Reviewerly/exHarmony huggingface dataset:
Towards the Automatic Generation of Fictional Ideas for Games

The invention of fictional ideas is often a central process in the creative production of artefacts such as poems, music, paintings and games. Currently, fictional ideation is being studied by the Computational Creativity community within the WHIM European project. The aim of WHIM is to develop the What-If Machine, a software system capable of inventing, evaluating and presenting fictional ideas with cultural value. In this paper we explore the potential applications of the What-If Machine in the context of games. Specifically, we propose ways in which the What-If Machine can be used as an assistant for the design of games, by providing ideas about characters, the environment, etc., as well as a creative system during gameplay, through interesting interactions with the player.
Formation of technology teachers’ methodological readiness for project activity organization by using the internship platform resources

Regarding the general education system’s intensive changes, great attention is paid to the modernization of school technological education. The emergence of the new technological design directions for schoolchildren have made it mandatory for the teachers to improve their methodological readiness. This article aims to determine the effectiveness of the formation of the technology teachers’ methodological readiness to develop the schoolchildren’s project activity using the internship platform resources. We used the following research methods: filling of the diagnostic card ‘Assessment of the teacher's readiness to participate in innovative activities’ (according to the methodology of V.A. Slastenin, L.S. Podymova); conducting the questionnaire ‘Assessment of the teacher's readiness for professional development (according to the methodology of V.I. Zvereva, N.V. Nemova); carrying out the test ‘Modern educational technologies’ (according to the methodology of O.A. Ivanova); using the method of evaluation of the teachers’ activity products. These methods made it possible to determine the level of motivational, cognitive, and activity criteria of the teachers’ readiness to organize schoolchildren' project activity. To implement the internship, we have developed: the program; the structural and content model of the teachers ' activity; the educational and didactic manual; the method for estimating the level of the universal educational actions’ formation at schoolchildren during design activity based on the concept of the universal educational actions’ development by A.G. Asmolov.
Features of the work organization at the main stages on the project in the primary classes

The main aim of the article is to analyze the main stages of work on the project, characterizes the activities of the teacher and student at these stages. The basis of the project activity is the development of students' cognitive skills, the ability to independently design their knowledge and navigate the information space, the development of critical thinking; an idea is laid that constitutes the essence of the concept of "project", its pragmatic focus on the result, which is obtained when solving a particular practically or theoretically significant problem. This result can be seen, comprehended, applied in real practice. To achieve such a result, it is necessary to teach children to think independently, find and solve problems, using knowledge from different areas for this purpose, the ability to predict the results and possible consequences of different solutions, the ability to establish cause-and-effect relationships. To accomplish the goal of the study, several primary stages of work on a project are taken into account, including problematization, goal-setting, planning, implementation, presentation, and reflection. In conclusion, it can be inferred that the application of the project method in the classroom in primary school does not supplant the classroom system but complements, arranges, and extends it.
Enhancing Land Registration through Blockchain Technology: A Comprehensive Analysis

Abstract: The ownership and verification of land are complex tasks involving multiple parties. This process is susceptible to fraud, as forged documents can mislead buyers, and disputes over land can lead to lengthy court battles, wasting time and resources. The current land registration process lacks clarity, prompting the need for technological integration, such as blockchain. Blockchain is a decentralized and transparent ledger that can address these challenges. This research proposes a land registration application system that utilizes blockchain technology and smart contracts. The system requires new buyers to register and proceed with the necessary procedures. The proposed model incorporates the SHA256 algorithm for generating unique hash values to secure messages stored in blocks, while Ethereum blockchain technology is utilized to store both smart contract and transaction details.
Class Enhancement Losses with Pseudo Labels for Open-Vocabulary Semantic Segmentation

Recent mask proposal models have significantly improved the performance of open-vocabulary semantic segmentation. However, the use of a ‘background’ embedding during training in these methods is problematic as the resulting model tends to over-learn and assign all unseen classes as the background class instead of their correct labels. Furthermore, they ignore the semantic relationship of text embeddings, which arguably can be highly informative for open-vocabulary prediction as some classes may have close relationship with other classes. To this end, this paper proposes novel class enhancement losses to bypass the use of the ‘background’ embbedding during training, and simultaneously exploit the semantic relationship between text embeddings and mask proposals by ranking the similarity scores. To further capture the relationship between base and novel classes, we propose an effective pseudo label generation pipeline using the pretrained vision-language model. Extensive experiments on several benchmark datasets show that our method achieves overall the best performance for open-vocabulary semantic segmentation. Our method is flexible, and can also be applied to the zero-shot semantic segmentation problem.
An Empirical Study of Text Features for Identifying Subjective Sentences in Portuguese

Studies in sentiment analysis have examined how different features are effective in identifying subjective sentences. Several studies on sentiment analysis exist in the literature that have already performed some evaluation of NLP classifications. However, the vast majority of them did not handle texts in the Brazilian Portuguese language, and there is no one to consider the combination of sets of text features of NLP tasks with classifiers. Therefore, in our investigation, we combined empirical features to identify subjective sentences in Portuguese and provide a comprehensive analysis of each set of features’ relative importance using a representative set of user reviews.
Diversification of Teaching Resources of Japanese Curriculum Construction in Colleges and Universities Based on Big Data Analysis

Language learning should be connected with cultural learning, and the process of learning language is also the process of learning cultural background. In view of the problems existing in the process of College Japanese teaching, through the in-depth analysis of the cultural characteristics and differences between China and Japan, this paper puts forward some strategies for College Japanese teaching. The specific strategies include: renewing teaching concepts, recognizing the importance of cultural differences, paying attention to the two subjects and realizing the interaction between teachers and students in the teaching process; experiencing extracurricular practice of cultural differences; cultural infiltration to cultivate cross-cultural communication ability; cultural methods to enhance the awareness of cultural differences. Starting from the influence of big data era on Japanese teachers in Colleges and universities, this paper analyzes the challenges and outstanding problems faced by Japanese teachers in Colleges and universities, and studies how to realize the leap forward development of career. College Japanese teachers should update their teaching concepts, improve classroom teaching modes and methods, improve their teaching ability and the ability to adapt to the "interdisciplinary" differentiation of College Japanese curriculum, establish the concept and consciousness of lifelong learning and participation, and carry out relevant training and learning. The results show that the proportion of female students choosing Japanese major is the highest, 87.5%, and the proportion of female students choosing Japanese major is 75.4% higher than that of male students.
Research on the Co-construction and Sharing Mode of Military Data Resources Based on Cloud Service

The co-construction and sharing of military data resource is important for improving the supporting role of data in military decision-making. The paper explains the part cloud service play in the co-construction and sharing of military data resources, analyzes the requirements of military data management platforms construction based on cloud service, gives an idea on planning and design of military data management platforms construction based on cloud service, and proposes the co-construction and sharing strategy of military data resource based on cloud service.
On plateaued functions satisfying propagation criterion of degree l$l$

The propagation criterion is defined in cryptography in order to analyze the security of cryptographic components with respect to differential cryptanalysis. This paper explains a class of plateaued functions satisfying the propagation criterion of degree l $l$ by using the Maiorana–McFarland construction. And a variety of other desirable criteria for functions with cryptographic application could also be satisfied, such as balancedness, high non-linearity, non-existence of non-zero linear structures, good global avalanche characteristics.
Online multi‐object tracking based on time and frequency domain features

Multi-object tracking (MOT) can be considered as an interesting field in computer vision research. Its application can be found in video motion analysis, smart interfaces, and visual surveillance. It is a challenging issue due to difficulties made by a variable number of objects and interaction between them. In this work, a new method for online MOT based on time and frequency domain features is presented. The features are obtained from the wavelet transform and fractal dimension. The modified cuckoo optimization algorithm is utilized for feature selection, which has the ability such as fast convergence and global optima finding. The features are given for learning vector quantization, which is a supervised artificial neural network (ANN). It is used to classify the dataset. To evaluate the performance of the presented technique, simulations are performed using the ETH Mobile Platform and VS-PETS 2009 datasets. The simulation results show the superiority of the presented technique for MOT compared to earlier studies in terms of accuracy. The mostly tracked values for the datasets are 74.3% and 97.2%, which leads to at least 4.2% and 2.5% better performance according to the other methods, respectively.
Research and Exploration on the Teaching Practice System in Application Oriented Universities—–Research and Exploration on the Teaching Reform of Circuit Principle Course of Jilin Agricultural Science and Technology College

Circuit principle is a course combining theory and Practice for undergraduate electronic information major, and it is an important part of information science and control science. It is the major course of postgraduate entrance examination, which plays an important role in the whole talent training program and curriculum system of electronics and electrical information major. It plays an important role as a link between the past and the future. According to the conditions of application-oriented undergraduate courses, this study starts from improving the ability of colleges and universities to serve the society and economic development, and concludes the following conclusions. In accordance with the curriculum objectives, this paper explores the teaching concept, content, teaching methods and evaluation system of the curriculum, so as to provide an effective basis for the development of local colleges and universities [1].
Research on Field Routing Planning and Optimization Algorithm Based on GIS

In this paper, A* optimization algorithm based on GIS is proposed to solve the problems of the lack of scientific methods and means in routing planning under the field conditions and the low computational efficiency. Firstly, this paper extracted the information of water source, road network and elevation through GIS related software, stored it in raster layer and mapped the environment model to save the storage space of the map. Then, the moving speed and constraint constraints were calculated by combining the opening conditions of the environmental model, which were introduced into the cost function of A* algorithm as an adaptive factor. Then, the efficiency of the algorithm was improved and the space and time costs were reduced through the step size crossing mechanism and the two-way search strategy.Finally, software is used to standardize the algorithm flow and visualize the algorithm results, which effectively proves the feasibility and practical application of the algorithm Status.
Three-dimensional shape measurement based on projector defocusing and deep learning

High-speed real-time three-dimensional(3D) measurement is of great significance for the measurement of high-speed dynamic process. However, the projector needs a long time integration to project different gray levels, and needs a complicated gamma correction process, which greatly limits its measurement speed and accuracy. The appearance of defocus fringe projection technology provides a new perspective for high-speed dynamic 3D measurement. With the research on the feasibility of the combination of deep learning and traditional digital fringe projection 3D measurement technology, the development of fringe projection technology is also breeds new breakthroughs. In this paper, we combine the defocus fringe projection technology with deep learning, and propose a 3D shape measurement based on projector defocusing and deep learning. Experiments show that by collecting sufficient and comprehensive training data and after many reasonable trainings, this method can effectively eliminate the ripple error in the traditional three-step phase shift method and the measurement accuracy is comparable to that of the twelve-step phase-shifting method.
Visual Analysis of Industrial Multivariate Time Series

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. We investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field.
Optimizing Optical Convolution with Nonlinear Absorption

Convolution is an essential operation in signal processing and has found applications in convolutional neural networks. All-optical convolution is challenged by small nonlinear coefficients. Two configurations for all-optical convolution with nonlinear absorption are analysed and simulated to identify parameters for optimizing signal-to-noise.
A new breast cancer diagnosis application based on ResNet50

Histopathology is the primary tool employed in breast cancer diagnosis. It involves examining metastatic tissues of lymph nodes under a microscope. Histopathologists are responsible for making tissue diagnoses, and the process is challenging and tedious. To diminish their workload and allocate more time to efficiently maintain patients' care, deploying an intelligent system to support the diagnosis is reasonable. Therefore, we introduced a new breast cancer diagnosis application based on deep learning technology in this paper. The application's foremost objectives were to handle the vast dataset of digital pathology scans and train deep residual networks to classify small patches from the sizable whole slide images with higher accuracy. Experimental outcomes indicated that our model could achieve 97.3%. Another noteworthy feature was a FAQ chatbot that we implemented for patient consulting.
The performance impact of the graph topology in Ibm’s Quantum devices on Grover’s search algorithm

In the context of actual information technology, data processing and storage have increased exponentially. Searching through large databases is considered an important problem, with multiple applications, where the properties of quantum computing could be used to find a potential solution. In this paper, the authors present an implementation for Grover’s search algorithm, a fast quantum algorithm for database search, and analyze the results for various graph topologies used in some of the real quantum devices provided by IBM. The algorithm provides a quadratic speedup over its classical counterpart (linear complexity). The code used in the implementation is possible with the help of the Qiskit framework and IBM Q Experience platform. Multiple tests with their results are presented. The experiments include three implementations of Grover’s search algorithm for different input register sizes: 2, 3 and 4 qubits, and exploration of multiple running configurations. We focus on the performance impact of selecting different groups of qubits on various real quantum devices, such as the probability to find the desired state and the execution time. For each experiment, we present the motivation, the results and their analysis.
Overall Design of Interactive Intelligent Translation Platform Based on Cloud Computing Technology

research-article Overall Design of Interactive Intelligent Translation Platform Based on Cloud Computing Technology Share on Authors: Lingzhi Zuo Guangzhou College of Commerce, China Guangzhou College of Commerce, ChinaView Profile , Dingchang Mao Jiangmen Preschool Education College, China Jiangmen Preschool Education College, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2757–2762https://doi.org/10.1145/3482632.3487509Online:22 November 2021Publication History 0citation4DownloadsMetricsTotal Citations0Total Downloads4Last 12 Months4Last 6 weeks0 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
Dynamic Construction Method of Aircraft Cable Assembly Process Model Based on SLAM Algorithm

Aiming at problems such as the fact that the aircraft cable assembly process file is not related to actual data and the pose uncertainty of the cable assembly path in actual assembly, this paper proposes a method for constructing a dynamic model of aircraft cable assembly process based on Simultaneous Localization And Mapping (SLAM) algorithm. SLAM of Random Finite Set (RFS) performs position and posture tracking of cables during the laying process to dynamically construct the process model of aircraft cable assembly. In the framework of RFS, this method uses Gaussian Mixture Probability Hypothesis Density (GM-PHD) to predict and update the feature information of map components according to the data collected by the sensor. Using Square Root Transformed Unscented Kalman Filter (SRTUKF) and a single feature strategy to estimate the pose of the aircraft cable assembly path. Finally, the method proposed in this paper is verified by an example.
Evaluation of ECMWF model to predict daily and monthly solar radiation over Indonesia region

Abstract Solar radiation forecast is a pivotal information needed in the operational activity of large-scale solar energy production. In this study, the reliability of SSRD (surface solar radiation downward) forecast from the 51 ensemble members in the ECMWF (European Centre for Medium Range Forecast) long-range forecast to predict daily and monthly radiation in 5 climatological stations in Indonesia is evaluated. The global horizontal irradiance (GHI) data from the solar radiation observation network from January 2018 – December 2020 are used in the quantitative evaluation of the SSRD forecast. Post-processing methods are applied to the model output, namely the bilinear interpolation method and the empirical quantile mapping to reduce consistent biases in the model output. The evaluation was carried out for different cloud covers based on the calculation of clearness index (k_t). The cloud condition affects the performance of the model, where the highest correlation value is achieved during sunny days (0.18 – 0.65) and the lowest correlation happens in overcast days (0.05 – 0.35). Models also tend to underestimate radiation when the sky is clear and overestimate it in cloudy days, based on negative MBE values during clear days (-0.47 kWh/m2 – -1.29 kWh/m2). The spatial averaging method did not necessarily improve the accuracy of the forecast, but the empirical quantile mapping method provides better accuracy, which is indicated by a values (mean error ratio) lower than 1 in most stations. Information about the influence of cloud cover on model performance can be used in future application of the model output and the bias correction process carried out in this study can be applied to reduce bias in the model.
Sentiment Classification of Chinese Text Based on Extending Semantic Similar Sentiment Words

research-article Sentiment Classification of Chinese Text Based on Extending Semantic Similar Sentiment Words Share on Author: Yanying Mao Chongqing College of Electronic Engineering, China Chongqing College of Electronic Engineering, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 1994–1997https://doi.org/10.1145/3482632.3484084Online:22 November 2021Publication History 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months12Last 6 weeks2 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