Reviewerly/exHarmony huggingface dataset:
A Data Mining Framework for Maintenance Prediction of Faulty Products Under Warranty

Predicting the sequential patterns of maintenance activities (replacement or repair) with the needed spare parts for faulty products becomes the main challenge to maintenance engineers. This research, therefore, develops a data mining framework for predicting the integrated sequential patterns of maintenance activities and identifying the classification of the frequent components’ spare parts for faulty products under warranty. In this framework, a large data set was mined for products under warranty including product attributes, maintenance activities, and spare parts. Then, data mining techniques were performed to determine the frequent sequence pattern of maintenance activities, involving: the selection of monthly maintenance activities, generalized sequential patterns (GSP), generation of association rules, and rule-based classification with/without considering product attributes. The frequent sequential patterns were validated using testing data. Further, the GSP was applied to determine the frequent sequential patterns of spare parts. Finally, integrated sequential patterns were generated for maintenance activities and spare parts. A case study of water-cooled chiller products was deployed to illustrate the developed framework. The effectiveness of the framework was illustrated with the warranty data mining for a water-cooled chiller. In conclusion, the proposed framework allows maintenance engineers to extract hidden knowledge regarding sequential patterns of maintenance planning and provides valuable information for maintenance prediction.
Construction and approximation for a class of feedforward neural networks with sigmoidal function

As we know, feedforward neural networks (FNNs) with sigmoidal activation function are universal approximators. Theoretically, for any continuous function defined on a compact set, there exists an FNN such that the FNN can approximate the function with arbitrary accuracy, which constitutes a theoretical guarantee that FNN can be used as an efficient learning machine. This paper addresses the construction and approximation for FNNs. We construct an FNN with sigmoidal activation function and estimate its approximation error. In particular, an inverse theorem of the approximation is established, which implies the equivalence characterization theorem of the approximation and reveals the relationship between the topological structure of the FNN and its approximation ability. As keys in this study, the concepts of modulus of continuity of function, K-functional, and their relationship are utilized, and two Bernstein-type inequalities are established.
Identifying business information through deep learning: analyzing the tender documents of an Internet-based logistics bidding platform

Purpose The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in particular, existing named entity recognition (NER) solutions are mostly unsuitable as they involve domain-specific terminologies and possess different semantic features. Design/methodology/approach To tackle this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation and a conditional random field (CRF) layer, is proposed in this paper for identifying valuable entities from logistic tender documents. Instead of traditional word embedding, the proposed model uses the pretrained Bidirectional Encoder Representations from Transformers (BERT) model as input to augment the contextual feature representation. Subsequently, with the Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation. Findings The proposed model is then verified by the Chinese logistic tender named entity corpus. Moreover, the results suggest that the proposed model excels in the logistics tender corpus over other mainstream NER models. The proposed model underpins the automatic extraction of logistics tender information, enabling logistic companies to perceive the ever-changing market trends and make far-sighted logistic decisions. Originality/value (1) A practical model for logistic tender NER is proposed in the manuscript. By employing and fine-tuning BERT into the downstream task with a small amount of data, the experiment results show that the model has a better performance than other existing models. This is the first study, to the best of the authors' knowledge, to extract named entities from Chinese logistic tender documents. (2) A real logistic tender corpus for practical use is constructed and a program of the model for online-processing real logistic tender documents is developed in this work. The authors believe that the model will facilitate logistic companies in converting unstructured documents to structured data and further perceive the ever-changing market trends to make far-sighted logistic decisions.
Development Space And Prospects Of Self Balancing Car

The balance car is one of the types of balance robots that keep the machine in balance according to the constant adjustment of the sensors and controllers of the machine itself. As a basic balancing robot, the two wheeled self balancing robot not only has the characteristics of simple structure and program, small volume, but also can realize the rotation movement in a small radius, which proves that the motion of the balancing vehicle itself is flexible, and it is very suitable as one of the tools of urban traffic. As a new tool, balance car is in the stage of rapid development. On the premise of briefly comparing and analyzing the principles, characteristics and backgrounds of different balance robots, this research summarizes the development trend, advantages and disadvantages of balance cars in the current market, and provides targeted development suggestions based on objective analysis.
Multiple Machine Learning Models on Credit Card Fraud Detection

Nowadays, there is a huge increase in digital financial fraud as a result of the widespread usage of credit cards for online purchases. Therefore, a credit card fraud detection method with high accuracy, minimizing the risk of losing money when the transaction occurs, becomes imperative. The study first processed a synthetic data set, discarding useless features, using one hot encoding to convey categorical information to numerical ones and separating the training and testing datasets. Based on the new formed datasets, the study built three Machine Learning (ML) models for fraud detection, which are the Support Vector Machine (SVM) model, the logistic regression model and the decision tree model, respectively. Next, the study implemented on training these three models by fitting the model on training datasets and predicting the test data in testing datasets. Lastly, the heatmap named confusion matrix was provided to visualize the outcomes, indicating the accuracy of each model. Also, the study uses another four performance measurement scores which are Area under the Receiver Operating Characteristic Curve (ROC AUC score), accuracy classification score, balanced F1 score and precision score for evaluating models. Comparing these three models based on the confusion matrix and the four accuracy scores, the study explored that the decision tree model can achieve the best performance compared to other models in predicting fraud.
Multi-robot material delivery in industrial parks based improved on A * algorithm

To realize the intellectualization of the industrial park and liberate the employees from the repetitive work, the material distribution method based on muti robot needs to be studied. This paper explores and improves two multi robot path planning algorithms HCA and CBS based on A * algorithm through the specific analysis of the environment in the industrial park, so that the robot can avoid dynamic obstacles. Furthermore, after simulation through webots, the advantages and disadvantages of the two algorithms are compared. Finally, through the demand analysis of the industrial park, a feasible scheme for multi-robot path planning in the industrial park is obtained.
Stock Prediction of TSMC and Intel using Machine Learning

The advancement of communications, computers, healthcare, military systems, transportation, renewable energy, and numerous more uses is made possible by semiconductors, which are a crucial part of electronic equipment. Semiconductors have been experiencing a shortage due to various reasons. Thus, the increasing demand for chips has attracted countless people to invest in the semiconductor industry. Intel Corporation and Taiwan Semiconductor Manufacturing Company Limited (TSMC) are two companies that can be said to be dominating the industry currently as they have been making very advanced chips. As two giant suppliers in the semiconductor industry, they are close substitutes and competitors. This essay is going to evaluate Intel and TSMC s’ stocks for investors by using the method of machine learning to estimate and predict the future trend of the two stocks. The two models that will be used in the essay are the Linear model and the Long Short-Term Memory model (LSTM), showing the outcome of prediction and determining which stock is better for the investment.
The Analysis of Bitcoin’s Price Fluctuation

The objective of this article is to examine Bitcoin's price fluctuations. To uncover Bitcoin's dramatic movements and unpredictability, I'll depict the cryptocurrency's price data using a variety of visualization tools, including Python. Additionally, we will discuss the causes for Bitcoin's past increase and rise in 2021, such as how the insane attention generated by certain news stories will fuel Bitcoin's ascent. Bitcoin provides investors with a more profitable investment opportunity, but it is also an exceedingly dangerous one.
Prediction of Electric Load Neural Network Prediction Model for Big Data

In the background of information network and big data era, the digitalization and information network level of China's financial industry has been improved, and many advanced big data analysis technologies have been derived, among which mobile big data analysis model is the most widely used. Based on the concept of financial investment, mobile big data analysis model can not only improve the accuracy of financial data analysis, but also quickly establish the financial investment evaluation and prediction model, providing a reliable basis for financial investment and financial development strategy. In this regard, the author combines relevant literature and his own work experience to conduct a detailed study on mobile big data analysis model based on financial investment concept. Mobile big data analysis model is a kind of data analysis model established based on big data technology, which has the characteristics of strong data collection ability, high accuracy of data statistics and analysis, and high efficiency of data application. Based on the concept of financial investment, the mobile big data analysis model can not only improve the financial investment analysis function of the mobile big data analysis model, but also improve the financial risk analysis ability of the model, which provides a reliable basis for financial investment and financial risk management. Therefore, the following briefly outlines financial investment, then analyzes the importance of building mobile big data analysis model based on financial investment concept, and finally analyzes the method of building mobile big data analysis model based on financial investment concept and briefly discusses the typical application of the model.
A Method Using LSTM Networks to Impute Missing Temperatures in Temperature Datasets and to Predict Future Temperatures

With the growth of population and the development of industry, global warming has become a serious problem. It is necessary to focus on temperature change now. This paper designed and tested methods to impute the missing temperature in global temperature datasets and predict the future temperature. Imputation was divided into two steps, the first step is filling small gaps with equally spaced points for efficiency, and the second step is filling larger gaps with Long Short-Term Memory (LSTM) neural networks. This combination of imputation methods gave valid points with both accuracy and efficiency and successfully made some datasets continuous. Another LSTM network was used to predict future temperatures. The χ2 test for the goodness of prediction with real data in Australia showed that this prediction method using LSTM network is highly accurate.
Research on Path Planning Problem of Smart Car Considering Kinematic Constraints

With the development of artificial intelligence, intelligent cars have gradually appeared in people's lives and brought many conveniences to people's lives and work. On the street, we can see many busy figures of unmanned delivery cars; In some factories, smart cars shuttle back and forth between product production processes for material distribution; In some hospitals, we can also find that some smart cars replace medical staff to undertake the task of sending medicines and tests. For intelligent cars, autonomous collision-free path planning and trajectory tracking of the planned path are the most basic and core tasks of intelligent vehicles. The existing path planning algorithms are mainly divided into search-based algorithms (Dijkstra, A*, etc.) and sample-based algorithms (PRM, RRT, etc.), and different types of algorithms have good performance in their respective applicable scenarios. However, these path planning algorithms all use the same simplification assumption, that is, the intelligent car is regarded as a freely moving point or sphere in the path planning task, and the driving experience in life tells us that the movement of the wheel-driven intelligent car is constrained by the turning radius of the vehicle, so the path planned by the simplified intelligent car for a point is often not suitable for the driving of the intelligent car in reality. Considering this constraint, this project models the kinematics of the intelligent trolley with four-wheel steering, and adds kinematic constraints to the path planning, so as to plan the path that conforms to the kinematics model of the intelligent trolley. The path planning algorithm proposed in this topic will be tested in ROS and Gazebo simulation environments, and compared with the traditional search-based A* algorithm, the results of multiple scenarios verify the effectiveness of the algorithm. Finally, this project summarizes and looks forward to this research, which has completed the construction of low-cost physical intelligent vehicles, and plans to complete the verification of the real vehicle function by combining the designed path planning algorithm with the real-time mapping and positioning system of the car in the future.
Transformation of higher education based on distance learning technologies

The article examines the advantages and disadvantages of distance learning and analyzes the opinions of the heads of higher educational institutions on this issue. The author shows that the problems of distance education are actively discussed in the scientific literature, on forums, and in the press, since digital education is replacing analog education, and the entire educational paradigm is changing, taking into account Russia's withdrawal from the Bologna process. The distance learning format began to be actively implemented in Russian higher educational institutions in early 2020 due to COVID restrictions. The St. Petersburg State University was the first to change the format of work and switch to distance learning. Other higher educational institutions in the country followed. The article considers the processes of transformation of the educational process in the higher educational institutions of the country.
MOBILE-BASED SOCIAL DISTANCING AWARENESS SYSTEM INTEGRATED WITH GPS SENSOR: A CASE STUDY OF UMP STUDENTS

Social distancing is becoming a norm in everyone’s daily life due to the COVID-19 pandemic. As the pandemic has only started two years ago, there are not many social distancing awareness systems specifically made for higher education institutions (HEI) students. As many students are required to return to the campus to partake in physical lessons and assessment, social distancing practice is very important to reduce the risk of spreading of the viruses. Social Distancing Awareness System is a mobile application that helps students to increase their social distancing awareness. The objective of this research is to alert users to practise social distancing by developing a proposed system called mobile application for social distancing awareness. The proposed system applies the agile model of Software Development Life Cycle (SDLC) as the methodology for this work. The proposed system will detect nearby devices using GPS and save the location of detection coordinates. The system will generate reports for social distancing scores to show students’ records of their social distancing practice. The risk of the detected device is categorised based on the distance between user coordinates. The closer the device to the students, the higher the risk and shall be able to get detection details sent by GPS sensors. The system will send notifications to the students’ devices to warn them about their surroundings. Conclusively, the findings from this work are described as follows; the system is able to generate a social distancing report score based on the detection log to provide statistics to UMP students on their social distancing practices. Thus, the social distancing awareness has been increased. The proposed system was found to be a useful application for the social distancing detection in order to ensure that one of the SOPs during the COVID-19 pandemics is performed by students.
COVID-19 WEARABLE DEVICE USING RSSI BLE MODULE MONITORING SYSTEM

Self-quarantine is essential for persons returning from overseas or red zone locations. It is critical that the self-quarantine be completed without incident, since this might result in the self-quarantine individual becoming a carrier of COVID-19 in the community. The majority of nations worldwide have developed a variety of COVID-19 monitoring and tracing systems in order to navigate and address this issue. However, some constraints and limitations may result in deliberate non-adherence. Individuals who have been quarantined may continue to roam within the community by detaching their wristbands or by submitting a false contact status in the tracing application. This article proposes a novel configuration for an obligatory self-quarantine system. It will facilitate communication between wearable and contact tracing technologies, ensuring that authorities maintain complete control over the system. The suggested system's hardware in the wearable device is low-cost, lightweight, and safe for the next user after the quarantine period is complete. The software (both software and database) that connects the quarantine user to the normal user makes use of cutting-edge artificial intelligence (AI) reporting and flagging techniques.
LARGE SCALE SEISMIC ISOLATION FOR A POST-EARTHQUAKE RECONSTRUCTION PRESERVING IDENTITY OF SITES

The effects of seismic attacks in oldest parts of hit towns mostly depend on the quality of materials and construction technique even for low and medium intensity earthquakes. The negative consequences of traditional construction approaches appeared in all their evidence in many areas of Central Italy affected by recent seismic sequences of medium intensity (M5-M6) in 2016-17 where entire villages were destroyed. The reconstruction should solve the problem to rebuild with safety but preserving the historical aspect of buildings and landscape. This paper presents a particular application of the known technique of seismic isolation for the reconstruction with integral seismic protection of the worldwide known village of Castelluccio di Norcia in Umbria (Central Italy). The adoption of a seismic isolation system at city scale involves the construction of a large floating platform, having the dimensions of the entire compartment, supported by seismic isolators. In the considered case the platform is stepped due to the site orography, and, above it buildings are built with the aesthetic and constructive characteristics of the collapsed original ones. The solution allows a correct interpretation of the objective to rebuild "as it was, where it was", safeguarding the landscape, prolonging the lifetime, saving the expected cost.
Italian davvero (‘really’) as a trigger of implicit contents in persuasive discourse

It is generally maintained that, as a modal adverb, Italian davvero ‘really’ signals a high degree of certainty attributed by the speaker to the modified linguistic material, or expresses a positive evaluation. Differently from other modal adverbs, in its absolute use, davvero can react to an interlocutor’s statement, reinforcing it or questioning it, thus expressing a judgement on an already existing assertion. In political monologues, we observe that the existence of an “interlocutor’s voice” responsible for an asserted content can be presupposed, with davvero being the trigger of such presupposition. In such cases, a second implicit content (a conversational implicature) is also generally conveyed which expresses the epistemic judgement of the speaker. We observe that this happens typically in some specific discourse configurations (i.e., questions and conditional subordinates) and we label this pragmatic enrichment ‘polemic polyphonic judgement’. We also show the manipulative potential of this pragmatic phenomenon. We finally suggest that davvero can trigger a conversational implicature also in other linguistic configurations, and we propose that context, and more specifically the stretch of text in which the adverb occurs, whether argumentative or non-argumentative, plays a crucial role in this pragmatic process.