Reviewerly/exHarmony huggingface dataset:
Drifting

In this book, we deal with the epistemology of design. Epistemology is one of the grand terms of philosophy, where it means discourse about knowledge — usually about certainty, whether we can trust our senses, thoughts, and other pieces of knowledge. For us, two design researchers, the concept is more specific. We deal with epistemology in one particular context, constructive design research, in which design artefacts are vehicles of knowledge creation. In our work we have identified four epistemic traditions in constructive design research: (1) experiential; (2) methodic; (3) programmatic; and (4) dialectic. These are described in Chap. 3 . Our aim is not to contribute to philosophy; our aim is to clarify how knowledge works in constructive design research. Rather than tightening the bridle this book is our attempt to maximize the freedom of research as it happens in constructive design research. The descriptions and methodologies provided in this book is our attempt to give research legitimacy to a cherished design practice we call drifting — however, drifting by intention.
A Brief Review of Immersive Virtual Environments for Teaching Microbiology

This paper presents a compilation of the goals of teaching strategies through technology in the microbiology education field. This brief review moves through multimedia technologies to virtual reality simulations. Thus, this reports that visualization artifacts in three dimensions aid learning and animations denote visualization tools for beginners and aid in long-term memory retention. At last, high-fidelity graphics allows for the greater appropriation of the characteristics of the microbiological elements studied.
3D object detection and state estimation method based on stereo vision and LIDAR fusion

Environmental perception is the foundation of autonomous driving, and a single sensor cannot handle the complex urban road environment efficiently. In this paper, we propose a vehicle object shape, position and motion state estimation method based on stereo vision and LiDAR fusion. First, images and point clouds of LiDAR are detected using YOLOv4 and Point-GNN, respectively. Secondly, to estimate the position and shape of the object, the paper proposes an estimation method using 3D points of image object and object type. Finally, the image and LiDAR objects are converted to the same vehicle coordinate system using the calibrated external parameters, the IOU and Global Nearest Neighbor(GNN) data association method is used to match the image object and the LiDAR object, and the matched object are fused to obtain the accuracy shape-position of the object, and the object is tracked using the extended Kalman filter (EKF) algorithm. This paper is validated on the KITTI dataset and real road scenes. The experimental results show that the average errors of object shape, position and velocity in 20m are 4.33%,4.41% and 2.34%, respectively.
Game Theory Role in Wireless Network Security – Status, Challenges and Future trends

As the Internet grows omnipresent in the lives of these people, people rely largely on the Internet for appropriate communication and quick access to information. The network has security problems, though. Network security has become a challenge since numerous new network assaults have gotten increasingly complicated and caused enormous network losses. The Game Theory technique has been introduced as a viable tool for addressing such challenging cyber threats. Methods of game theory were offered as valuable tools for addressing these difficult cyber threats. Many researchers have advocated using game theory to improve network security. On the one hand, the lack of a quantitative decision-making framework is a major drawback of traditional network security systems. Game theory handles the problem of several individuals competing with opposing motivations or goals; it can give a mathematical framework for studying and modeling network-related security challenges.
Forces Between Tip and Sample

The idea behind the atomic force microscope (AFM) is to measure the force between the surface and the scanning tip in order to track the surface topography. Before we describe the atomic force microscopy technique in detail, we consider the forces acting between tip and sample as well as the tip-sample contact mechanics. We consider also the snap-to-contact phenomenon, which can occur due to attractive tip-sample forces.
Project of Automated System's Information Security System Selection

Automation of production processes of enterprise today has enormous scales. The spread of automated control systems of technological processes (ACSTP) and their integration with corporate information systems have led to increase in proportion of successfully implemented malicious impacts on ACSTP. Occurrence of new types of attacks and increase in number of successfully implemented attacks cause the need to form protection systems of industrial systems. Designing protection system of ACSTP is multi-stage process, starting with collection of data on object of protection, its main components, information assets, links with other systems, and identification of relevant information security threats to the system. In this article analysis of structure of typical automated control system of technological process is carried out. The most important components of ACSTP from the point of view of information security are identified. The article defines the need to form project of protection system of ACSTP on the basis of monitoring data of events that occur in ACSTP. Main classes of system events from the point of view of information security are defined. The most typical information security threats for this type of automated systems are considered. Neutralization of groups of threats relevant to ACSTP is carried out by means of forming effective protection system. The typical composition of data protection system of ACSTP is defined. According to results of analysis, the correspondence between security events of ACSTP, current threat groups and typical means of information protection is determined. Choosing the composition of information security tools is complex task that requires a lot of time. To achieve greater efficiency in its solving, it is proposed to use decision support system and system of artificial intelligence. Authors developed functional model of selection of protection system's project using intelligent method of decision support with neural network in it.
Performance analysis of support vector machines with polynomial kernel for sentiment polarity identification: A case study in lecturer’s performance questionnaire

Abstract The lecturers’ performance evaluation process can be carried out using an open questionnaire filled out by students at the end of the semester. In this questionnaire, students can provide an assessment in the form of comments, suggestions, and criticism of the ’lecturer’s performance, which can describe the level of student satisfaction with the lecture process. Conducting assessments or analyses on the open questionnaire entries manually will certainly impact the high costs time and energy. Sentiment polarity identification is a process in sentiment analysis that classifies text into a sentence or document and then determines whether the opinion expressed is positive, negative or neutral. In this research, a sentiment polarity detection system was developed in a lecturer evaluation questionnaire using the Support Vector Machine (SVM) method with a polynomial kernel. The test results showed that the SVM method’s performance with the Polynomial kernel was strongly influenced by the value of the learning rate parameter, the maximum iteration, and the degree, with the optimal parameter values, respectively, 0.001, 200, and 0.3. The use of optimal parameter values in the process of identifying sentiment polarity obtained an accuracy value of 84.88%.
Temporal Enhanced Multi-Stream Graph Convolutional Nerual Networks For Skeleton-Based Action Recognition

Compared with image sequence, skeleton sequence is an ideal choice for human action recognition because of no redundant information and lightweight data. Recently,a tremendous breakthrough in skeleton-based human activity recognition methods. By way of illustration, Spatial- Temporal Graph Convolutional Networks (ST-GCN) creatively distill the information of human joints into the structure of a graph and Two-Stream Adaptive Graph Convolutional Networks (2S-AGCN) propose to use the length and orientation information of bones and combine it with joint information to predict in an explicit way. However there are still some issues exist in these GCN-based models. The model lacks long-term dependency modeling capabilities and does not explore the deep correlation between joints and bones. In this work, we propose a temporal enhanced multi-stream graph convolutional nerual networks (TAMS-GCN) for skeleton-based action recognition.We combine the temporal attention module with a graph convolutional neural network to extract skeletal information of each frame and actively incorporates it into the global features by global pooling of all joints for each frame to acquire attention at the frame level of the action. In addition we propose a fusion network of joint and bone information that implicitly learns the connection between joints and bones, increasing the compactness between joints and bones. We tested our TAMS-GCN model on NTU-RGBD datasets, the model achieves excellent performance compared to the state-of-the-art.
The relationship between tolerance for uncertainty and creativity among high school students with a critical style of thinking

The paper presents the results of a study of tolerance relation to uncertainty and creativity among senior schoolchildren with a critical style of thinking. The authors use valid diagnostic methods: A. Belousovas thinking style questionnaire, S. Badners uncertainty tolerance questionnaire, and P. Torrances test. The sample consists of 90 senior schoolchildren, both boys and girls. As a result of the study, the groups of schoolchildren with a dominant critical and practical style of thinking have been identified. The authors show that there are significant differences in the development of relationships between uncertainty and creativity tolerance scales, between uncertainty tolerance and critical thinking style, creativity and critical thinking style. The differences have been obtained in the development of tolerance for uncertainty, creativity in relation to the practical style of thinking.
Mobile Assisted Language Learning Experiences

The purpose of this study was to investigate the benefits of learning with mobile technology for TESOL students and to explore their perceptions of learning with this type of technology. The study provided valuable insights on how students perceive and adapt to learning with mobile technology for effective learning experiences for both students and teachers. The authors conducted a convergent mixed methods design to achieve the research objectives. Three critical and practical recommendations for effective mobile learning experiences were scrutinized for design and implementation of Mobile Assisted Language Learning (MALL): effective instructional strategies, training or professional workshop development, and ongoing technical support and assistance.
Online learning at universities: polish-ukrainian experience

The article presents a study of the organization of cooperation between a teacher and students in online learning. This applies to online classes (lectures and seminars), collection and evaluation of final papers. The steps to use the Google Meet platform for lectures are considered. In addition, a form of training in the field of practical tasks was mentioned – for organizing a more effective system of student education. Attempts were made to ensure not only passive but also active participation, during which the scores obtained by students are included in the final grade. Research has shown that the ability to ask questions and answer in chat after the lecture is seen by students as a good opportunity to better master the lecture material. The problem of organizing online learning was illustrated by the example of teaching disciplines at the Academy of Management and Administration in Opole.
Challenges for Big Data Security and Privacy

Security and privacy issues are magnified by the volume, variety, and velocity of big data, such as large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, big data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, big data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the big data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging big data security and privacy issues.
Environmental Education and Education for Sustainable Development Through Mobile Learning

Growing planetary challenges demand environmental consciousness and energetic workforce. The environmental education and education for sustainable development are essential for the knowledge workers so as to intellectually empower them with cognitive skills, analytical abilities and problem solving capabilities leading to the efficient development and implementation of sustainable development policies and programmes. Increasing Access through Mobile Learning for EE and ESD is the need of the hour, especially for developing countries where the use of mobile technology is increasing at a phenomenal rate. Mobile learning will augment the efficacy of participatory teaching and learning methods that motivate and empower learners to change their behaviour and take action for environment and sustainable development. M-Learning enables learners to merge their learning experiences in a shared collaborative environment. This paper shows how mobile learning can transform the delivery of EE and ESD.
Educational Technology Goes Mobile

Recent decades have revealed that the digital educational technology that is expected to revolutionise schooling for generations to come, is fraught with challenges. One major challenge is that educational systems vastly vary between cultures and countries. The differences start from the conceptualisation of education and school. It is, therefore, quite inaccurate to handle education as a universal concept. In this article the authors evade generalisation by discussing the use of mobile technology in the schools of one single, relatively homogenous nation: Finland. The backbone of their analysis is the core national curriculum of basic education. The appropriateness of mobile technology in the school context is reflected upon through the objectives and ethos of basic education. The conclusions are discussed in terms of their contribution to the understanding of the use culture of mobile technology.
Interactive Attention Graph Convolution Networks for Aspect-level Sentiment Classification

Different from coarse-grained sentiment analysis, fine-grained sentiment analysis can identify the sentiment orientation of a given aspect in the context. Although neural network model with attention mechanism perform well, most of them focus only on semantic analysis of annotations, with little consideration of syntactic constraints between aspects and context. How to efficiently use syntactic dependency information to optimize the representation of contexts is an important issue. An Interactive Attention Graph Convolutional Network (IAGCN) was constructed by us. In order to use syntactic information, the network first extracts syntactic information on the syntactic dependency tree with the help of the graph convolutional network. Then, through interactive learning, various aspects and contextual representations are generated. In this design, the aspects and context of syntactic information are fully learned. The experimental results on 5 data sets verify the advantages of the model.
Speech Synthesis of Tibetan Amdo Dialect Based on Attention and Recurrent Neural Network

This paper proposes a speech synthesis method of Tibetan Amdo Dialect based on an attention mechanism and Recurrent Neural Network(RNN). This paper mainly introduces the Sequence to Sequence structure with an attention mechanism. The advantages of this structure are it can receive the input of characters, and output the corresponding original spectrum- diagram, then finally directly generate speech by using the vocoder algorithm. In order to solve the problem of information overload and improve the efficiency and accuracy of task processing, an attention module is added between the encoder and the decoder to learn the alignment information from text sequence to Mel spectrum sequence, this can make full use of the information carried by the input sequence. Using the above structure and the synthetic corpus of Tibetan Amdo Dialect, we realize and explore the speech synthesis of syllables and phonemes in Tibetan Amdo Dialect. The experimental results show that the speech quality synthesized by this method is further improved compared with the traditional speech synthesis method.
Soldier Threat Assessment Method Based on Bayesian Network

Since it is very important for soldiers to obtain threat information in battlefield environment, this paper determines the threat elements of soldiers in battlefield environment based on METT-TC elements proposed by the US Army Laboratory for battlefield needs and combined with domestic literature. In this paper, a threat assessment method of soldiers based on Bayesian network is proposed. Aiming at the problem that Bayesian network cannot deal with dynamic threats, this paper proposes a threat assessment method of soldiers based on dynamic Bayesian network. The threat assessment of soldiers is compared and verified by simulation examples. The results show that the dynamic Bayesian network model has good accuracy and real-time performance, which proves the superiority of the dynamic Bayesian network model. The simulation results are consistent with the expert experience evaluation, and this method has good practicability.
Analysis of Algorithms

Analysis of optimization algorithms can be carried out from different perspectives. This chapter first highlights the essence of optimization algorithms, followed by the brief introduction of some commonly used nature-inspired algorithms. Then, parameter tuning and parameter control are discussed in the context of algorithm analysis and performance.
Design of English Writing System Based on Machine Learning

Recently, artificial intelligence and machine learning have developed rapidly, but their application in helping learners improve their English writing is very limited. The author designs a new English writing system based on machine learning to improve the level of English writing. In this article, the author introduces the background and development of deep learning, natural language processing and machine translation. Then the author introduces how to build the framework of recurrent neural network, obtain training related data and reliable results by selecting model of sequence to sequence, so as to enhance English writing ability. Finally, the author discusses both the advantages and limitations of selecting data and building models, as well as the direction of future research and improvement.
Election Tweets Prediction Using Enhanced Cart and Random Forest

In this digital era, the framework and working process of election and other such political works are becoming increasingly complex due to various factors such as number of parties, policies, and most notably the mixed public opinion. The advent of social media has deployed the ability to converse and discuss with a wide range of audience across the globe, whereas gaining a sheer amount of attention from a tweet or post is unimaginable. Recent advances in the area of profound learning have contributed to the use of many different verticals. Techniques such as long-term memory (LSTM) perform a sentiment analysis of the posts. This can be used to determine the overall mixed reviews of the population towards a political party or person. Several experiments have shown how to forecast public sentiment loosely by examining consumer behaviour in blogging sites and online social networks in national elections. This paper has proposed a model of machine learning to predict the chances of winning the upcoming election based on the common people or supporter views on the web of social media. The supporter or user shares their opinion or suggestions about the group or opposite group of their choice in social media. It has been required to collect the text posts about election and political campaigns, and then the machine learning models are developed to predict the outcome.
Geochemical Mapping

Systematic geochemical mapping is the best available method to document the spatial distribution of chemical elements and compounds, and physicochemical properties across the Earth's surface. Phased geochemical mapping is a key component of mineral exploration for the delineation of potential economic mineralization. Since, the 1970s it has also been used for the location of contamination caused by human activities, and the mapping of the geochemical environment of urban areas. Geochemical mapping, whether for mineral exploration, environmental contamination or urban projects, should be carried out systematically by well-trained individuals. Sampling and sample preparation are the most important components in geochemical mapping and should be performed with the utmost care. To ensure the successful completion of a geochemical mapping project, an effective quality assurance/quality control procedure should be implemented during these two stages, and carried forward in the laboratory analysis of collected samples. This article presents examples of geochemical mapping surveys at the continental-scale, from mineral exploration, and for the urban setting.
Data Visualization

Data analytics is a burgeoning field—with methods emerging quickly to explore and make sense of the huge amount of information that is being created every day. However, with any data set or analysis result, the primary concern is in communicating the results to the reader. Unfortunately, human perception is not optimized to understand interrelationships between large (or even moderately sized) sets of numbers. However, human perception is excellent at understanding interrelationships between sets of data, such as series, deviations, and the like, through the use of visual representations.