or null Internet connection in the last mile. This proposal combines a multipurpose mini local server with a Learning Management System (LMS) platform of a higher education institution on the Internet, distributing educational resources (i.e., open e-books, videos, audios, images, among others) through secondary Uniform Resource Locators (URLs). Besides, the solution uses the existing small-scale Local Area Network (LAN) infrastructure available in a remote associate center in the Galapagos Islands. Finally, technical results submit the access system performance after the pilot test in the second term of an academic period (October 2019 - February 2020). The information provides evidence of the cached requests made by the devices (i.e., desktop computer, laptop, smartphone, or tablet) connected to the network.