Reviewerly/exHarmony huggingface dataset:
Soil Clustering and Anomaly Detection Based on EPBM Data Using Principal Component Analysis and Local Outlier Factor

This paper presents an unsupervised anomaly detection method to infer changing ground conditions in real time during tunneling. The proposed method combines dimensionality reduction and density-based outlier detection methods. The method uses features related to the cutter, thrust, and ground conditioning systems of an earth pressure balance tunnel boring machine (EPBM) as the input. The principal component analysis (PCA) was used to extract information on the ground conditions from selected EPBM features, reduce the dimensionality, and embed the data points in a geometrical space. The local outlier factor (LOF) was used to measure the degree of the anomaly of the projected data points. This study used a data set from the State Route 99 (SR 99) tunnel construction in Seattle, Western Australia. Based on this study, it can be concluded that with appropriately selected EPBM features, PCA can dynamically cluster EPBM data according to the ground conditions. Interestingly, the dynamic behavior of the projected data points does not substantially affect the soil clusters as they were always grouped. This study also demonstrates that LOF can be a sensible measure to detect changing ground conditions.
Interpretable artificial intelligence (AI) – saliency maps

Artificial intelligence has increasingly been usedin the biomedical field.Could you please provide an example of such usage and also discuss what can be improved in future applications?In a previous article, we introduced the application of Artificial Intelligence (AI) in biomedical research. 1rtificial intelligence offers inherent advantages in complex data analysis, particularly in making predictions.Unlike linear models commonly used in data analysis, AI, such as deep learning neural networks, can capture non-linear relationships among variables.Additionally, AI is capable of handling large and high-dimensional datasets without requiring extensive data preprocessing or feature engineering.Moreover, AI models scale effectively with increasing data size and complexity.However, despite these advantages, one limitation of AI is its tendency to be perceived as a "black box" with its internal operation being opaque to users.This limitation restricts its applicability in certain fields.
Aircraft Detection and Fine-Grained Recognition Based on High-Resolution Remote Sensing Images

In order to realize the detection and recognition of specific types of an aircraft in remote sensing images, this paper proposes an algorithm called Fine-grained S2ANet (FS2ANet) based on the improved Single-shot Alignment Network (S2ANet) for remote sensing aircraft object detection and fine-grained recognition. Firstly, to address the imbalanced number of instances of various aircrafts in the dataset, we perform data augmentation on some remote sensing images using flip and color space transformation methods. Secondly, this paper selects ResNet101 as the backbone, combines space-to-depth (SPD) to improve the FPN structure, constructs the FPN-SPD module, and builds the aircraft fine feature focusing module (AF3M) in the detection head of the network, which reduces the loss of fine-grained information in the process of feature extraction, enhances the extraction capability of the network for fine aircraft features, and improves the detection accuracy of remote sensing micro aircraft objects. Finally, we use the SkewIoU based on Kalman filtering (KFIoU) as the algorithm’s regression loss function, improving the algorithm’s convergence speed and the object boxes’ regression accuracy. The experimental results of the detection and fine-grained recognition of 11 types of remote sensing aircraft objects such as Boeing 737, A321, and C919 using the FS2ANet algorithm show that the mAP0.5 of FS2ANet is 46.82%, which is 3.87% higher than S2ANet, and it can apply to the field of remote sensing aircraft object detection and fine-grained recognition.
Explore Room(xR): Explore Digital Hotel Bedrooms Using Augmented Reality

xR(Explore Room) is an application feature to explore virtual hotel rooms using Augmented Reality. By utilizing Augmented Reality technology, users will be able to ensure the hotel services which will be obtained before booking hotel room. The application feature is developed using the Agile Development method, which is software development method that requires quick adjustments and is able to receive changes repeatedly and oriented towards the quality of the results developed. xR(Explore Room) operates AR technology as the main technology where the feature will display a 3-dimensional model of a hotel room using markers on the phone screens. From use of the Agile method, it is found that the application feature which made requires several improvement for the results to be maximized. The results of application testing suggest adding human resources to produce more 3D room model assets to adjust number of rooms that have been registered in the application.
Web Center Design of Student Activity Unit (UKM) Universitas Muhammadiyah Sidoarjo as a Center for Membership Information Event and Product Marketing

Information systems and technology are advancing so rapidly that technology and people no longer play separate roles. One of the technologies currently being developed is a website-based information system. The website is a collection of linked pages and related files. Websites have been widely used by non-profit and non-profit organizations to provide accurate information to the public by functioning as a platform for information dissemination. The purpose of this research is to create a central web information system to display complete detailed data regarding the Sidoarjo Muhammadiyah University Student Activity Unit online. The research method used is the waterfall method using blackbox testing. From the test results the researchers succeeded in implementing a student activity unit website that displayed information in the form of info on achievements, works, events, products, articles, announcements and members from each UKM. With this system, student activity units can record every activity, reduce the risk of loss or damage to data and make it easier to record all of its members.
An Efficient and Secure Cryptographic Algorithm Using Elliptic Curves and Max-Plus Algebra-Based Wavelet Transform

With the advent of communication networks, protecting data from security threats has become increasingly important. To address this issue, we present a new text encryption scheme that uses a combination of elliptic curve cryptography and max-plus algebra-based wavelet transform to provide enhanced security and efficiency. The proposed encryption process consists of three main phases. In the first phase, the plaintext is encoded using ASCII characters, followed by the introduction of diffusion in its representation. In the second phase, points are computed on an elliptic curve, and a mapping method is applied to introduce randomness into the data. Finally, in the third phase, the output is decomposed using a max-plus algebra-based wavelet transform to generate the ciphertext. We conduct a comprehensive security analysis of our scheme that includes NIST analysis, entropy analysis, correlation analysis, key space, key sensitivity, plaintext sensitivity, encryption quality, ciphertext-only attack, known-plaintext attack, chosen-plaintext attack, and chosen-ciphertext attack. The findings indicate that the proposed scheme exhibits excellent encryption quality, surpassing a value of 76, which is closer to the ideal value. Moreover, the sensitivity of the plaintext is greater than 91%, indicating its high sensitivity. The correlation between the plaintext and ciphertext is very close to the ideal value of zero. The encrypted texts exhibit a high level of randomness and meet the necessary criteria for a strong key space. These characteristics contribute to its superior security, providing protection against various cryptographic attacks. Additionally, the encryption process for a 5995-character plaintext only takes 0.047 s, while decryption requires 0.038 s. Our results indicate that the proposed scheme offers high levels of security while maintaining reasonable computational efficiency. Thus, it is suitable for secure text communication in various applications. Moreover, when compared with other state-of-the-art text encryption methods, our proposed scheme exhibits better resistance to modern cryptanalysis.
Formation of professional self-attitude among students – sports teachers

The article deals with the problem of formation of professional self-attitude among student-athletes in the process of professional training. The essence of the concept of self-relationship is presented, its structural components of the professional self-relationship of a sports teacher are revealed. Functions and principles of formation of professional self-attitude at the stage of semantic transition from the status of an athlete to a coach are determined. Selected educational technologies that contribute to the development of professional self-attitude of a sports teacher. The specificity of the activity of future sports teachers is noted, which implies awareness of oneself as a visitor to a certain culture. Two subsystems of self-attitude are presented in comparison according to its aspects. It is proved that professionally oriented thinking is associated with self-attitude and allows you to show individuality to achieve positive sports results.
Design of a Web-based Livestock Feed Sales Information System (Case Study of Lukman Jaya Store)

Lukman Jaya Store is business engaged in the sale of chicken feed and duck feed for both layer and broilers, and chicken / duck breeds. This shop in located in singkalan village RT14 RW06, Balongbendo district, Sidoarjo regency. The purpose of this research is to design a website-based livestock feed sales information system. The system used at lukman jaya stores is still manual, where the media for promotion is only throught banners and stories from one customer to another. The analysis of the system used is media observation, interviews and literature study. The results of this study are to make it easier for customers to buy and know the chicken and duck feed available at the Lukman Jaya Store.
A Novel Memory Concurrent Editing Model for Large-Scale Video Streams in Edge Computing

Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficiency of concurrent editing and the satisfaction of service users under the constraint of limited memory buffer resources, the allocation of memory buffers of concurrent editing servers is transformed into the bin-packing problem, which is solved using an ant colony algorithm to achieve the least loaded utilization batch. Meanwhile, a new distributed online concurrent editing algorithm for video streams is designed for the conflict problem of large-scale video editing in an edge computing environment. It incorporates dual-buffer read-and-write technology to solve the difficult problem of concurrent inefficiency of editing and writing disks. The experimental results of the simulation show that the scheme not only achieves a good performance in the scheduling of concurrent editing but also implements the editing resource allocation function in an efficient and reasonable way. Compared with the benchmark traditional single-exclusive editing scheme, the proposed optimized scheme can simultaneously enhance editing efficiency and user satisfaction under the restriction of providing the same memory buffer computing resources. The proposed model has a wide application to video real-time processing application scenarios in edge computing.
The power to narrate

Abstract This chapter highlights the relevant role of narratives for the development of our cognitive and social skills. Then, it discusses how computer models can contribute to the understanding of narrative generation. Next, we explain why we wrote a book about automatic narrative generation. We discuss that it is hard to find material designed for readers with no background in computer science but who, nevertheless, are interested in understanding the core processes underlying these systems. We refer to this phenomenon as the AI knowledge gap. We also discuss the dissemination of misinformation related to automatic storytelling. Finally, we describe the core features of this book and how it is organized.
A methodology for designing a narrative generator based on problem-solving

Abstract This chapter introduces a four-step methodology for developing a narrative generator based on problem-solving: (1) Choose a main problem that a character must solve; this is known as the driving goal. (2) Develop the story’s outline where the driving goal is the core motivation. (3) Using the outline as a reference, build a goal-oriented plot graph (GOP-graph). (4) Employ the goal-oriented plot graph as a guideline to define the characteristics of the story-world and the attributes of the data structures. Our methodology starts with a very general idea, which is refined little by little, until we can specify the data structures to be used in the program. We introduce the concept of nested goals and explain the use of rules when defining goals.
Weaving texts with patterns

Abstract This chapter describes how a statistical information technique, known as Markov Chains, can be employed to generate texts. We introduce concepts like probabilistic distribution, patterns, and generalization. We provide an example that illustrates how this technique works. Employing the short story ‘The Devoted Friend’, by Oscar Wilde, we show how it is possible to generate novel sentences using statistical methods. The chapter continues with some comments about the novel A Noise Such as a Man Might Make written by a computer program using Markov Chains. This program employs data extracted from two novels: The Old Man and the Sea by Ernest Hemingway, published in 1952, and The Road by Cormac McCarthy, published in 2006. We finish with some thoughts about the use of statistical information techniques for text generation.
Narrative generation from the character’s perspective

Abstract This chapter discusses how problem-solving techniques are employed for the automatic generation of narratives. In this approach, characters’ goals (e.g. actors deciphering puzzles to discover a treasure, heroes trying to help people in problems) drive the development of the tale. These goals are broken into easier tasks. In this way, a collection of relatively simple challenges produces complex scenarios that result in interesting narratives. We introduce the main concepts behind problem-solving: goal-structure, character-structure, action-structure, preconditions, post conditions, goal’s plan, backwards reasoning. Next, we provide an example that illustrates the central mechanisms that this type of storyteller employs to generate narratives. Finally, we discuss the core features of a narrative generation system based on problem-solving.
Endless ways to use computers to study narrative generation (part 2)

Abstract Continuing with the analysis of Chapter 12, here we explore how the knowledge generated in other fields, specifically narratology and studies on human daydreaming, shape the design of CURVESHIP and DAYDREAMER, two programs for narrative generation. We also consider SLANT, a system that integrates the core features of three narrative generators. We provide useful information to understand better the motivations behind the systems, how the generation process works in each case, and the type of outputs they produce. We end with some reflections about how computers offer endless ways to approach the study of narrative generation.
Mexica

Abstract In this chapter we introduce the general characteristics of our proposal known as the engagement and reflection cognitive account of creative writing. During engagement the writer devotes full attention to creating a chain of associated ideas and turning them into text. During reflection the writer reviews the written material, invokes memories, transforms ideas, and identifies what new material to create. We explain how this proposal shapes the design of our computer model for narrative generation. Then we describe MEXICA, a realization of this model. The program produces outlines of short stories about the Mexicas, the former inhabitants of what today is México City. The program represents the emotional relations and the conflicts that are established between characters as the story develops. These representations are the key element to progress the plot. We provide a detailed example of how it works, and describe some of its main features, like its capacity to produce collaborative narratives and evaluate its own outputs.
Narrative templates

Abstract This chapter introduces the concept of the template, describes its components, points out how we use templates in our everyday lives, and explains how they can be employed to produce narratives. Next, we present the notion of digital templates. Because templates require data and computers are good at getting and processing large amounts of data, we explain how to employ digital technology to fill in templates automatically. We provide examples that illustrate how to use this technique, for example to generate automatic sport news articles. Finally, the chapter describes diverse methods for generating more flexible templates, such as contextual templates, and thus to produce more appealing texts, emphasizing the relevance of data analysis.
Representing the author’s experience in planning

Abstract In this chapter we continue our study of planning techniques. We describe how a storyteller gains knowledge from its own outputs or from examples provided by the user employing a technique known as case-based reasoning (CBR). A storyteller based on CBR needs to include methods for storing and retrieving useful episodes, routines for adapting those episodes to novel situations as well as mechanisms that make sure that the coherence of the tales is preserved. We explain how narrative elements like suspense or flashback can be implemented as part of the author’s goals. Next, we review MINSTREL a well-known storyteller that illustrates all the concepts we studied about planning. The main contribution of this program is its representation of the creative process. When MINSTREL cannot find events in memory to instantiate a theme, a set of heuristics called Transform Recall Adapt Methods (TRAMS) are employed to create novel scenes. We finish with some thoughts about the scope and limitations of planning to narrative generation.
Inspired by the brain

Abstract In the 1980s and 1890s, a typical neural network comprised hundreds of elements organized in layers. Nowadays, deep neural networks (DNN) interconnect millions of those artificial neurons. This chapter introduces DNNs. Its objective is to discuss the basic concepts behind DNNs, such as parallel distributed system, training, learning, and transformers, as well as to illustrate how these concepts are used to generate texts. We describe how to design a narrative generator employing this approach. The chapter ends with a summary of some automatic storytellers based on DNNs like GPT-3, some thoughts about the neural-symbolic systems, and a reflection on this approach to narrative generation.
Preface

Extract We first discussed writing a book on automatic narrative generation in 2001. At that time, the tentative title was ‘Creativity in Computers: Computer-Based Storytellers’. We estimated that it would be 150 pages long and would be finished by 31 May 2002. Different circumstances forced us to postpone the project. Three years after that first attempt, we published an article which analysed and compared in detail the main characteristics of three story-generating systems (Pérez y Pérez and Sharples 2004). This work, in a way, functioned as a preview or prototype, exploring how we could materialize our interest in producing a book. In February 2017, at a meeting in México City, we spoke again about the importance of resuming this project and agreed that, as soon as we finished our current commitments, we would start working on the manuscript. Finally, in September 2019, the necessary conditions were in place, and sitting at the kitchen table of a beautiful London apartment, we developed a roadmap to realize our long-standing ambition of writing a text on computer systems that generate narratives, which culminated in this book.
Dedication

Extract To Pilar, Rafael, and Yani, who are indispensable and always present. To Susana, my life partner. To Minji, Jenny, and Evelyn for their love and inspiration.
A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction

Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of methods: feature selection and feature extraction. However, the two categories of methods have their own advantages and limitation. In this paper, we propose a Hybrid Feature Dimensionality Reduction Approach (HFDRA) for SDP, which combines the two different kinds of methods, to improve the performance of SDP. HFDRA approach can be divided into two stages: feature selection and feature extraction. First, HFDRA divides the original features into several feature subsets through a clustering algorithm in the feature selection stage. Then, in the feature extraction stage, kernel principal component analysis (KPCA) is used to reduce the dimensionality of each feature subset. Finally, the reduced-dimensional data is used to build the prediction model. In the empirical study, we use 22 projects from AEEEM, SOFTLAB, MORP, and ReLink as experiment object. In this paper, we first compare our approach with seven baseline methods and three state-of-the-art methods. Then, we analyze the relationship between FR and prediction performance. Experiment results show that our approach outperforms the state-of-the-art data dimensionality reduction methods for defect prediction.