Reviewerly/exHarmony huggingface dataset:
Three-Dimensional Landscape Rendering and Landscape Spatial Distribution of Traditional Villages Based on Big Data Information System

Traditional villages refer to the villages that formed earlier, are rich in traditional resources, and have various values and must be protected. This paper aims to study how to draw three-dimensional landscapes of traditional villages based on the big data information system. This paper puts forward the problem of 3D landscape rendering, which is based on big data and modeling, and then elaborates around the concept of data mining and related algorithms and designs and systematically tests 3D landscape rendering. The experimental results show that when the number of rendered patches is less than 120193 between 120193 and 242029, the system can run smoothly. It can achieve acceptable and relatively smooth operation; when it exceeds 242029, the system is obviously stuck. The design scheme of the three-dimensional landscape geographic information system of the traditional villages is reasonable and feasible, the operation is stable, and the expected research goals are achieved.
To join or not to join?–A framework for the evaluation of enterprise blockchain consortia

Within the past years, enterprise blockchain solutions were frequently developed within different industry consortia. In most cases, this resulted in isolated solutions competing against each other due to similar approaches and goals. Today, decision makers do not necessarily need to establish entirely new blockchain consortia, as established ones already exist, and participation is a considerable way to avoid unreasonable efforts. In this paper, we apply an iterative literature review to identify different factors relevant for practitioners, who face the challenge of joining an existing enterprise blockchain consortium. In a second step, we discuss these factors utilizing supply chain management as a role model. As a main finding, we propose an evaluation framework for the purpose of enterprise blockchain consortium analysis. Additionally, we provide several questions relevant for practitioners during their evaluation stages. With our evaluation framework we contribute to blockchain research, where - despite its high relevance - the topic of consortium evaluation has so far been neglected. We also contribute to research in the field of technology evaluation by proposing and merging five different evaluation dimensions.
Text CAPTCHA Traversal via Knowledge Distillation of Convolutional Neural Networks: Exploring the Impact of Color Channels Selection

While most of the existing works have investigated the recognition of a fixed-length CAPTCHA, the authors of the article propose to apply knowledge distillation to approximate the predictions of recurrent convolutional neural networks. Such models have proven themselves well in predicting the dynamic length of characters in images. The paper studies the influence of the size and complexity of the training sample on the quality of recognition. The authors studied the effect of individual color channels and their linear combination on the final quality of models. An estimate of the importance of each color channel was acquired using the trainable scalar coefficients in a linear combination. The results obtained made it possible to reduce the size of the input data for recognition without loss in quality of recognition, as well as speed up the training of the model. The analysis of model errors allowed us to offer suggestions for improving ways of countering automatic recognition.
Extracting Software Requirements from Unstructured Documents

Requirements identification in textual documents or extraction is a tedious and error prone task that many researchers suggest automating. We manually annotated the PURE dataset and thus created a new one containing both requirements and non-requirements. Using this dataset, we fine-tuned the BERT model and compare the results with several baselines such as fastText and ELMo. In order to evaluate the model on semantically more complex documents we compare the PURE dataset results with experiments on Request For Information (RFI) documents. The RFIs often include software requirements, but in a less standardized way. The fine-tuned BERT showed promising results on PURE dataset on the binary sentence classification task. Comparing with previous and recent studies dealing with constrained inputs, our approach demonstrates high performance in terms of precision and recall metrics, while being agnostic to the unstructured textual input.
Teachers' Levels of Use and Stages of Concern Regarding Metaverse-based Classes in Home Economics Education

The purpose of this study was to identify a support method for the introduction of metaverse-based classes (MBC) in home economics (HE) education. This was achieved by diagnosing the stages of concern and levels of use exhibited by HE teachers applying the concerns-based adoption model (CBAM). Questionnaires were sent to a convenience sample of middle- and high-school HE teachers using the KSDC (Korea Social Science Data Center). Overall, 271 responses were received, and the data were analyzed using KSDC E-STAT 3.0 and SPSS 28.0.1.1. The results were as follows: First, regarding the level of knowledge of MBC, the introductory level was the most common (139 respondents, 51.3%,), followed by the beginner level (81, 29.9%), the intermediate level (28, 10.3%,), the advanced level (12, 4.4%), and the master level (11, 4.1%). Average responses on a 5-point Likert scale to questions about the use of metaverse in HE classes were as follows: possibility (4.02), necessity (3.82), and usefulness (3.90). Second, HE teachers' stages of concern in MBC were as follows (in descending order): unconcerned - stage 0, and information - stage 1 (86.9), personal - stage 2 (85.6), management - stage 3 (80.9), collaboration - stage 5 (57.5), consequence - stage 4 (57.4), and refocusing - stage 6 (55.2). Third, the use of MBC was highest for orientation - level 1 (173 respondents, 63.8%), followed by non-use - level 0 (34, 12.5%), preparation - level 2 (29, 10.7%), mechanical - level 3 (15, 5.5%), refinement - level 5 (8, 3.0%), renewal - level 7 (8, 3.0%), routine - level 4 (3, 1.1%), and integration - level 6 (1, 0.4%). Many HE teachers had heard about MBC but were in the introductory level of not knowing what it is, and at the stage of being unconcerned or wanting to know about MBC. Of the 271 respondents, only 35 used metaverse in classes. Therefore, it is necessary to provide teacher training opportunities that provide basic information on the significance and implementation of MBC for HE teachers. Also, an MBC guideline book should be developed and distributed to HE teachers. Finally, a teacher community meeting is needed to share the expertise of teachers with substantial experience in using MBC.
Developing a dual axis photoelectric tracking module using a multi quadrant photoelectric device

To determine the distance, presence, or absence of the object by using a transmitter or photoelectric receiver the photoelectric device is used. The potential of a photovoltaic (PV) panel to produce power is roughly dependent on the intensity of sunlight falling on it. This study planned and constructed a dual-axis solar programmable logical controller (PLC) based automatic tracking system, as well as its management and signal conditioning. The suggested automated monitoring system regulates the elevation and orientation angles of solar panels to keep them transverse to the sun at all times. The capacity of the pouring source to right path the position of the sunlight is detracting for the proper functioning of warm excited lasers, which is an essential resource of absorbing light part of every 24 h. The current pursuing electronics, in another way, has poor strength and pursuing accuracy. To view the same issue, a photoelectric tracking system accompanying qual pivot is ingrained. The proposed system is based on the idiosyncratic multi quadrant photoelectric detector is implemented and a tracking scheme with a solar orbital method is included. The cosmic at fixed intervals monitoring device’s control design, in pair with the fine bringing into the harmony of the multi-one of four equal parts photoelectric indicator, substantially increases the effectiveness of the projected model. The pump source’s accurate positioning and tracking capabilities to the solar azimuth enable the pump source’s system reliability.
Representative Feature Alignment for Adaptive Object Detection

Unsupervised domain adaptation for object detection aims to generalize the object detector trained on the label-rich source domain to the unlabeled target domain. Recently, existing works adopt the instance-level alignment or pixel-level alignment to perform domain transfer, which can effectively avoid the negative transfer due to the diverse background between domains. However, we find that they treat all the regions of an instance feature equally without suppressing background area. They do not segment the specific texture and discriminative regions of objects, which are transferable during adaptation. We call the features that combine the local structure feature and semantic discriminant features as representative features. We propose a novel Representative Feature Alignment (RFA) model to align the features extracted from representative patterns of objects, i.e. representative features, for domain adaptation. Specifically, the representative features are extracted by the Representative Feature Extraction (RFE) submodules. The RFE submodules take the features extracted from different intermediate layers of the detector as input, and filter out the representative features layer-by-layer via integrating class weighting generator, category selection and class activation mapping. Then the representative features from multi-layers are further adaptively aggregated to obtain the final representative features, which are utilized to conduct feature alignment in a class-aware manner. Our representative features are free of untransferable regions and background areas, which leads to better feature alignment. Extensive experimental results show that the proposed model outperforms state-of-the-art methods on a few benchmark datasets.
Immediate Vehicle Movement Estimation and 3D Reconstruction for Mono Cameras by Utilizing Epipolar Geometry and Direction Prior

Motion estimation of surrounding objects is indispensable to any mobile machinery. The paper proposes a method to solve the estimation and reconstruction problem of dynamic objects with a mono camera. Using the relative camera motion and detected rigidly moving objects on the image, we estimate their movement up to a scale factor. Utilization priors about their moving direction are used to estimate the transformation, which maps the 3D object from the previous frame to the actual one. Our two-frame method works twice the speed or more as other methods using three frames or more for the estimation, and we do this without any constraints. We evaluate our method on various traffic scenarios of different autonomous driving datasets.
Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases

Extracting stable periodic-frequent patterns in very large temporal databases is a key task in big data analytics. Existing studies have mainly concentrated on discovering these patterns only in row temporal databases, and completely ignored the existence of these patterns in columnar databases, which are widely becoming popular for storing big data. In this paper we propose an efficient algorithm, Stable Periodic-frequent Pattern-Equivalence CLass Transformation (SPP-ECLAT), to find the desired patterns in a columnar temporal database. Empirical results demonstrate that the SPP-ECLAT algorithm is much faster and consumes significantly less memory than the state-of-the-art SPP-growth algorithm on sparse and dense databases.
Extractive Text Summarization on Large-scale Dataset Using K-Means Clustering

Extractive text summarization is one of the most important tasks in natural language processing. In this work, we use K-Means clustering to create the clusters on the Vietnamese large-scale dataset, then use these clusters to extract the most relevant sentences on the single-document to produce the summary. At first, we collected the articles in the Vietnamese online newspapers, cleaned up and packaged them into the dataset, after that we applied our summarization model for the experimentation. The best F-Score of this model based on ROUGE-2 and ROUGE-L are 15.48% and 28.68%.
Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

Abstract This paper aims to eradicate the poor performance of the convolutional neural network (CNN) for intelligent analysis and detection in samples. Moreover, to avoid overfitting of the CNN model during the training process, an algorithm is proposed for the fusion of maximum pooled and weight decay. Firstly, the maximum pooled method for the pooling layer is explored after mask processing to reduce the number of irrelevant neurons. Secondly, when updating the neuron weight parameters, the weight decay is introduced to further cut down complexity in model training. The experimental comparison shows that the overfitting avoidance algorithm can reduce the detection error rate by more than 10% in image detection than other methods, and it has better generalisation.
The return of intelligent textbooks

Abstract The advancement of computational Artificial Intelligence (AI) in the recent decade has been transformative for many domains, including AI in Education. One direction, where it has caused a noticeable increase in research activity, is application of AI technologies to enhance digital textbooks by making them more interactive, engaging, adaptive, and intelligent. For many researchers coming into this field, it would have seemed as if an intelligent textbook is a completely new idea. We would like to provide a historic outlook on this field and outline the important phases that it went through over the last three decades. We hope that such an account can inform interested readers and help them better understand the problems and the approaches of intelligent textbooks.
Spatial–temporal modeling for prediction of stylized human motion

• Auto-regressive network structure for stylized motion prediction. • Style feature extracting for the subtle style differences. • Capturing temporal dynamics by hierarchical multi-scale RNN. Human motion prediction refers to forecasting human motion in the future given a past motion sequence, which has significant applications in human tracking, automatic motion generation, autonomous driving, human-robotics interaction, etc. Previous works usually used RNN-based methods, focusing on modeling the temporal dynamics of human motion, which have made great effort on content motions. However, it is unclear for their performance on stylized motion, which is with more expressive emotions and states of the human motion. Different styles within the same motion type have similar motion patterns but also subtle variances. This makes it difficult to be predicted. The main idea of this paper is to learn the spatial characteristic of stylized motion and combine it with the temporal dynamics to achieve accurate prediction. We adopt a transformer-based style encoder to learn the motion representation in the pose space and then maps it to the latent space modeled by the constant variance Gaussian mixture model; meanwhile, we use the hierarchical multi-scale RNN as a temporal encoder to capture the temporal dynamics of human motion; finally, we feed the spatial and temporal features into the prediction decoder to predict the next frame. Our experiments on the Human 3.6 M and Stylized MotionDatasets demonstrate that our model has comparable prediction performance with the state-of-the-art motion prediction works on Human 3.6 M and outperforms previous works on stylized human motion prediction.
Improving the robustness of industrial Cyber–Physical Systems through machine learning-based performance anomaly identification

We propose a versatile and fully data-centric methodology towards anomaly detection and identification in modern industrial Cyber–Physical Systems (CPS). Our motivation behind this move is the ever-growing computerisation in these systems, in the form of complex distributed computing nodes, running complex distributed software. Industrial CPS also demonstrate heavy deployment of hardware sensors, as well as an increasing role for software. We observe the insufficiency and costliness of design-time measures in prevention of anomalies. As our main contribution, our methodology is taking advantage of this data-rich environment by means of Extra-Functional Behaviour (EFB) monitoring, analytics pipelines and Artificial Intelligence (AI). Specifically, we demonstrate the use of compartmentalisation of execution timelines into distinct units, i.e., execution phases. We introduce the generation of representations for these phases, i.e., behavioural signatures and behavioural passports, as our way of behavioural fingerprinting. Composed using regression modelling techniques, signatures as the representation of ongoing behaviour, are compared to passports, representing reference behaviour. The comparison is done by means of goodness-of-fit scores, creating quantifiable measures of deviation between different recorded behaviours. We have used both partially synthetic and real-world traces in our experiments, depending on the use-case. We have also followed both white box and black box approaches for our use-cases, with discussions on the pros and cons of each. The effectiveness of our data-centric methodology is demonstrated by two proofs-of-concept from the industry, to represent the two ends of the industrial CPS complexity spectrum, with one being a large semiconductor photolithography machine, while the other is an image analysis platform. Each use-case comes with its own characteristics and limitations, confirming the flexibility of our methodology and the relevance of its integral steps in the approach towards the initial analysis and data transformations. The results of anomaly classification show overall high accuracies, as high as 99% in certain set-ups. These results show the capability of our data-centric methodology, suiting the presented modern industrial CPS designs.
Breast Cancer Prediction Using Greedy Optimization and Enlarge C4.5

Detecting Breast Cancer in the early step is significant to minimize the death rate by enhancing the available treatments. As there exists no definitive treatment for Breast Cancer detection, early detection is significant. Hence, this study employs Machine Learning (ML) methods for expecting Breast Cancer effectively with efficiently. In this study, novelty is given in the feature selection and classification process. This study intends to perform relevant feature selection through the proposed Greedy Optimization (GO) method. In addition, it aims to perform a classification of the relevant features by the use of the newly introduced Enlarge C4.5 algorithm (Ext.-C4.5). At first, Breast Cancer dataset is loaded. Then, pre-processing is performed. The pre-processed data is obtained through data reduction and dimensionality reduction. Here, data reduction is performed using the Block Level Deduplication, dimensionality reduction is performed using C-Isomap. This pre-processed data is considered for feature selection. This feature selection is given for train and test split. This is followed by classification. Lastly, the prediction is performed by the trained model. The experimental implementation and performance analysis of the planned system is undertaken. It is performed by comparing the proposed system with the existing systems (Naïve Bayes (NB), Decision Tree (DT), Nearest Neighbour (NN), Multilayer Perceptron (MLP), Artificial Neural Network (ANN), Extreme Learning Machine, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM)) in terms of correctness, accuracy, specificity, sensitivity, f1-score and Completing Time. The results revealed that the proposed GO and E-C4.5 performs efficiently than the existing methodology.
Country-wide solar power load profile for Germany 2015 to 2019: The impact of system curtailments on prediction models

A large (43824 h) country-wide solar power load profile (LP), solar irradiance and meteorological dataset (ten variables) for Germany covering years 2015 to 2019 is compiled and forecast with eight machine learning and deep learning (ML/DL) algorithms. Analysis reveals that system curtailments are likely responsible for some outlying predictions. The adaptive boosting (ADA) and random forest (RF) algorithms outperform convolutional neural networks and other algorithms in supervised prediction and forecasting tasks with the dataset. Once tuned with 2015 to 2018 data, ADA and RF forecast 2019 hourly data with root mean squared error (RMSE) of < 0.03. Similar forecasting accuracy is achieved using smaller datasets of three months of historical data from 2015 to 2018 to forecast hourly LP in each month of 2019. Forecasts for July 2019 are associated with the highest errors (RMSE = 0.41). The transparent open box (TOB) algorithm, because it can reveal details of data matching contributions to each of its forecasts, is used to data mine the July 2019 forecasts and conduct in-depth outlier analysis. It reveals nine extreme outliers that each overestimate LP based on irradiance and meteorological inputs. Analysis suggests that substantial overestimates leading to high RMSE for July 2019 are likely due to system curtailments. It is beneficial to combine less transparent (e.g., ADA and RF) with more transparent (e.g. TOB) ML algorithms to accurately forecast and data mine large solar power data sets. It also indicates that solar LP forecasts cannot always rely on irradiance and meteorological variables in isolation. There is a need to be mindful of system constraints and market conditions when predicting LP on a country-wide basis.